The apparent changes in the Indian summer monsoon rainfall pattern and the nature of extreme rain... more The apparent changes in the Indian summer monsoon rainfall pattern and the nature of extreme rainfall events (EREs) in the southern Western Ghats (WG) caused widespread landslides across the region. Landslide susceptibility maps generated using the past landslide inventory are one of the efficient tools that can help mitigate the deleterious effects of landslides. Landslide susceptibility maps produced using the landslide inventory of normal and extreme rainfall years by deep neural network (DNN) modelling vary in terms of their degree of susceptibility, and the differences are prominent in the moderate and high susceptibility zones. The DNN model trained by landslide inventory of normal rainfall years is capable of predicting landslides during EREs (with Area Under the Curve (AUC) value >0.90). The inclusion of the landslides that occurred during recent EREs (since 2018) into the existing landslide inventory provides a more accurate and refined prediction of landslide susceptibility, which facilitates risk-informed landscape planning and development of the region. In addition, the result reveals that 13 % of the Kerala state is extremely susceptible for landslide occurrence, among this Idukki, Palakkad, Malappuram, Pathanamthitta, and Wayanad districts are highly vulnerable to the occurrence of landslides. Besides, the study also shows an increase of 3.46 % area in extreme susceptibility zone after the 2018 ERE. The updated landslide susceptibility map of the region may be used as a vital tool for planning landslide mitigation activities in the wake of recurrent EREs and associated landslide occurrences.
Remote Sensing Applications: Society and Environment, 2020
Abstract The present study aims to develop a spatially integrated evidential belief function base... more Abstract The present study aims to develop a spatially integrated evidential belief function based logistic regression model (EBF-LR) for landslide susceptibility mapping in a naturally sloping terrain of Southern Western Ghats in Kerala, India. For this, a landslide inventory map of 83 previous landslides was prepared using satellite imageries and verified in field check. Thereafter the landslide inventory was randomly divided into 70%–30% basis for model training and testing. Twelve landslide conditioning factors viz., lithology, land use/land cover, NDVI, slope angle, slope aspect, profile curvature, distance to stream, distance to roads, distance to lineaments, soil texture, topographic wetness index and average annual rainfall were considered for landslide susceptibility modelling. The resultant susceptibility maps were validated using receiver operating characteristics curve (ROC) with area under the curve (AUC) value, sensitivity, specificity, kappa index, mean absolute error (MAE) and root mean square error (RMSE). Analysis shows that the integrated EBF-LR model outweigh other conventional bivariate and multivariate approaches with a ROC-AUC value of 0.935, Kappa score 0.719, sensitivity 0.885, specificity 0.833 and least RMSE of 0.456 in the validation stage. The study also reveals that anthropogenic disturbances have a significant role on landslide initiation in the study area. Considering the ROC- AUC, MAE, RMSE and other validation measures, the accuracy of the proposed landslide susceptibility model is satisfactory and can be used for future land use planning and landslide mitigation in the study area.
Environmental Science and Pollution Research, 2021
Detailed investigation on hydrogeochemistry of hard rock terrains is important to identify the ma... more Detailed investigation on hydrogeochemistry of hard rock terrains is important to identify the major geochemical processes and the source of ionic constituents in groundwater. The present study is carried out to understand the hydrogeochemistry of groundwater resources and the major hydrogeochemical processes, controlling the concentration of major ions in groundwater in the Kallada River Basin (KRB), South India. About 166 groundwater samples were collected from KRB during pre- and post-monsoon of 2016 for hydrogeochemical analysis. Most of the groundwater samples in KRB were within permissible limits of drinking water quality. The dominant groundwater types during pre-monsoon were Ca2+-Mg2+-Cl− which was changed to Na+-Cl− during post-monsoon. This is supported by the inverse relationship of depth of wells and change in EC during pre- and post-monsoon periods. Rock-water interaction processes such as reverse ion exchange and silicate weathering are major geochemical processes responsible for the hydrogeochemical signatures of KRB. The shallower wells (< 10 m) show strongest relation with the water types Na+-HCO3− and Ca2+-Mg2+-Cl− which have been changed to Na+-Cl− and Ca2+-Mg2+-HCO3− during post-monsoon. However, in deeper wells, Na+-Cl− is the dominant type of water during both seasons. The hierarchical cluster analysis displays different hydrogeochemical associations representing diverse physicochemical parameters both spatially and temporally. This study could shed light on diverse hydrogeochemical processes which are responsible for the hydrogeochemistry in KRB. Major hydrogeochemical processes in the Kallada River Basi
Remote Sensing Applications: Society and Environment, 2021
Abstract The novel SARS-CoV-2 virus influenced the world severely in the first half of 2020 cause... more Abstract The novel SARS-CoV-2 virus influenced the world severely in the first half of 2020 caused shut down of all kind of human activities. It is reported that a word-wide ecological improvement in terms of air quality and water quality during this lock down period. In the present study, an attempt has been made to study the progression in water quality through examining suspended particulate matter using remote sensing data in a tropical Ramsar site viz, Asthamudi Lake in Southern India. The change in spectral reflectance of water along the study area were analyzed and suspended particulate matter (SPM) is estimated from Landsat 8 OLI images. A comparison analysis of pre and co lockdown periods reveal that the concentration of SPM values during lockdown (mean SPM 8.01 mg/l) is lower than that of pre-lockdown (10.03 mg/l). The time series analysis of last five-year data from 2015 to 2020 also shows an average decrease of 43% in SPM concentration during lockdown period compared to the last five-year average value of 9.1 mg/l. The reasons for improvement of SPM in water quality during the lockdown period in April–May 2020 was discussed, in terms of the role of anthropogenic activities and strategies for the sustainable management of coastal ecosystems and water resources in the Asthamudi Lake were also presented.
The present study aims to develop a spatially integrated evidential belief function based logisti... more The present study aims to develop a spatially integrated evidential belief function based logistic regression model (EBF-LR) for landslide susceptibility mapping in a naturally sloping terrain of Southern Western Ghats in Kerala, India. For this, a landslide inventory map of 83 previous landslides was prepared using satellite imageries and verified in field check. Thereafter the landslide inventory was randomly divided into 70%-30% basis for model training and testing. Twelve landslide conditioning factors viz., lithology, land use/land cover, NDVI, slope angle, slope aspect, profile curvature, distance to stream, distance to roads, distance to lineaments, soil texture, topographic wetness index and average annual rainfall were considered for landslide susceptibility modelling. The resultant susceptibility maps were validated using receiver operating characteristics curve (ROC) with area under the curve (AUC) value, sensitivity, specificity, kappa index, mean absolute error (MAE) and root mean square error (RMSE). Analysis shows that the integrated EBF-LR model outweigh other conventional bivariate and multivariate approaches with a ROC-AUC value of 0.935, Kappa score 0.719, sensitivity 0.885, specificity 0.833 and least RMSE of 0.456 in the validation stage. The study also reveals that anthropogenic disturbances have a significant role on landslide initiation in the study area. Considering the ROC-AUC, MAE, RMSE and other validation measures, the accuracy of the proposed landslide susceptibility model is satisfactory and can be used for future land use planning and landslide mitigation in the study area.
Accidents are distressing experiences which affect physical, psychological, and social welfare of... more Accidents are distressing experiences which affect physical, psychological, and social welfare of clans. Road accident fatalities are increasing in Kerala due to the traffic congestions and increase in both population and number of vehicles. The present study investigates the spatial and temporal patterns of the road accidents from 2013 to 2015 in Thrissur district, Kerala using geospatial technology. Assessment of spatio-temporal clustering of road accidents was carried out using Moran’s I method of spatial autocorrelation, Getis-Ord Gi* hotspot analysis, and Kernel density functions. The year wise total accidents show a clustered pattern, whereas the spatio-temporal breakup of events shows random and clustered distribution in certain classes. The continuous road stretches and isolated zones of hotspots and cold spots were delineated by the Kernel density surface using the results of Getis-Ord Gi* hotspots analysis. Four major road segments such as National highway near Kodungallur to Althara stretch, Thrissur town to Perumpilav stretch via Kunnamangalam, Thrissur town to Vadanappally segment, and Guruvayur to kunnamangalam stretch are identified as highly clustered accident hotspots. The results of this study can be effectively used by local self-governments, police departments, as well as national agencies for implementing better road safety policies in the accident hotspots stretches.
The rate of crime incidents is increasing in developing countries mainly due to the unequal distr... more The rate of crime incidents is increasing in developing countries mainly due to the unequal distribution of wealth and societal status. The present study attempts to identify and explore the rate and spatial variation of crime in Thiruvananthapuram city for a period from 2010 to 2014. The improved computer based technologies like GIS and availability of Geographic data make it possible for law and enforcement agencies to create analytical maps and various analysis to identify the crime hotspot area .The hotspot analysis in Geographic Information System is helpful for the identification of crime hotspot through spatial auto correlation, spatial analysis and interpolation. The Moran's I test statistic of spatial auto correlation has been done prior to Getis-Ord Gi* hotspot analysis to find out the clustering pattern as well as the outliers in the data. The crime hotspot analysis uses vectors to identify the locations of statistically significant crime hotspots and cold spots and IDW interpolation method is used for better visualization. These methods are applied on the crime data of Thiruvananthapuram city of Kerala state to find the hotspots for crime incidents like Murder, Robbery, Snatching and Theft.
International Journal of Remote Sensing Applications, 2016
The rate of crime incidents is increasing in developing countries mainly due to the unequal distr... more The rate of crime incidents is increasing in developing countries mainly due to the unequal distribution of wealth and societal status. The present study attempts to identify and explore the rate and spatial variation of crime in Thiruvananthapuram city for a period from 2010 to 2014. The improved computer based technologies like GIS and availability of Geographic data make it possible for law and enforcement agencies to create analytical maps and various analysis to identify the crime hotspot area .The hotspot analysis in Geographic Information System is helpful for the identification of crime hotspot through spatial auto correlation, spatial analysis and interpolation. The Moran's I test statistic of spatial auto correlation has been done prior to GetisOrd Gi* hotspot analysis to find out the clustering pattern as well as the outliers in the data. The crime hotspot analysis uses vectors to identify the locations of statistically significant crime hotspots and cold spots and IDW interpolation method is used for better visualization. These methods are applied on the crime data of Thiruvananthapuram city of Kerala state to find the hotspots for crime incidents like Murder, Robbery, Snatching and Theft.
Hypothyroidism is a disease assuming increasing relevance. The causative role of acidic nature of... more Hypothyroidism is a disease assuming increasing relevance. The causative role of acidic nature of drinking water has not yet been investigated in Kerala. We attempted to determine the spatial association between the occurrence of self-reported hypothyroidism and pH of ground water using the geographic information system. The cross-sectional study was conducted among 1649 individuals residing in the subcenter area in urban Trivandrum. Self-reported hypothyroidism was obtained by the interview. Differential Global Positioning System was used to record the location of each house and its drinking water source. PH of 50 open-well water samples was estimated. The prevalence of self-reported hypothyroidism was 4.24%. Maps depicting pH distribution and occurrence of hypothyroidism were prepared. Most of the areas had acidic ground water. Geo-statistical analysis revealed the occurrence of statistically significant clustering of hypothyroid individuals in areas having acidic ground water. The study brings out possible linkage between hypothyroidism and acidic water intake necessitating detailed epidemiological investigations for drawing more robust associations.
Abstract The recurrent forest fires have been a serious management concern in southern Western Gh... more Abstract The recurrent forest fires have been a serious management concern in southern Western Ghats, India. This study investigates the applicability of various geospatial data, machine learning techniques (MLTs) and spatial statistical tools to demarcate the forest fire susceptible regions of the forested landscape of the Wayanad district in the southern Western Ghats (Kerala, India). The inventory map of 279 forest fire locations (period = 2001–2018) was developed via Sentinel 2A satellite images, NASA fire archives, and field visits. The forest fire susceptibility modelling involves twelve influencing factors, such as ambient air temperature, wind speed, rainfall, relative humidity, atmospheric water vapor pressure (WVP), elevation, slope angle, topographical wetness index (TWI), slope aspect, land use/land cover (LU/LC), distance from the road and distance from the villages. Considering the varying level of performances (i.e., receiver operating characteristics-area under curve (ROC-AUC) values ranging from 0.869 to 0.924 in the testing phase) of the MLTs, viz., artificial neural network (ANN), generalized linear model (GLM), multivariate adaptive regression splines (MARS), Naive Bayesian classifier (NBC), K-nearest neighbour (KNN), support vector machine (SVM), random forest (RF), gradient boosting machine (GBM), adaptive boosting (AdaBoost) and maximum entropy (MaxEnt), we propose a weighted approach to characterize the forest fire susceptibility of the region using the outputs of the different MLTs. The proposed method demonstrates improvement in accuracy (AUC = 89%) for mapping the forest fire susceptibility of the region compared to the individual MLTs (AUC = 71.5 to 86.9%) while validating with the recent forest fire data (i.e., 2019–2021). This study suggests that roughly one-third of the study area is highly susceptible to the occurrence of forest fires, implying the severity of the disturbance regime. The analysis also indicates the role of anthropogenic factors in the occurrence of forest fires in the region. It is expected that the demarcation and prioritization of the forest fire susceptibility zones in the region, which is a part of one of the global biodiversity hotspots, have significant implications on biodiversity conservation at a regional scale.
ABSTRACT The study was undertaken to produce the landslide susceptibility maps by using Dempster–... more ABSTRACT The study was undertaken to produce the landslide susceptibility maps by using Dempster–Shafer, Bayesian probability and logistic regression methods for the southern Western Ghats, Kerala, India. A landslide inventory database of 82 landslides is prepared and used for landslide susceptibility modelling. Twelve landslide conditioning factors including lithology, geomorphological features, slope angle, soil texture, distance from stream, distance from road, distance from lineaments, land use/land cover, slope curvature, rainfall, topographic wetness index and relative relief are extracted from the spatial database and used for modelling. Multi-collinearity among the independent variables were tested and landslide susceptibility maps are constructed. The constructed models were validated with sensitivity, specificity, classification accuracy, ROC-AUC, root mean square error (RMSE) and kappa index. The Bayesian probability model obtained highest ROC-AUC (0.833), sensitivity (0.870), specificity (0.800) and kappa index (0.667) with least RMSE (0.4550) in validation phase. In addition, the study reveals that the agricultural areas have 10°–40° slopes falling on the denudational structural hills are extremely susceptible to landslide occurrence with extended influence from distance from roads, distance from streams and soil texture. The predicted model is trustworthy for future land use planning in the southern Western Ghats to mitigate the risk from landslide hazard.
Detailed investigation on hydrogeochemistry of hard rock terrains is important to identify the ma... more Detailed investigation on hydrogeochemistry of hard rock terrains is important to identify the major geochemical processes and the source of ionic constituents in groundwater. The present study is carried out to understand the hydrogeochemistry of groundwater resources and the major hydrogeochemical processes, controlling the concentration of major ions in groundwater in the Kallada River Basin (KRB), South India. About 166 groundwater samples were collected from KRB during pre-and post-monsoon of 2016 for hydrogeochemical analysis. Most of the groundwater samples in KRB were within permissible limits of drinking water quality. The dominant groundwater types during pre-monsoon were Ca 2+-Mg 2+-Cl − which was changed to Na +-Cl − during post-monsoon. This is supported by the inverse relationship of depth of wells and change in EC during pre-and post-monsoon periods. Rock-water interaction processes such as reverse ion exchange and silicate weathering are major geochemical processes responsible for the hydrogeochemical signatures of KRB. The shallower wells (< 10 m) show strongest relation with the water types Na +-HCO 3 − and Ca 2+-Mg 2+-Cl − which have been changed to Na +-Cl − and Ca 2+-Mg 2+-HCO 3 − during post-monsoon. However, in deeper wells, Na +-Cl − is the dominant type of water during both seasons. The hierarchical cluster analysis displays different hydrogeochemical associations representing diverse physicochemical parameters both spatially and temporally. This study could shed light on diverse hydrogeochemical processes which are responsible for the hydrogeochemistry in KRB.
The apparent changes in the Indian summer monsoon rainfall pattern and the nature of extreme rain... more The apparent changes in the Indian summer monsoon rainfall pattern and the nature of extreme rainfall events (EREs) in the southern Western Ghats (WG) caused widespread landslides across the region. Landslide susceptibility maps generated using the past landslide inventory are one of the efficient tools that can help mitigate the deleterious effects of landslides. Landslide susceptibility maps produced using the landslide inventory of normal and extreme rainfall years by deep neural network (DNN) modelling vary in terms of their degree of susceptibility, and the differences are prominent in the moderate and high susceptibility zones. The DNN model trained by landslide inventory of normal rainfall years is capable of predicting landslides during EREs (with Area Under the Curve (AUC) value >0.90). The inclusion of the landslides that occurred during recent EREs (since 2018) into the existing landslide inventory provides a more accurate and refined prediction of landslide susceptibility, which facilitates risk-informed landscape planning and development of the region. In addition, the result reveals that 13 % of the Kerala state is extremely susceptible for landslide occurrence, among this Idukki, Palakkad, Malappuram, Pathanamthitta, and Wayanad districts are highly vulnerable to the occurrence of landslides. Besides, the study also shows an increase of 3.46 % area in extreme susceptibility zone after the 2018 ERE. The updated landslide susceptibility map of the region may be used as a vital tool for planning landslide mitigation activities in the wake of recurrent EREs and associated landslide occurrences.
Remote Sensing Applications: Society and Environment, 2020
Abstract The present study aims to develop a spatially integrated evidential belief function base... more Abstract The present study aims to develop a spatially integrated evidential belief function based logistic regression model (EBF-LR) for landslide susceptibility mapping in a naturally sloping terrain of Southern Western Ghats in Kerala, India. For this, a landslide inventory map of 83 previous landslides was prepared using satellite imageries and verified in field check. Thereafter the landslide inventory was randomly divided into 70%–30% basis for model training and testing. Twelve landslide conditioning factors viz., lithology, land use/land cover, NDVI, slope angle, slope aspect, profile curvature, distance to stream, distance to roads, distance to lineaments, soil texture, topographic wetness index and average annual rainfall were considered for landslide susceptibility modelling. The resultant susceptibility maps were validated using receiver operating characteristics curve (ROC) with area under the curve (AUC) value, sensitivity, specificity, kappa index, mean absolute error (MAE) and root mean square error (RMSE). Analysis shows that the integrated EBF-LR model outweigh other conventional bivariate and multivariate approaches with a ROC-AUC value of 0.935, Kappa score 0.719, sensitivity 0.885, specificity 0.833 and least RMSE of 0.456 in the validation stage. The study also reveals that anthropogenic disturbances have a significant role on landslide initiation in the study area. Considering the ROC- AUC, MAE, RMSE and other validation measures, the accuracy of the proposed landslide susceptibility model is satisfactory and can be used for future land use planning and landslide mitigation in the study area.
Environmental Science and Pollution Research, 2021
Detailed investigation on hydrogeochemistry of hard rock terrains is important to identify the ma... more Detailed investigation on hydrogeochemistry of hard rock terrains is important to identify the major geochemical processes and the source of ionic constituents in groundwater. The present study is carried out to understand the hydrogeochemistry of groundwater resources and the major hydrogeochemical processes, controlling the concentration of major ions in groundwater in the Kallada River Basin (KRB), South India. About 166 groundwater samples were collected from KRB during pre- and post-monsoon of 2016 for hydrogeochemical analysis. Most of the groundwater samples in KRB were within permissible limits of drinking water quality. The dominant groundwater types during pre-monsoon were Ca2+-Mg2+-Cl− which was changed to Na+-Cl− during post-monsoon. This is supported by the inverse relationship of depth of wells and change in EC during pre- and post-monsoon periods. Rock-water interaction processes such as reverse ion exchange and silicate weathering are major geochemical processes responsible for the hydrogeochemical signatures of KRB. The shallower wells (< 10 m) show strongest relation with the water types Na+-HCO3− and Ca2+-Mg2+-Cl− which have been changed to Na+-Cl− and Ca2+-Mg2+-HCO3− during post-monsoon. However, in deeper wells, Na+-Cl− is the dominant type of water during both seasons. The hierarchical cluster analysis displays different hydrogeochemical associations representing diverse physicochemical parameters both spatially and temporally. This study could shed light on diverse hydrogeochemical processes which are responsible for the hydrogeochemistry in KRB. Major hydrogeochemical processes in the Kallada River Basi
Remote Sensing Applications: Society and Environment, 2021
Abstract The novel SARS-CoV-2 virus influenced the world severely in the first half of 2020 cause... more Abstract The novel SARS-CoV-2 virus influenced the world severely in the first half of 2020 caused shut down of all kind of human activities. It is reported that a word-wide ecological improvement in terms of air quality and water quality during this lock down period. In the present study, an attempt has been made to study the progression in water quality through examining suspended particulate matter using remote sensing data in a tropical Ramsar site viz, Asthamudi Lake in Southern India. The change in spectral reflectance of water along the study area were analyzed and suspended particulate matter (SPM) is estimated from Landsat 8 OLI images. A comparison analysis of pre and co lockdown periods reveal that the concentration of SPM values during lockdown (mean SPM 8.01 mg/l) is lower than that of pre-lockdown (10.03 mg/l). The time series analysis of last five-year data from 2015 to 2020 also shows an average decrease of 43% in SPM concentration during lockdown period compared to the last five-year average value of 9.1 mg/l. The reasons for improvement of SPM in water quality during the lockdown period in April–May 2020 was discussed, in terms of the role of anthropogenic activities and strategies for the sustainable management of coastal ecosystems and water resources in the Asthamudi Lake were also presented.
The present study aims to develop a spatially integrated evidential belief function based logisti... more The present study aims to develop a spatially integrated evidential belief function based logistic regression model (EBF-LR) for landslide susceptibility mapping in a naturally sloping terrain of Southern Western Ghats in Kerala, India. For this, a landslide inventory map of 83 previous landslides was prepared using satellite imageries and verified in field check. Thereafter the landslide inventory was randomly divided into 70%-30% basis for model training and testing. Twelve landslide conditioning factors viz., lithology, land use/land cover, NDVI, slope angle, slope aspect, profile curvature, distance to stream, distance to roads, distance to lineaments, soil texture, topographic wetness index and average annual rainfall were considered for landslide susceptibility modelling. The resultant susceptibility maps were validated using receiver operating characteristics curve (ROC) with area under the curve (AUC) value, sensitivity, specificity, kappa index, mean absolute error (MAE) and root mean square error (RMSE). Analysis shows that the integrated EBF-LR model outweigh other conventional bivariate and multivariate approaches with a ROC-AUC value of 0.935, Kappa score 0.719, sensitivity 0.885, specificity 0.833 and least RMSE of 0.456 in the validation stage. The study also reveals that anthropogenic disturbances have a significant role on landslide initiation in the study area. Considering the ROC-AUC, MAE, RMSE and other validation measures, the accuracy of the proposed landslide susceptibility model is satisfactory and can be used for future land use planning and landslide mitigation in the study area.
Accidents are distressing experiences which affect physical, psychological, and social welfare of... more Accidents are distressing experiences which affect physical, psychological, and social welfare of clans. Road accident fatalities are increasing in Kerala due to the traffic congestions and increase in both population and number of vehicles. The present study investigates the spatial and temporal patterns of the road accidents from 2013 to 2015 in Thrissur district, Kerala using geospatial technology. Assessment of spatio-temporal clustering of road accidents was carried out using Moran’s I method of spatial autocorrelation, Getis-Ord Gi* hotspot analysis, and Kernel density functions. The year wise total accidents show a clustered pattern, whereas the spatio-temporal breakup of events shows random and clustered distribution in certain classes. The continuous road stretches and isolated zones of hotspots and cold spots were delineated by the Kernel density surface using the results of Getis-Ord Gi* hotspots analysis. Four major road segments such as National highway near Kodungallur to Althara stretch, Thrissur town to Perumpilav stretch via Kunnamangalam, Thrissur town to Vadanappally segment, and Guruvayur to kunnamangalam stretch are identified as highly clustered accident hotspots. The results of this study can be effectively used by local self-governments, police departments, as well as national agencies for implementing better road safety policies in the accident hotspots stretches.
The rate of crime incidents is increasing in developing countries mainly due to the unequal distr... more The rate of crime incidents is increasing in developing countries mainly due to the unequal distribution of wealth and societal status. The present study attempts to identify and explore the rate and spatial variation of crime in Thiruvananthapuram city for a period from 2010 to 2014. The improved computer based technologies like GIS and availability of Geographic data make it possible for law and enforcement agencies to create analytical maps and various analysis to identify the crime hotspot area .The hotspot analysis in Geographic Information System is helpful for the identification of crime hotspot through spatial auto correlation, spatial analysis and interpolation. The Moran's I test statistic of spatial auto correlation has been done prior to Getis-Ord Gi* hotspot analysis to find out the clustering pattern as well as the outliers in the data. The crime hotspot analysis uses vectors to identify the locations of statistically significant crime hotspots and cold spots and IDW interpolation method is used for better visualization. These methods are applied on the crime data of Thiruvananthapuram city of Kerala state to find the hotspots for crime incidents like Murder, Robbery, Snatching and Theft.
International Journal of Remote Sensing Applications, 2016
The rate of crime incidents is increasing in developing countries mainly due to the unequal distr... more The rate of crime incidents is increasing in developing countries mainly due to the unequal distribution of wealth and societal status. The present study attempts to identify and explore the rate and spatial variation of crime in Thiruvananthapuram city for a period from 2010 to 2014. The improved computer based technologies like GIS and availability of Geographic data make it possible for law and enforcement agencies to create analytical maps and various analysis to identify the crime hotspot area .The hotspot analysis in Geographic Information System is helpful for the identification of crime hotspot through spatial auto correlation, spatial analysis and interpolation. The Moran's I test statistic of spatial auto correlation has been done prior to GetisOrd Gi* hotspot analysis to find out the clustering pattern as well as the outliers in the data. The crime hotspot analysis uses vectors to identify the locations of statistically significant crime hotspots and cold spots and IDW interpolation method is used for better visualization. These methods are applied on the crime data of Thiruvananthapuram city of Kerala state to find the hotspots for crime incidents like Murder, Robbery, Snatching and Theft.
Hypothyroidism is a disease assuming increasing relevance. The causative role of acidic nature of... more Hypothyroidism is a disease assuming increasing relevance. The causative role of acidic nature of drinking water has not yet been investigated in Kerala. We attempted to determine the spatial association between the occurrence of self-reported hypothyroidism and pH of ground water using the geographic information system. The cross-sectional study was conducted among 1649 individuals residing in the subcenter area in urban Trivandrum. Self-reported hypothyroidism was obtained by the interview. Differential Global Positioning System was used to record the location of each house and its drinking water source. PH of 50 open-well water samples was estimated. The prevalence of self-reported hypothyroidism was 4.24%. Maps depicting pH distribution and occurrence of hypothyroidism were prepared. Most of the areas had acidic ground water. Geo-statistical analysis revealed the occurrence of statistically significant clustering of hypothyroid individuals in areas having acidic ground water. The study brings out possible linkage between hypothyroidism and acidic water intake necessitating detailed epidemiological investigations for drawing more robust associations.
Abstract The recurrent forest fires have been a serious management concern in southern Western Gh... more Abstract The recurrent forest fires have been a serious management concern in southern Western Ghats, India. This study investigates the applicability of various geospatial data, machine learning techniques (MLTs) and spatial statistical tools to demarcate the forest fire susceptible regions of the forested landscape of the Wayanad district in the southern Western Ghats (Kerala, India). The inventory map of 279 forest fire locations (period = 2001–2018) was developed via Sentinel 2A satellite images, NASA fire archives, and field visits. The forest fire susceptibility modelling involves twelve influencing factors, such as ambient air temperature, wind speed, rainfall, relative humidity, atmospheric water vapor pressure (WVP), elevation, slope angle, topographical wetness index (TWI), slope aspect, land use/land cover (LU/LC), distance from the road and distance from the villages. Considering the varying level of performances (i.e., receiver operating characteristics-area under curve (ROC-AUC) values ranging from 0.869 to 0.924 in the testing phase) of the MLTs, viz., artificial neural network (ANN), generalized linear model (GLM), multivariate adaptive regression splines (MARS), Naive Bayesian classifier (NBC), K-nearest neighbour (KNN), support vector machine (SVM), random forest (RF), gradient boosting machine (GBM), adaptive boosting (AdaBoost) and maximum entropy (MaxEnt), we propose a weighted approach to characterize the forest fire susceptibility of the region using the outputs of the different MLTs. The proposed method demonstrates improvement in accuracy (AUC = 89%) for mapping the forest fire susceptibility of the region compared to the individual MLTs (AUC = 71.5 to 86.9%) while validating with the recent forest fire data (i.e., 2019–2021). This study suggests that roughly one-third of the study area is highly susceptible to the occurrence of forest fires, implying the severity of the disturbance regime. The analysis also indicates the role of anthropogenic factors in the occurrence of forest fires in the region. It is expected that the demarcation and prioritization of the forest fire susceptibility zones in the region, which is a part of one of the global biodiversity hotspots, have significant implications on biodiversity conservation at a regional scale.
ABSTRACT The study was undertaken to produce the landslide susceptibility maps by using Dempster–... more ABSTRACT The study was undertaken to produce the landslide susceptibility maps by using Dempster–Shafer, Bayesian probability and logistic regression methods for the southern Western Ghats, Kerala, India. A landslide inventory database of 82 landslides is prepared and used for landslide susceptibility modelling. Twelve landslide conditioning factors including lithology, geomorphological features, slope angle, soil texture, distance from stream, distance from road, distance from lineaments, land use/land cover, slope curvature, rainfall, topographic wetness index and relative relief are extracted from the spatial database and used for modelling. Multi-collinearity among the independent variables were tested and landslide susceptibility maps are constructed. The constructed models were validated with sensitivity, specificity, classification accuracy, ROC-AUC, root mean square error (RMSE) and kappa index. The Bayesian probability model obtained highest ROC-AUC (0.833), sensitivity (0.870), specificity (0.800) and kappa index (0.667) with least RMSE (0.4550) in validation phase. In addition, the study reveals that the agricultural areas have 10°–40° slopes falling on the denudational structural hills are extremely susceptible to landslide occurrence with extended influence from distance from roads, distance from streams and soil texture. The predicted model is trustworthy for future land use planning in the southern Western Ghats to mitigate the risk from landslide hazard.
Detailed investigation on hydrogeochemistry of hard rock terrains is important to identify the ma... more Detailed investigation on hydrogeochemistry of hard rock terrains is important to identify the major geochemical processes and the source of ionic constituents in groundwater. The present study is carried out to understand the hydrogeochemistry of groundwater resources and the major hydrogeochemical processes, controlling the concentration of major ions in groundwater in the Kallada River Basin (KRB), South India. About 166 groundwater samples were collected from KRB during pre-and post-monsoon of 2016 for hydrogeochemical analysis. Most of the groundwater samples in KRB were within permissible limits of drinking water quality. The dominant groundwater types during pre-monsoon were Ca 2+-Mg 2+-Cl − which was changed to Na +-Cl − during post-monsoon. This is supported by the inverse relationship of depth of wells and change in EC during pre-and post-monsoon periods. Rock-water interaction processes such as reverse ion exchange and silicate weathering are major geochemical processes responsible for the hydrogeochemical signatures of KRB. The shallower wells (< 10 m) show strongest relation with the water types Na +-HCO 3 − and Ca 2+-Mg 2+-Cl − which have been changed to Na +-Cl − and Ca 2+-Mg 2+-HCO 3 − during post-monsoon. However, in deeper wells, Na +-Cl − is the dominant type of water during both seasons. The hierarchical cluster analysis displays different hydrogeochemical associations representing diverse physicochemical parameters both spatially and temporally. This study could shed light on diverse hydrogeochemical processes which are responsible for the hydrogeochemistry in KRB.
Western Ghats (WG) of India is experiencing frequent landslides during every Indian summer monsoo... more Western Ghats (WG) of India is experiencing frequent landslides during every Indian summer monsoon. Due to the unique blend of topography and tropical humid climate, accelerates chemical weathering, forming a layer of unconsolidated soil unconformably overlies the Precambrian crystalline rock. Lack of cohesion or bonding in these contrasting geologic materials, makes WG vulnerable to various forms of landslides during the peak of Indian summer monsoon. Hence detailed information about soil thickness has a predominant role in identifying the landslide prone area and understanding the landslides in WG. However, soil thickness maps are not available for WG area and steep rugged terrain makes it difficult to collect detailed soil thickness data. This study used a random forest (RF) machine-learning model to predict the soil depth with a limited number of sparse samples in the Panniar river basin of WG. The model was combined using 70 soil depth observations with eleven covariates such a...
<p>The Western Ghats (WG), an elevated passive continental margin along the southwestern co... more <p>The Western Ghats (WG), an elevated passive continental margin along the southwestern coast of India, is the most widely populated biodiversity hot spot in the world. Monsoon climate is prevalent throughout the length of the Western Ghats. The WG region is prone to the occurrence of various hydro-climatic disasters such as extreme rainfall-driven floods and landslides. During the past 100 years, landslides and floods caused by extreme rainfall events in the WG have occurred in 1924 and 1979; but the most disastrous event, in terms of area of impact, loss of life and economic impact, occurred in August 2018. Generally, the south-west monsoon (Indian summer monsoon) occurs in the first week of June and extends up to September and the Indian Meteorological Department (IMD) predicted above-normal rainfall of 13% during the month of August 2018. But the State received an excess of 96% during the period from 1st to 30th August 2018, and 33% during the entire monsoon period till the end of August. The unprecedented heavy rains, storms, floods and associated thousands of landslides have caused exorbitant losses including 400 life losses, over 2.20 lakh people were displaced, and 20000 homes and 80 dams were damaged or destructed. This study aimed to elucidate the reasons behind the thousands of landslides caused in WG using observed and field evidences. Changes in south-west monsoon pattern and rainfall intensity played a vital role in the occurrence of landslides in WG. Further, the extensive causalities are the result of anthropogenic disturbances including landscape alterations and improper landuse practices in the hilly tracks of WG. The major causative factors for series of landslides in various segments of WG is due to hindrance of lower order streams/springs, vertical cutting, intensive quarrying, unscientific rain pits & man-made structures together with erratic rainfall triggered major and minor landslides in various segments of WG. The present investigation concludes that a scientific landuse policy and geoscientific awareness is essential to mitigate the environment.</p>
Western Ghats (WG) of India is experiencing frequent landslides during every Indian summer monsoo... more Western Ghats (WG) of India is experiencing frequent landslides during every Indian summer monsoon. Due to the unique blend of topography and tropical humid climate, accelerates chemical weathering, forming a layer of unconsolidated soil unconformably overlies the Precambrian crystalline rock. Lack of cohesion or bonding in these contrasting geologic materials, makes WG vulnerable to various forms of landslides during the peak of Indian summer monsoon. Hence detailed information about soil thickness has a predominant role in identifying the landslide prone area and understanding the landslides in WG. However, soil thickness maps are not available for WG area and steep rugged terrain makes it difficult to collect detailed soil thickness data. This study used a random forest (RF) machine-learning model to predict the soil depth with a limited number of sparse samples in the Panniar river basin of WG. The model was combined using 70 soil depth observations with eleven covariates such a...
Remote Sensing Applications: Society and Environment, 2021
Abstract The novel SARS-CoV-2 virus influenced the world severely in the first half of 2020 cause... more Abstract The novel SARS-CoV-2 virus influenced the world severely in the first half of 2020 caused shut down of all kind of human activities. It is reported that a word-wide ecological improvement in terms of air quality and water quality during this lock down period. In the present study, an attempt has been made to study the progression in water quality through examining suspended particulate matter using remote sensing data in a tropical Ramsar site viz, Asthamudi Lake in Southern India. The change in spectral reflectance of water along the study area were analyzed and suspended particulate matter (SPM) is estimated from Landsat 8 OLI images. A comparison analysis of pre and co lockdown periods reveal that the concentration of SPM values during lockdown (mean SPM 8.01 mg/l) is lower than that of pre-lockdown (10.03 mg/l). The time series analysis of last five-year data from 2015 to 2020 also shows an average decrease of 43% in SPM concentration during lockdown period compared to the last five-year average value of 9.1 mg/l. The reasons for improvement of SPM in water quality during the lockdown period in April–May 2020 was discussed, in terms of the role of anthropogenic activities and strategies for the sustainable management of coastal ecosystems and water resources in the Asthamudi Lake were also presented.
<p>The Western Ghats (WG), an elevated passive continental margin along the southwestern co... more <p>The Western Ghats (WG), an elevated passive continental margin along the southwestern coast of India, is the most widely populated biodiversity hot spot in the world. Monsoon climate is prevalent throughout the length of the Western Ghats. The WG region is prone to the occurrence of various hydro-climatic disasters such as extreme rainfall-driven floods and landslides. During the past 100 years, landslides and floods caused by extreme rainfall events in the WG have occurred in 1924 and 1979; but the most disastrous event, in terms of area of impact, loss of life and economic impact, occurred in August 2018. Generally, the south-west monsoon (Indian summer monsoon) occurs in the first week of June and extends up to September and the Indian Meteorological Department (IMD) predicted above-normal rainfall of 13% during the month of August 2018. But the State received an excess of 96% during the period from 1st to 30th August 2018, and 33% during the entire monsoon period till the end of August. The unprecedented heavy rains, storms, floods and associated thousands of landslides have caused exorbitant losses including 400 life losses, over 2.20 lakh people were displaced, and 20000 homes and 80 dams were damaged or destructed. This study aimed to elucidate the reasons behind the thousands of landslides caused in WG using observed and field evidences. Changes in south-west monsoon pattern and rainfall intensity played a vital role in the occurrence of landslides in WG. Further, the extensive causalities are the result of anthropogenic disturbances including landscape alterations and improper landuse practices in the hilly tracks of WG. The major causative factors for series of landslides in various segments of WG is due to hindrance of lower order streams/springs, vertical cutting, intensive quarrying, unscientific rain pits & man-made structures together with erratic rainfall triggered major and minor landslides in various segments of WG. The present investigation concludes that a scientific landuse policy and geoscientific awareness is essential to mitigate the environment.</p>
Environmental Science and Pollution Research, 2021
Detailed investigation on hydrogeochemistry of hard rock terrains is important to identify the ma... more Detailed investigation on hydrogeochemistry of hard rock terrains is important to identify the major geochemical processes and the source of ionic constituents in groundwater. The present study is carried out to understand the hydrogeochemistry of groundwater resources and the major hydrogeochemical processes, controlling the concentration of major ions in groundwater in the Kallada River Basin (KRB), South India. About 166 groundwater samples were collected from KRB during pre- and post-monsoon of 2016 for hydrogeochemical analysis. Most of the groundwater samples in KRB were within permissible limits of drinking water quality. The dominant groundwater types during pre-monsoon were Ca2+-Mg2+-Cl− which was changed to Na+-Cl− during post-monsoon. This is supported by the inverse relationship of depth of wells and change in EC during pre- and post-monsoon periods. Rock-water interaction processes such as reverse ion exchange and silicate weathering are major geochemical processes responsible for the hydrogeochemical signatures of KRB. The shallower wells (< 10 m) show strongest relation with the water types Na+-HCO3− and Ca2+-Mg2+-Cl− which have been changed to Na+-Cl− and Ca2+-Mg2+-HCO3− during post-monsoon. However, in deeper wells, Na+-Cl− is the dominant type of water during both seasons. The hierarchical cluster analysis displays different hydrogeochemical associations representing diverse physicochemical parameters both spatially and temporally. This study could shed light on diverse hydrogeochemical processes which are responsible for the hydrogeochemistry in KRB. Major hydrogeochemical processes in the Kallada River Basi
Remote Sensing Applications: Society and Environment, 2020
Abstract The present study aims to develop a spatially integrated evidential belief function base... more Abstract The present study aims to develop a spatially integrated evidential belief function based logistic regression model (EBF-LR) for landslide susceptibility mapping in a naturally sloping terrain of Southern Western Ghats in Kerala, India. For this, a landslide inventory map of 83 previous landslides was prepared using satellite imageries and verified in field check. Thereafter the landslide inventory was randomly divided into 70%–30% basis for model training and testing. Twelve landslide conditioning factors viz., lithology, land use/land cover, NDVI, slope angle, slope aspect, profile curvature, distance to stream, distance to roads, distance to lineaments, soil texture, topographic wetness index and average annual rainfall were considered for landslide susceptibility modelling. The resultant susceptibility maps were validated using receiver operating characteristics curve (ROC) with area under the curve (AUC) value, sensitivity, specificity, kappa index, mean absolute error (MAE) and root mean square error (RMSE). Analysis shows that the integrated EBF-LR model outweigh other conventional bivariate and multivariate approaches with a ROC-AUC value of 0.935, Kappa score 0.719, sensitivity 0.885, specificity 0.833 and least RMSE of 0.456 in the validation stage. The study also reveals that anthropogenic disturbances have a significant role on landslide initiation in the study area. Considering the ROC- AUC, MAE, RMSE and other validation measures, the accuracy of the proposed landslide susceptibility model is satisfactory and can be used for future land use planning and landslide mitigation in the study area.
Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in the world. The de... more Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in the world. The determinants of CVD in an urban population using conventional and geographic information system techniques were attempted as a community-based census-type cross-sectional study in Kerala, India, among 1649 individuals residing in 452 households. Sociodemographic details, risk factor exposures, and self-reported disease prevalence were determined. Location of houses, wells from which subjects drew drinking water, and distances of the house from the outer road (proxy for air pollution) were mapped using differential global positioning system and pH of water samples determined. Prevalence of CVD was 5.8%. Significant predictors of CVD were male gender, diabetes mellitus, hypertension, and hypothyroidism. Statistically significant spatial association was found between CVD and groundwater pH. Geographic information system technology is useful in identification of spatial clustering and disease ...
KN - Journal of Cartography and Geographic Information, 2019
Accidents are distressing experiences which affect physical, psychological, and social welfare of... more Accidents are distressing experiences which affect physical, psychological, and social welfare of clans. Road accident fatalities are increasing in Kerala due to the traffic congestions and increase in both population and number of vehicles. The present study investigates the spatial and temporal patterns of the road accidents from 2013 to 2015 in Thrissur district, Kerala using geospatial technology. Assessment of spatio-temporal clustering of road accidents was carried out using Moran’s I method of spatial autocorrelation, Getis-Ord Gi* hotspot analysis, and Kernel density functions. The year wise total accidents show a clustered pattern, whereas the spatio-temporal breakup of events shows random and clustered distribution in certain classes. The continuous road stretches and isolated zones of hotspots and cold spots were delineated by the Kernel density surface using the results of Getis-Ord Gi* hotspots analysis. Four major road segments such as National highway near Kodungallur to Althara stretch, Thrissur town to Perumpilav stretch via Kunnamangalam, Thrissur town to Vadanappally segment, and Guruvayur to kunnamangalam stretch are identified as highly clustered accident hotspots. The results of this study can be effectively used by local self-governments, police departments, as well as national agencies for implementing better road safety policies in the accident hotspots stretches.
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Journal Publications by Achu A L
are increasing in Kerala due to the traffic congestions and increase in both population and number of vehicles. The present
study investigates the spatial and temporal patterns of the road accidents from 2013 to 2015 in Thrissur district, Kerala using
geospatial technology. Assessment of spatio-temporal clustering of road accidents was carried out using Moran’s I method of
spatial autocorrelation, Getis-Ord Gi* hotspot analysis, and Kernel density functions. The year wise total accidents show a
clustered pattern, whereas the spatio-temporal breakup of events shows random and clustered distribution in certain classes.
The continuous road stretches and isolated zones of hotspots and cold spots were delineated by the Kernel density surface
using the results of Getis-Ord Gi* hotspots analysis. Four major road segments such as National highway near Kodungallur
to Althara stretch, Thrissur town to Perumpilav stretch via Kunnamangalam, Thrissur town to Vadanappally segment, and
Guruvayur to kunnamangalam stretch are identified as highly clustered accident hotspots. The results of this study can be
effectively used by local self-governments, police departments, as well as national agencies for implementing better road
safety policies in the accident hotspots stretches.
are increasing in Kerala due to the traffic congestions and increase in both population and number of vehicles. The present
study investigates the spatial and temporal patterns of the road accidents from 2013 to 2015 in Thrissur district, Kerala using
geospatial technology. Assessment of spatio-temporal clustering of road accidents was carried out using Moran’s I method of
spatial autocorrelation, Getis-Ord Gi* hotspot analysis, and Kernel density functions. The year wise total accidents show a
clustered pattern, whereas the spatio-temporal breakup of events shows random and clustered distribution in certain classes.
The continuous road stretches and isolated zones of hotspots and cold spots were delineated by the Kernel density surface
using the results of Getis-Ord Gi* hotspots analysis. Four major road segments such as National highway near Kodungallur
to Althara stretch, Thrissur town to Perumpilav stretch via Kunnamangalam, Thrissur town to Vadanappally segment, and
Guruvayur to kunnamangalam stretch are identified as highly clustered accident hotspots. The results of this study can be
effectively used by local self-governments, police departments, as well as national agencies for implementing better road
safety policies in the accident hotspots stretches.