I am an environmental geographer who studies spatial change related to a variety of processes. I am principally interested in extreme natural events that sometimes beget disasters, but often prompt changes in spatial patterns of human and natural landscapes. Most recently, I have been focused on the understanding how viticulture is affected by changing climates and extreme events like wildfires that reflect changing weather and ecological conditions. Phone: 512-245-8327 Address: Department of Geography Texas State University, San Marcos 601 University Dr. San Marcos, TX 78666
Natural Hazards - Risk, Exposure, Response, and Resilience demonstrates advanced techniques to me... more Natural Hazards - Risk, Exposure, Response, and Resilience demonstrates advanced techniques to measure risks, exposures, responses, and solutions to hazards in an array of communities. Eleven original research reports by international scholars on hazard assessment and management are organized into four sections: studies assessing risk using in-depth modeling and technological detection to provide insight into problems associated with earthquakes, torrential rains, and nuclear power plant safety; studies revealing the spatial distributions of exposure and impacts from an assortment of hazards; studies examining human response to increased awareness of the patterns of hazard; and a study demonstrating assessment of resilience of sociotechnological systems to natural hazards. This volume contributes new conceptual and practical commentaries to assess, mitigate, and plan for disasters
Human perceptions and concerns about the implications of climate change have a profound importanc... more Human perceptions and concerns about the implications of climate change have a profound importance for adaptation to and mitigation of the impacts of climate change at various scales and in different economic and social sectors in a given geographical landscape. Although studies on public perceptions of climate change are increasing globally, they are still scant pertaining to the Himalayan region of Nepal. In order to fill this gap, this study aimed to understand individual and community perceptions of climate change in the Lower Mustang region of Nepal, where apple farming and agricultural activities have been significantly impacted by climate change over the last 30–40 years. The data collection for the study included a cross-sectional survey of 200 farmers with a structured questionnaire, seven focus group discussions, and 26 key informant interviews, which were administered in four rural municipalities: Kagbeni, Jomsom, Marpha, and Tukuche. Results of the study indicated that a...
Growing demand for water, as a consequence of population growth, farmland irrigation, and industr... more Growing demand for water, as a consequence of population growth, farmland irrigation, and industrial expansion, results in groundwater resources exploitation. This, in combination with droughts induced by climate change, has caused a sharp drop in groundwater levels throughout arid and semiarid countries. In Iran, all these factors are resulting in alarming water scarcity. Appropriate management and control of existing water resources can overcome water shortages, with healthy and sustainable management of groundwater as one of the most efficient tools. Artificial recharge of aquifers can be used to replenish water supplies and restore the water resources in Iran and other semiarid and arid countries, but selection of the right location for runoff collection is essential to achieve success. Precipitation, slope, geology, lineament density, drainage density, aquifer water quality, groundwater level, vegetation, and land use were selected in this study as key factors in locating suitable sites for artificial recharge. The weight of each, in terms of importance and impact on aquifer recharge, was determined using remote sensing techniques to prepare layers and analytic hierarchy process (AHP) and Boolean logic to identify the optimal weight for each factor. Geographic information system (GIS) was used for modeling, applying the weight of each criterion, and producing a final map. The results showed better performance of AHP than Boolean logic. For artificial recharge, 9.9% of the total study area (Mahdishahr in northern Iran) was found to be in a very good position and 22.6% in a good position. On filtering the privacy layer of fountains and aqueducts, the very good and good area declined to 8.4% and 14.7% of the total area, respectively, and mainly comprised alluvial valleys and coarse alluvial sediments with low slope and drainage density.
Groundwater depletion is an important problem driven by population growth, land use and land cove... more Groundwater depletion is an important problem driven by population growth, land use and land cover (LULC) change, climate change, and other factors. Groundwater depletion generates water stress and encourages unstainable resource use. The aim of this study is to determine how population growth, LULC change, and climate change relate to groundwater depletion in the Alto Atoyac sub-basin, Oaxaca, Mexico. Twenty-five years of dry season water table data from 1984 to 2009 are analyzed to examine annual groundwater depletion. Kriging is used to interpolate the region’s groundwater levels in a geographic information system (GIS) from mapped point measurements. An analysis of remotely sensed data revealed patterns of LULC change during a 34-year (1986–2018) period, using a supervised, machine-learning classification algorithm to calculate the changes in LULC. This analysis is shown to have an 85% accuracy. A global circulation model (GFDL-CM3) and the RCP4.5 and RCP8.5 scenarios were used ...
River water quality assessment is one of the most important tasks to enhance water resources mana... more River water quality assessment is one of the most important tasks to enhance water resources management plans. A water quality index (WQI) considers several water quality variables simultaneously. Traditionally WQI calculations consume time and are often fraught with errors during derivations of sub-indices. In this study, 4 standalone (random forest (RF), M5P, random tree (RT), and reduced error pruning tree (REPT)) and 12 hybrid data-mining algorithms (combinations of standalones with bagging (BA), CV parameter selection (CVPS) and randomizable filtered classification (RFC)) were used to create Iran WQI (IRWQIsc) predictions. Six years (2012 to 2018) of monthly data from two water quality monitoring stations within the Talar catchment were compiled. Using Pearson correlation coefficients, 10 different input combinations were constructed. The data were divided into two groups (ratio 70:30) for model building (training dataset) and model validation (testing dataset) using a 10-fold cross-validation technique. The models were evaluated using several statistical and visual evaluation metrics. Result show that fecal coliform (FC) and total solids (TS) had the greatest and least effect on the prediction of IRWQIsc. The best input combinations varied among the algorithms; generally variables with very low correlations displayed weaker performance. Hybrid algorithms improved the prediction power of several of the standalone models, but not all. Hybrid BA-RT outperformed the other models (R2 = 0.941, RMSE = 2.71, MAE = 1.87, NSE = 0.941, PBIAS = 0.500). PBIAS indicated that all algorithms, with the exceptions of RT, BA-RT and CVPS-REPT, overestimated WQI values.
Land subsidence (LS) is a significant problem that can cause loss of life, damage property, and d... more Land subsidence (LS) is a significant problem that can cause loss of life, damage property, and disrupt local economies. The Semnan Plain is an important part of Iran, where LS is a major problem for sustainable development and management. The plain represents the changes occurring in 40% of the country. We introduce a novel-ensemble intelligence approach (called ANN-bagging) that uses bagging as a meta- or ensemble-classifier of an artificial neural network (ANN) to predict LS spatially on the Semnan Plain in Semnan Province, Iran. The ensemble model's goodness-of-fit (to training data) and prediction accuracy (of the validation data) are compared to benchmarks set by ANN-bagging. A total of 96 locations of LS and 12 LS conditioning factors (LSCFs) were collected. Each feature in the LS inventory map (LSIM) was randomly assigned to one of four groups or folds, each comprising 25% of cases. The novel ensemble model was trained using 75% (3 folds) and validated with the remaining 25% (1 fold) in a four-fold cross-validation (CV) system, which is used to control for the effects of the random selection of the training and validation datasets. LSCFs for LS prediction were selected using the information-gain ratio and multi-collinearity test methods. Factor significance was evaluated using a random forest (RF) model. Groundwater drawdown, land use and land cover, elevation, and lithology were the most important LSCFs. Using the k-fold CV approaches, twelve LS susceptibility maps (LSSMs) were prepared as each fold employed all three models (ANN-bagging, ANN, and bagging). The LS susceptibility mapping showed that between 5.7% and 12.6% of the plain had very high LS susceptibility. All three models produced LS susceptibility maps with acceptable prediction accuracies and goodness-of-fits, but the best maps were produced by the ANN-bagging ensemble method. Overall, LS risk was highest in agricultural areas with high groundwater drawdown in the flat lowlands on quaternary sediments (Qcf). Groundwater extraction rates should be monitored and potentially limited in regions of severe or high LS susceptibility. This investigation details a novel methodology that can help environmental planners and policy makers to mitigate LS to help achieve sustainability.
The morphometric characteristics of the Kalvārī basin were analyzed to prioritize sub-basins base... more The morphometric characteristics of the Kalvārī basin were analyzed to prioritize sub-basins based on their susceptibility to erosion by water using a remote sensing-based data and a GIS. The morphometric parameters (MPs)—linear, relief, and shape—of the drainage network were calculated using data from the Advanced Land-observing Satellite (ALOS) phased-array L-type synthetic-aperture radar (PALSAR) digital elevation model (DEM) with a spatial resolution of 12.5 m. Interferometric synthetic aperture radar (InSAR) was used to generate the DEM. These parameters revealed the network’s texture, morpho-tectonics, geometry, and relief characteristics. A complex proportional assessment of alternatives (COPRAS)-analytical hierarchy process (AHP) novel-ensemble multiple-criteria decision-making (MCDM) model was used to rank sub-basins and to identify the major MPs that significantly influence erosion landforms of the Kalvārī drainage basin. The results show that in evolutionary terms this is...
Abstract A GIS-based hybrid approach for gully erosion susceptibility mapping (GESM) in the Biarj... more Abstract A GIS-based hybrid approach for gully erosion susceptibility mapping (GESM) in the Biarjamand watershed in Iran is presented. A database comprised of 15 geo-environmental factors (GEFs) was compiled and used to predict the spatial distribution of 358 gully locations; 70% (251) of which were extracted for training and 30% (107) for validation. A Dempster-Shafer (DS) statistical model was employed to map susceptibility. Next, the results of four kernels (binary logistic, reg logistic, binary logitraw, and reg linear) of a boosted regression tree (BRT) model were combined to increase the efficiency and accuracy of the mapping. Area under receiver operating characteristics (AUROC), true skill statistic (TSS) and efficiency (E) metrics were used to rank the five validated models. The results show that elevation and distance to road play crucial roles in gullying. Integrating BRT and DS enhanced prediction accuracy. Among the four BRT kernels, binary logistic performed best (AUROC of 0.886, TSS of 0.854 and E equal to 0.880). The worst results were produced by the individual DS model (AUROC = 0.849, TSS = 0.774 and E = 0.834). The hybrid binary logistic-BRT and DS map categorized 14.50% of the study area as having very-low susceptibility, 16.99% low susceptibility, 22.77% moderate susceptibility, 24.12% high susceptibility, and 21.59% very-high susceptibility.
Abstract Dust storms are believed to play an essential role in many climatological, geochemical, ... more Abstract Dust storms are believed to play an essential role in many climatological, geochemical, and environmental processes. This atmospheric phenomenon can have a significant negative impact on public health and significantly disturb natural ecosystems. Identifying dust-source areas is thus a fundamental task to control the effects of this hazard. This study is the first attempt to identify dust source areas using hybridized machine-learning algorithms. Each hybridized model, designed as an intelligent system, consists of an adaptive neuro-fuzzy inference system (ANFIS), integrated with a combination of metaheuristic optimization algorithms: the bat algorithm (BA), cultural algorithm (CA), and differential evolution (DE). The data acquired from two key sources – the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue and the Ozone Monitoring Instrument (OMI) – are incorporated into the hybridized model, along with relevant data from field surveys and dust samples. Goodness-of-fit analyses are performed to evaluate the predictive capability of the hybridized models using different statistical criteria, including the true skill statistic (TSS) and the area under the receiver operating characteristic curve (AUC). The results demonstrate that the hybridized ANFIS-DE model (with AUC = 84.1%, TSS = 0.73) outperforms the other comparative hybridized models tailored for dust-storm prediction. The results provide evidence that the hybridized ANFIS-DE model should be explored as a promising, cost-effective method for efficiently identifying the dust-source areas, with benefits for both public health and natural environments where excessive dust presents significant challenges.
To more effectively prevent and manage the scourge of gully erosion in arid and semi-arid regions... more To more effectively prevent and manage the scourge of gully erosion in arid and semi-arid regions, we present a novel-ensemble intelligence approach—bagging-based alternating decision-tree classifier (bagging-ADTree)—and use it to model a landscape’s susceptibility to gully erosion based on 18 gully-erosion conditioning factors. The model’s goodness-of-fit and prediction performance are compared to three other machine learning algorithms (single alternating decision tree, rotational-forest-based alternating decision tree (RF-ADTree), and benchmark logistic regression). To achieve this, a gully-erosion inventory was created for the study area, the Chah Mousi watershed, Iran by combining archival records containing reports of gully erosion, remotely sensed data from Google Earth, and geolocated sites of gully head-cuts gathered in a field survey. A total of 119 gully head-cuts were identified and mapped. To train the models’ analysis and prediction capabilities, 83 head-cuts (70% of t...
Although snow avalanches are among the most destructive natural disasters, and result in losses o... more Although snow avalanches are among the most destructive natural disasters, and result in losses of life and economic damages in mountainous regions, far too little attention has been paid to the prediction of the snow avalanche hazard using advanced machine learning (ML) models. In this study, the applicability and efficiency of four ML models: support vector machine (SVM), random forest (RF), naïve Bayes (NB) and generalized additive model (GAM), for snow avalanche hazard mapping, were evaluated. Fourteen geomorphometric, topographic and hydrologic factors were selected as predictor variables in the modeling. This study was conducted in the Darvan and Zarrinehroud watersheds of Iran. The goodness-of-fit and predictive performance of the models was evaluated using two statistical measures: the area under the receiver operating characteristic curve (AUROC) and the true skill statistic (TSS). Finally, an ensemble model was developed based upon the results of the individual models. Res...
Widespread detrimental and long-lasting droughts are having catastrophic impacts around the globe... more Widespread detrimental and long-lasting droughts are having catastrophic impacts around the globe. Researchers, organizations, and policy makers need to work together to obtain precise information, enabling timely and accurate decision making to mitigate drought impacts. In this study, a spatial modeling approach based on an adaptive neuro-fuzzy inference system (ANFIS) and several metaheuristic optimizations (ANFIS-BA, ANFIS-GA, ANFIS-ICA, ANFIS-PSO) was developed to predict the spatial occurrence of drought in a region in southeastern Queensland, Australia. In this approach, data describing the distribution of eight drought-contributing factors were prepared for input into the models to serve as independent variables. Relative departures of rainfall (RDR) and relative departures of soil moisture (RDSM) were analyzed to identify locations where drought conditions have occurred. The set of locations in the study area identified as having experienced drought conditions was randomly divided into two groups, 70% were used for training and 30% for validation. The models employed these data to generate maps that predict the locations that would be expected to experience drought. The prediction accuracy of the model-produced drought maps was scrutinized with two evaluation metrics: area under the receiver operating characteristic curve (AUC) and root mean square error (RMSE). The results demonstrate that the hybridized models (ANFIS-BA (AUCmean = 83.7%, RMSEmean = 0.236), ANFIS-GA (AUCmean = 81.62%, RMSEmean = 0.247), ANFIS-ICA (AUCmean = 82.12%, RMSEmean = 0.247), and ANFIS-PSO (AUCmean = 81.42%, RMSEmean = 0.255)) yield better predictive performance than the standalone ANFIS model (AUCmean = 71.8%, RMSEmean = 0.344). Furthermore, sensitivity analyses indicated that plant-available water capacity, the percentage of soil comprised of sand, and mean annual precipitation were the most important predictors of drought hazard. The versatility of the new approach for spatial drought modeling and the capacity of ANFIS model hybridization to improve model performance suggests great potential to assist decision makers in their formulations of drought risk, recovery, and response management, and in the development of contingency plans.
Abstract Finding a digital elevation model (DEM) of suitable spatial resolution is vital to inves... more Abstract Finding a digital elevation model (DEM) of suitable spatial resolution is vital to investigate piping erosion using aerial remote-sensing platforms like unmanned aerial vehicles (UAV). Previous studies have implied that the best spatial resolution is a DEM with the most detail. This study evaluates piping-affected areas with five DEMs (1, 5, 10, 20, and 30 m resolutions) with three trained machine-learning methods: support vector machine (SVM), maximum entropy (ME), and boosted regression tree (BRT). This method enables the identification of the specific impacts caused by changing pixel resolution to guide the selection of the most effective DEM. This study employs piping morphometry data to predict the locations of completely collapsed pipes. The performance of the methods for mapping of pipes was assessed against a piping inventory map. The results demonstrate that the finest resolution DEM is not always the most useful. Though 1 m-resolution DEMs show the most detail, the best performance was the 5 m-resolution DEM when tested for all three mapping models. The 5 m-resolution DEM-SVM combination was the best predictor of known piping sites (AUC = 81.0%). The 5-m DEM-ME was second most effective model (AUC = 75.8%). And 5-m DEM-BRT was third (AUC = 72.9%). Applying more DEM derivatives may increase confidence in the selection of the most appropriate resolution.
Natural Hazards - Risk, Exposure, Response, and Resilience demonstrates advanced techniques to me... more Natural Hazards - Risk, Exposure, Response, and Resilience demonstrates advanced techniques to measure risks, exposures, responses, and solutions to hazards in an array of communities. Eleven original research reports by international scholars on hazard assessment and management are organized into four sections: studies assessing risk using in-depth modeling and technological detection to provide insight into problems associated with earthquakes, torrential rains, and nuclear power plant safety; studies revealing the spatial distributions of exposure and impacts from an assortment of hazards; studies examining human response to increased awareness of the patterns of hazard; and a study demonstrating assessment of resilience of sociotechnological systems to natural hazards. This volume contributes new conceptual and practical commentaries to assess, mitigate, and plan for disasters
Human perceptions and concerns about the implications of climate change have a profound importanc... more Human perceptions and concerns about the implications of climate change have a profound importance for adaptation to and mitigation of the impacts of climate change at various scales and in different economic and social sectors in a given geographical landscape. Although studies on public perceptions of climate change are increasing globally, they are still scant pertaining to the Himalayan region of Nepal. In order to fill this gap, this study aimed to understand individual and community perceptions of climate change in the Lower Mustang region of Nepal, where apple farming and agricultural activities have been significantly impacted by climate change over the last 30–40 years. The data collection for the study included a cross-sectional survey of 200 farmers with a structured questionnaire, seven focus group discussions, and 26 key informant interviews, which were administered in four rural municipalities: Kagbeni, Jomsom, Marpha, and Tukuche. Results of the study indicated that a...
Growing demand for water, as a consequence of population growth, farmland irrigation, and industr... more Growing demand for water, as a consequence of population growth, farmland irrigation, and industrial expansion, results in groundwater resources exploitation. This, in combination with droughts induced by climate change, has caused a sharp drop in groundwater levels throughout arid and semiarid countries. In Iran, all these factors are resulting in alarming water scarcity. Appropriate management and control of existing water resources can overcome water shortages, with healthy and sustainable management of groundwater as one of the most efficient tools. Artificial recharge of aquifers can be used to replenish water supplies and restore the water resources in Iran and other semiarid and arid countries, but selection of the right location for runoff collection is essential to achieve success. Precipitation, slope, geology, lineament density, drainage density, aquifer water quality, groundwater level, vegetation, and land use were selected in this study as key factors in locating suitable sites for artificial recharge. The weight of each, in terms of importance and impact on aquifer recharge, was determined using remote sensing techniques to prepare layers and analytic hierarchy process (AHP) and Boolean logic to identify the optimal weight for each factor. Geographic information system (GIS) was used for modeling, applying the weight of each criterion, and producing a final map. The results showed better performance of AHP than Boolean logic. For artificial recharge, 9.9% of the total study area (Mahdishahr in northern Iran) was found to be in a very good position and 22.6% in a good position. On filtering the privacy layer of fountains and aqueducts, the very good and good area declined to 8.4% and 14.7% of the total area, respectively, and mainly comprised alluvial valleys and coarse alluvial sediments with low slope and drainage density.
Groundwater depletion is an important problem driven by population growth, land use and land cove... more Groundwater depletion is an important problem driven by population growth, land use and land cover (LULC) change, climate change, and other factors. Groundwater depletion generates water stress and encourages unstainable resource use. The aim of this study is to determine how population growth, LULC change, and climate change relate to groundwater depletion in the Alto Atoyac sub-basin, Oaxaca, Mexico. Twenty-five years of dry season water table data from 1984 to 2009 are analyzed to examine annual groundwater depletion. Kriging is used to interpolate the region’s groundwater levels in a geographic information system (GIS) from mapped point measurements. An analysis of remotely sensed data revealed patterns of LULC change during a 34-year (1986–2018) period, using a supervised, machine-learning classification algorithm to calculate the changes in LULC. This analysis is shown to have an 85% accuracy. A global circulation model (GFDL-CM3) and the RCP4.5 and RCP8.5 scenarios were used ...
River water quality assessment is one of the most important tasks to enhance water resources mana... more River water quality assessment is one of the most important tasks to enhance water resources management plans. A water quality index (WQI) considers several water quality variables simultaneously. Traditionally WQI calculations consume time and are often fraught with errors during derivations of sub-indices. In this study, 4 standalone (random forest (RF), M5P, random tree (RT), and reduced error pruning tree (REPT)) and 12 hybrid data-mining algorithms (combinations of standalones with bagging (BA), CV parameter selection (CVPS) and randomizable filtered classification (RFC)) were used to create Iran WQI (IRWQIsc) predictions. Six years (2012 to 2018) of monthly data from two water quality monitoring stations within the Talar catchment were compiled. Using Pearson correlation coefficients, 10 different input combinations were constructed. The data were divided into two groups (ratio 70:30) for model building (training dataset) and model validation (testing dataset) using a 10-fold cross-validation technique. The models were evaluated using several statistical and visual evaluation metrics. Result show that fecal coliform (FC) and total solids (TS) had the greatest and least effect on the prediction of IRWQIsc. The best input combinations varied among the algorithms; generally variables with very low correlations displayed weaker performance. Hybrid algorithms improved the prediction power of several of the standalone models, but not all. Hybrid BA-RT outperformed the other models (R2 = 0.941, RMSE = 2.71, MAE = 1.87, NSE = 0.941, PBIAS = 0.500). PBIAS indicated that all algorithms, with the exceptions of RT, BA-RT and CVPS-REPT, overestimated WQI values.
Land subsidence (LS) is a significant problem that can cause loss of life, damage property, and d... more Land subsidence (LS) is a significant problem that can cause loss of life, damage property, and disrupt local economies. The Semnan Plain is an important part of Iran, where LS is a major problem for sustainable development and management. The plain represents the changes occurring in 40% of the country. We introduce a novel-ensemble intelligence approach (called ANN-bagging) that uses bagging as a meta- or ensemble-classifier of an artificial neural network (ANN) to predict LS spatially on the Semnan Plain in Semnan Province, Iran. The ensemble model's goodness-of-fit (to training data) and prediction accuracy (of the validation data) are compared to benchmarks set by ANN-bagging. A total of 96 locations of LS and 12 LS conditioning factors (LSCFs) were collected. Each feature in the LS inventory map (LSIM) was randomly assigned to one of four groups or folds, each comprising 25% of cases. The novel ensemble model was trained using 75% (3 folds) and validated with the remaining 25% (1 fold) in a four-fold cross-validation (CV) system, which is used to control for the effects of the random selection of the training and validation datasets. LSCFs for LS prediction were selected using the information-gain ratio and multi-collinearity test methods. Factor significance was evaluated using a random forest (RF) model. Groundwater drawdown, land use and land cover, elevation, and lithology were the most important LSCFs. Using the k-fold CV approaches, twelve LS susceptibility maps (LSSMs) were prepared as each fold employed all three models (ANN-bagging, ANN, and bagging). The LS susceptibility mapping showed that between 5.7% and 12.6% of the plain had very high LS susceptibility. All three models produced LS susceptibility maps with acceptable prediction accuracies and goodness-of-fits, but the best maps were produced by the ANN-bagging ensemble method. Overall, LS risk was highest in agricultural areas with high groundwater drawdown in the flat lowlands on quaternary sediments (Qcf). Groundwater extraction rates should be monitored and potentially limited in regions of severe or high LS susceptibility. This investigation details a novel methodology that can help environmental planners and policy makers to mitigate LS to help achieve sustainability.
The morphometric characteristics of the Kalvārī basin were analyzed to prioritize sub-basins base... more The morphometric characteristics of the Kalvārī basin were analyzed to prioritize sub-basins based on their susceptibility to erosion by water using a remote sensing-based data and a GIS. The morphometric parameters (MPs)—linear, relief, and shape—of the drainage network were calculated using data from the Advanced Land-observing Satellite (ALOS) phased-array L-type synthetic-aperture radar (PALSAR) digital elevation model (DEM) with a spatial resolution of 12.5 m. Interferometric synthetic aperture radar (InSAR) was used to generate the DEM. These parameters revealed the network’s texture, morpho-tectonics, geometry, and relief characteristics. A complex proportional assessment of alternatives (COPRAS)-analytical hierarchy process (AHP) novel-ensemble multiple-criteria decision-making (MCDM) model was used to rank sub-basins and to identify the major MPs that significantly influence erosion landforms of the Kalvārī drainage basin. The results show that in evolutionary terms this is...
Abstract A GIS-based hybrid approach for gully erosion susceptibility mapping (GESM) in the Biarj... more Abstract A GIS-based hybrid approach for gully erosion susceptibility mapping (GESM) in the Biarjamand watershed in Iran is presented. A database comprised of 15 geo-environmental factors (GEFs) was compiled and used to predict the spatial distribution of 358 gully locations; 70% (251) of which were extracted for training and 30% (107) for validation. A Dempster-Shafer (DS) statistical model was employed to map susceptibility. Next, the results of four kernels (binary logistic, reg logistic, binary logitraw, and reg linear) of a boosted regression tree (BRT) model were combined to increase the efficiency and accuracy of the mapping. Area under receiver operating characteristics (AUROC), true skill statistic (TSS) and efficiency (E) metrics were used to rank the five validated models. The results show that elevation and distance to road play crucial roles in gullying. Integrating BRT and DS enhanced prediction accuracy. Among the four BRT kernels, binary logistic performed best (AUROC of 0.886, TSS of 0.854 and E equal to 0.880). The worst results were produced by the individual DS model (AUROC = 0.849, TSS = 0.774 and E = 0.834). The hybrid binary logistic-BRT and DS map categorized 14.50% of the study area as having very-low susceptibility, 16.99% low susceptibility, 22.77% moderate susceptibility, 24.12% high susceptibility, and 21.59% very-high susceptibility.
Abstract Dust storms are believed to play an essential role in many climatological, geochemical, ... more Abstract Dust storms are believed to play an essential role in many climatological, geochemical, and environmental processes. This atmospheric phenomenon can have a significant negative impact on public health and significantly disturb natural ecosystems. Identifying dust-source areas is thus a fundamental task to control the effects of this hazard. This study is the first attempt to identify dust source areas using hybridized machine-learning algorithms. Each hybridized model, designed as an intelligent system, consists of an adaptive neuro-fuzzy inference system (ANFIS), integrated with a combination of metaheuristic optimization algorithms: the bat algorithm (BA), cultural algorithm (CA), and differential evolution (DE). The data acquired from two key sources – the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue and the Ozone Monitoring Instrument (OMI) – are incorporated into the hybridized model, along with relevant data from field surveys and dust samples. Goodness-of-fit analyses are performed to evaluate the predictive capability of the hybridized models using different statistical criteria, including the true skill statistic (TSS) and the area under the receiver operating characteristic curve (AUC). The results demonstrate that the hybridized ANFIS-DE model (with AUC = 84.1%, TSS = 0.73) outperforms the other comparative hybridized models tailored for dust-storm prediction. The results provide evidence that the hybridized ANFIS-DE model should be explored as a promising, cost-effective method for efficiently identifying the dust-source areas, with benefits for both public health and natural environments where excessive dust presents significant challenges.
To more effectively prevent and manage the scourge of gully erosion in arid and semi-arid regions... more To more effectively prevent and manage the scourge of gully erosion in arid and semi-arid regions, we present a novel-ensemble intelligence approach—bagging-based alternating decision-tree classifier (bagging-ADTree)—and use it to model a landscape’s susceptibility to gully erosion based on 18 gully-erosion conditioning factors. The model’s goodness-of-fit and prediction performance are compared to three other machine learning algorithms (single alternating decision tree, rotational-forest-based alternating decision tree (RF-ADTree), and benchmark logistic regression). To achieve this, a gully-erosion inventory was created for the study area, the Chah Mousi watershed, Iran by combining archival records containing reports of gully erosion, remotely sensed data from Google Earth, and geolocated sites of gully head-cuts gathered in a field survey. A total of 119 gully head-cuts were identified and mapped. To train the models’ analysis and prediction capabilities, 83 head-cuts (70% of t...
Although snow avalanches are among the most destructive natural disasters, and result in losses o... more Although snow avalanches are among the most destructive natural disasters, and result in losses of life and economic damages in mountainous regions, far too little attention has been paid to the prediction of the snow avalanche hazard using advanced machine learning (ML) models. In this study, the applicability and efficiency of four ML models: support vector machine (SVM), random forest (RF), naïve Bayes (NB) and generalized additive model (GAM), for snow avalanche hazard mapping, were evaluated. Fourteen geomorphometric, topographic and hydrologic factors were selected as predictor variables in the modeling. This study was conducted in the Darvan and Zarrinehroud watersheds of Iran. The goodness-of-fit and predictive performance of the models was evaluated using two statistical measures: the area under the receiver operating characteristic curve (AUROC) and the true skill statistic (TSS). Finally, an ensemble model was developed based upon the results of the individual models. Res...
Widespread detrimental and long-lasting droughts are having catastrophic impacts around the globe... more Widespread detrimental and long-lasting droughts are having catastrophic impacts around the globe. Researchers, organizations, and policy makers need to work together to obtain precise information, enabling timely and accurate decision making to mitigate drought impacts. In this study, a spatial modeling approach based on an adaptive neuro-fuzzy inference system (ANFIS) and several metaheuristic optimizations (ANFIS-BA, ANFIS-GA, ANFIS-ICA, ANFIS-PSO) was developed to predict the spatial occurrence of drought in a region in southeastern Queensland, Australia. In this approach, data describing the distribution of eight drought-contributing factors were prepared for input into the models to serve as independent variables. Relative departures of rainfall (RDR) and relative departures of soil moisture (RDSM) were analyzed to identify locations where drought conditions have occurred. The set of locations in the study area identified as having experienced drought conditions was randomly divided into two groups, 70% were used for training and 30% for validation. The models employed these data to generate maps that predict the locations that would be expected to experience drought. The prediction accuracy of the model-produced drought maps was scrutinized with two evaluation metrics: area under the receiver operating characteristic curve (AUC) and root mean square error (RMSE). The results demonstrate that the hybridized models (ANFIS-BA (AUCmean = 83.7%, RMSEmean = 0.236), ANFIS-GA (AUCmean = 81.62%, RMSEmean = 0.247), ANFIS-ICA (AUCmean = 82.12%, RMSEmean = 0.247), and ANFIS-PSO (AUCmean = 81.42%, RMSEmean = 0.255)) yield better predictive performance than the standalone ANFIS model (AUCmean = 71.8%, RMSEmean = 0.344). Furthermore, sensitivity analyses indicated that plant-available water capacity, the percentage of soil comprised of sand, and mean annual precipitation were the most important predictors of drought hazard. The versatility of the new approach for spatial drought modeling and the capacity of ANFIS model hybridization to improve model performance suggests great potential to assist decision makers in their formulations of drought risk, recovery, and response management, and in the development of contingency plans.
Abstract Finding a digital elevation model (DEM) of suitable spatial resolution is vital to inves... more Abstract Finding a digital elevation model (DEM) of suitable spatial resolution is vital to investigate piping erosion using aerial remote-sensing platforms like unmanned aerial vehicles (UAV). Previous studies have implied that the best spatial resolution is a DEM with the most detail. This study evaluates piping-affected areas with five DEMs (1, 5, 10, 20, and 30 m resolutions) with three trained machine-learning methods: support vector machine (SVM), maximum entropy (ME), and boosted regression tree (BRT). This method enables the identification of the specific impacts caused by changing pixel resolution to guide the selection of the most effective DEM. This study employs piping morphometry data to predict the locations of completely collapsed pipes. The performance of the methods for mapping of pipes was assessed against a piping inventory map. The results demonstrate that the finest resolution DEM is not always the most useful. Though 1 m-resolution DEMs show the most detail, the best performance was the 5 m-resolution DEM when tested for all three mapping models. The 5 m-resolution DEM-SVM combination was the best predictor of known piping sites (AUC = 81.0%). The 5-m DEM-ME was second most effective model (AUC = 75.8%). And 5-m DEM-BRT was third (AUC = 72.9%). Applying more DEM derivatives may increase confidence in the selection of the most appropriate resolution.
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Papers by John Tiefenbacher