The natural streamflow of the River is encouraged to forecast through multiple methods. The impar... more The natural streamflow of the River is encouraged to forecast through multiple methods. The impartiality of this study is the comparison of the forecast accuracy rates of the time-series (TS) hybrid model with the conventional model. The behavior of the natural monthly statistical chaotic streamflow to use in the forecasting models has been compiled by projecting two distinguished rivers, the Indus and Chenab of Pakistan. Therefore, this article is based on the monthly streamflow forecast analysis that has been reported using the group method of data handling with wavelet decomposition (WGMDH) as a new forecasting attribute. Discrete wavelets decompose the perceived data into sub-series and forecast hydrological variables; these fittingly have been endorsed as inputs in the hybrid model. The forecast efficiency and estimations of the hybrid model are measured by the appropriate statistical techniques such as mean absolute error (RME), root mean square error (RMSE), and correlation c...
International Journal of Environmental Science and Technology, 2021
Over the years, many organizations across the globe have conducted various studies pertaining to ... more Over the years, many organizations across the globe have conducted various studies pertaining to air pollution and its ill effects. The results of these studies substantially conclude that a plethora of people succumbs to the adversities caused by the ever-increasing air pollutants. In this investigation, M5P, random forest (RF)- and Gaussian process (GP)-based approaches are used to predict the tropospheric ozone for Amritsar, Punjab state of India, metropolitan area. The models proposed were based on ten input parameters viz. particulate matter PM2.5, particulate matter PM10, sulphur dioxide (SO2), nitrogen dioxide (NO2), nitric oxide (NO), ammonia (NH3), temperature (T), solar radiation (SR), wind direction (WD) and wind speed (WS), while the tropospheric ozone (O3) was an output parameter. Three most popular statistical parameters such as correlation coefficient (CC), mean absolute error (MAE) and root mean square error (RMSE) were used for the assessment of the developed models. In comparison, it was found that better results were achieved with random forest-based model with CC value as 0.8850, MAE value as 0.0593 and RMSE value as 0.0772 for testing stage. The suggested models are expected to save cost of instrument, cost of labour work, time and contribute to greater accuracy. A result of sensitivity investigation concludes that the solar radiation is the most influencing parameter in estimating the actual values of O3 based on the current data set.
All existing methods regarding time series forecasting have always been challenged by the continu... more All existing methods regarding time series forecasting have always been challenged by the continuous climatic change taking place in the world. These climatic changes influence many unpredictable indefinite factors. This alarming situation requires a robust forecasting method that could efficiently work with incomplete and multivariate data. Most of the existing methods tend to trap into local minimum or encounter over fitting problems that mostly lead to an inappropriate outcome. The complexity of data regarding time series forecasting does not allow any one single method to yield results suitable in all situations as claimed by most researchers. To deal with the problem, a technique that uses hybrid models has also been devised and tested. The applied hybrid methods did bring some improvement compared to the individual model performance. However, most of these available hybrid models exploit univariate data that requires huge historical data to achieve precise forecasting results....
This study aims to propose a hydrological model for estimating the future value for monthly river... more This study aims to propose a hydrological model for estimating the future value for monthly river flow. The proposed model was constructed by combining three components: i.e. Discrete Wavelet Transform (DWT), Principal Component Analysis (PCA) and Least Square Support Vector Machine (LSSVM). The first two components, i.e. the wavelets and the PCA, are meant for preparing input data. Wavelets were employed to obtain a certain level of data decomposition, and in this case, a three level decomposition was employed. The output from the wavelets was given to PCA. This component simply picks up the important components from the given data, i.e. it addresses the issues relating to the dimensionality of the data. For approximating the desired value, LSSVM was employed for training, using the data derived from Wavelets and PCA models. For testing stability and reliability of the proposed model monthly data from two Pakistani rivers was collected. The reliability was measured by employing wel...
Particulate matter has a detrimental consequence on the health of living organisms throughout the... more Particulate matter has a detrimental consequence on the health of living organisms throughout the world and predicting their concentration is very imperative to assess their impact on human health. Faridabad is the most populated and largest city of Haryana, India, and the current study was designed to foresee the PM2.5 content by different modeling techniques: (1) support vector machine (SVM), (ii) random forest (RF), (iii) artificial neural network (ANN), (iv) M5P model, and (v) Gaussian process regression (GP). Collected data (659 observations) from May 2015 to May 2018 were used to develop the models. Parameters such as temperature (T), ground-level ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), nitric oxide (NO), NOx, wind speed (WS), wind direction (WD), relative humidity (RH), bar pressure (BP), and solar radiation (SR) are used as input parameters for prediction of PM2.5. The results of all the models suggested that RF model with testing correlation coefficient (C...
The aim of the present study was to explore the correlation between the land-use/land cover chang... more The aim of the present study was to explore the correlation between the land-use/land cover change and the flash-flood potential changes in Zăbala catchment (Romania) between 1989 and 2019. In this regard, the efficiency of GIS, remote sensing and machine learning techniques in detecting spatial patterns of the relationship between the two variables was tested. The paper elaborated upon an answer to the increase in flash flooding frequency across the study area and across the Remote Sens. 2020, 12, 1422; doi:10.3390/rs12091422 www.mdpi.com/journal/remotesensing Remote Sens. 2020, 12, 1422 2 of 30 earth due to the occurred land-use/land-cover changes, as well as due to the present climate change, which determined the multiplication of extreme meteorological phenomena. In order to reach the above-mentioned purpose, two land-uses/land-covers (for 1989 and 2019) were obtained using Landsat image processing and were included in a relative evolution indicator (total relative difference-sy...
Agrees that the evidence of a vast array of research concerning teamwork is conclusive: teams are... more Agrees that the evidence of a vast array of research concerning teamwork is conclusive: teams are capable of outstanding performance and are the primary unit of performance for increasing numbers of organisations. Nevertheless, high performance teams (HPTs) are a rarity. Presents the results of collaborative research aimed at determining the factors affecting successful implementation of HPTs. The factors have been
The natural streamflow of the River is encouraged to forecast through multiple methods. The impar... more The natural streamflow of the River is encouraged to forecast through multiple methods. The impartiality of this study is the comparison of the forecast accuracy rates of the time-series (TS) hybrid model with the conventional model. The behavior of the natural monthly statistical chaotic streamflow to use in the forecasting models has been compiled by projecting two distinguished rivers, the Indus and Chenab of Pakistan. Therefore, this article is based on the monthly streamflow forecast analysis that has been reported using the group method of data handling with wavelet decomposition (WGMDH) as a new forecasting attribute. Discrete wavelets decompose the perceived data into sub-series and forecast hydrological variables; these fittingly have been endorsed as inputs in the hybrid model. The forecast efficiency and estimations of the hybrid model are measured by the appropriate statistical techniques such as mean absolute error (RME), root mean square error (RMSE), and correlation c...
International Journal of Environmental Science and Technology, 2021
Over the years, many organizations across the globe have conducted various studies pertaining to ... more Over the years, many organizations across the globe have conducted various studies pertaining to air pollution and its ill effects. The results of these studies substantially conclude that a plethora of people succumbs to the adversities caused by the ever-increasing air pollutants. In this investigation, M5P, random forest (RF)- and Gaussian process (GP)-based approaches are used to predict the tropospheric ozone for Amritsar, Punjab state of India, metropolitan area. The models proposed were based on ten input parameters viz. particulate matter PM2.5, particulate matter PM10, sulphur dioxide (SO2), nitrogen dioxide (NO2), nitric oxide (NO), ammonia (NH3), temperature (T), solar radiation (SR), wind direction (WD) and wind speed (WS), while the tropospheric ozone (O3) was an output parameter. Three most popular statistical parameters such as correlation coefficient (CC), mean absolute error (MAE) and root mean square error (RMSE) were used for the assessment of the developed models. In comparison, it was found that better results were achieved with random forest-based model with CC value as 0.8850, MAE value as 0.0593 and RMSE value as 0.0772 for testing stage. The suggested models are expected to save cost of instrument, cost of labour work, time and contribute to greater accuracy. A result of sensitivity investigation concludes that the solar radiation is the most influencing parameter in estimating the actual values of O3 based on the current data set.
All existing methods regarding time series forecasting have always been challenged by the continu... more All existing methods regarding time series forecasting have always been challenged by the continuous climatic change taking place in the world. These climatic changes influence many unpredictable indefinite factors. This alarming situation requires a robust forecasting method that could efficiently work with incomplete and multivariate data. Most of the existing methods tend to trap into local minimum or encounter over fitting problems that mostly lead to an inappropriate outcome. The complexity of data regarding time series forecasting does not allow any one single method to yield results suitable in all situations as claimed by most researchers. To deal with the problem, a technique that uses hybrid models has also been devised and tested. The applied hybrid methods did bring some improvement compared to the individual model performance. However, most of these available hybrid models exploit univariate data that requires huge historical data to achieve precise forecasting results....
This study aims to propose a hydrological model for estimating the future value for monthly river... more This study aims to propose a hydrological model for estimating the future value for monthly river flow. The proposed model was constructed by combining three components: i.e. Discrete Wavelet Transform (DWT), Principal Component Analysis (PCA) and Least Square Support Vector Machine (LSSVM). The first two components, i.e. the wavelets and the PCA, are meant for preparing input data. Wavelets were employed to obtain a certain level of data decomposition, and in this case, a three level decomposition was employed. The output from the wavelets was given to PCA. This component simply picks up the important components from the given data, i.e. it addresses the issues relating to the dimensionality of the data. For approximating the desired value, LSSVM was employed for training, using the data derived from Wavelets and PCA models. For testing stability and reliability of the proposed model monthly data from two Pakistani rivers was collected. The reliability was measured by employing wel...
Particulate matter has a detrimental consequence on the health of living organisms throughout the... more Particulate matter has a detrimental consequence on the health of living organisms throughout the world and predicting their concentration is very imperative to assess their impact on human health. Faridabad is the most populated and largest city of Haryana, India, and the current study was designed to foresee the PM2.5 content by different modeling techniques: (1) support vector machine (SVM), (ii) random forest (RF), (iii) artificial neural network (ANN), (iv) M5P model, and (v) Gaussian process regression (GP). Collected data (659 observations) from May 2015 to May 2018 were used to develop the models. Parameters such as temperature (T), ground-level ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), nitric oxide (NO), NOx, wind speed (WS), wind direction (WD), relative humidity (RH), bar pressure (BP), and solar radiation (SR) are used as input parameters for prediction of PM2.5. The results of all the models suggested that RF model with testing correlation coefficient (C...
The aim of the present study was to explore the correlation between the land-use/land cover chang... more The aim of the present study was to explore the correlation between the land-use/land cover change and the flash-flood potential changes in Zăbala catchment (Romania) between 1989 and 2019. In this regard, the efficiency of GIS, remote sensing and machine learning techniques in detecting spatial patterns of the relationship between the two variables was tested. The paper elaborated upon an answer to the increase in flash flooding frequency across the study area and across the Remote Sens. 2020, 12, 1422; doi:10.3390/rs12091422 www.mdpi.com/journal/remotesensing Remote Sens. 2020, 12, 1422 2 of 30 earth due to the occurred land-use/land-cover changes, as well as due to the present climate change, which determined the multiplication of extreme meteorological phenomena. In order to reach the above-mentioned purpose, two land-uses/land-covers (for 1989 and 2019) were obtained using Landsat image processing and were included in a relative evolution indicator (total relative difference-sy...
Agrees that the evidence of a vast array of research concerning teamwork is conclusive: teams are... more Agrees that the evidence of a vast array of research concerning teamwork is conclusive: teams are capable of outstanding performance and are the primary unit of performance for increasing numbers of organisations. Nevertheless, high performance teams (HPTs) are a rarity. Presents the results of collaborative research aimed at determining the factors affecting successful implementation of HPTs. The factors have been
Uploads
Papers by Siraj Pandhiani