Your search found 3 records
1 Arabameri, A.; Pal, S. C.; Rezaie, F.; Nalivan, O. A.; Chowdhuri, I.; Saha, A.; Lee, S.; Moayedi, H. 2021. Modeling groundwater potential using novel GIS-based machine-learning ensemble techniques. Journal of Hydrology: Regional Studies, 36:100848. [doi: https://doi.org/10.1016/j.ejrh.2021.100848]
Groundwater potential ; Modelling ; Geographical information systems ; Machine learning ; Techniques ; Neural networks ; Remote sensing ; River basins ; Land use ; Land cover ; Landslides / Iran (Islamic Republic of) / Tabriz River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H050645)
https://www.sciencedirect.com/science/article/pii/S221458182100077X/pdfft?md5=008d5c28c1313c1b111fb09896b85615&pid=1-s2.0-S221458182100077X-main.pdf
https://vlibrary.iwmi.org/pdf/H050645.pdf
https://vlibrary.iwmi.org/pdf/H050645.pdf
(14.10 MB) (14.1 MB)
Study region: The present study has been carried out in the Tabriz River basin (5397 km2) in north-western Iran. Elevations vary from 1274 to 3678 m above sea level, and slope angles range from 0 to 150.9 %. The average annual minimum and maximum temperatures are 2 °C and 12 °C, respectively. The average annual rainfall ranges from 243 to 641 mm, and the northern and southern parts of the basin receive the highest amounts.
Study focus: In this study, we mapped the groundwater potential (GWP) with a new hybrid model combining random subspace (RS) with the multilayer perception (MLP), naïve Bayes tree (NBTree), and classification and regression tree (CART) algorithms. A total of 205 spring locations were collected by integrating field surveys with data from Iran Water Resources Management, and divided into 70:30 for training and validation. Fourteen groundwater conditioning factors (GWCFs) were used as independent model inputs. Statistics such as receiver operating characteristic (ROC) and five others were used to evaluate the performance of the models.
New hydrological insights for the region: The results show that all models performed well for GWP mapping (AUC > 0.8). The hybrid MLP-RS model achieved high validation scores (AUC = 0.935). The relative importance of GWCFs was revealed that slope, elevation, TRI and HAND are the most important predictors of groundwater presence. This study demonstrates that hybrid ensemble models can support sustainable management of groundwater resources.
Study focus: In this study, we mapped the groundwater potential (GWP) with a new hybrid model combining random subspace (RS) with the multilayer perception (MLP), naïve Bayes tree (NBTree), and classification and regression tree (CART) algorithms. A total of 205 spring locations were collected by integrating field surveys with data from Iran Water Resources Management, and divided into 70:30 for training and validation. Fourteen groundwater conditioning factors (GWCFs) were used as independent model inputs. Statistics such as receiver operating characteristic (ROC) and five others were used to evaluate the performance of the models.
New hydrological insights for the region: The results show that all models performed well for GWP mapping (AUC > 0.8). The hybrid MLP-RS model achieved high validation scores (AUC = 0.935). The relative importance of GWCFs was revealed that slope, elevation, TRI and HAND are the most important predictors of groundwater presence. This study demonstrates that hybrid ensemble models can support sustainable management of groundwater resources.
2 Mia, Md. U.; Rahman, M.; Elbeltagi, A.; Abdullah-Al-Mahbub, Md.; Sharma, G.; Islam, H. M. T.; Pal, S. C.; Costache, R.; Towfiqul Islam, A. R. Md.; Islam, Md. M.; Chen, N.; Alam, E.; Washakh, R. M. A. 2022. Sustainable flood risk assessment using deep learning-based algorithms with a blockchain technology. Geocarto International, 30p. (Online first) [doi: https://doi.org/10.1080/10106049.2022.2112982]
Flooding ; Risk assessment ; Disaster risk management ; Machine learning ; Blockchain technology ; Neural networks ; Sustainable development ; Floodplains ; Rain ; Forecasting ; Datasets ; Mapping ; Normalized difference vegetation index ; Models / Bangladesh / Brahmaputra River / Jamalpur / Gaibandha / Kurigram / Bogra
(Location: IWMI HQ Call no: e-copy only Record No: H051339)
https://www.tandfonline.com/doi/pdf/10.1080/10106049.2022.2112982
https://vlibrary.iwmi.org/pdf/H051339.pdf
https://vlibrary.iwmi.org/pdf/H051339.pdf
(5.41 MB) (5.41 MB)
The couplings of convolutional neural networks (CNN) with random forest (RF), support vector machine (SVM), long short-term memory (LSTM), and extreme gradient boosting (XGBoost) ensemble algorithms were used to construct novel ensemble computational models (CNN-LSTM, CNN-XG, CNN-SVM, and CNN-RF) for flood hazard mapping in the monsoon-dominated catchment, Bangladesh. The results revealed that geology, elevation, the normalized difference vegetation index (NDVI), and rainfall are the most significant parameters in flash floods based on the Pearson correlation technique. Statistical method such as the area under the curve (AUC) was used to evaluate model performance. The CNN-RF model could be a promising tool for precisely predicting and mapping flash floods as it is outperformed the other models (AUC = 1.0). Furthermore, to meet sustainable development goals (SDGs), a blockchain-based technology is proposed to create a decentralized flood management tool for help seekers and help providers during and post floods. The suggested tool accelerates emergency rescue operations during flood events.
3 Biswas, T.; Pal, S. C.; Chowdhuri, I.; Ruidas, D.; Saha, A.; Islam, A. R. Md. T.; Shit, M. 2023. Effects of elevated arsenic and nitrate concentrations on groundwater resources in Deltaic Region of Sundarban Ramsar Site, Indo-Bangladesh Region. Marine Pollution Bulletin, 188:114618. (Online first) [doi: https://doi.org/10.1016/j.marpolbul.2023.114618]
Groundwater ; Water resources ; Arsenic ; Nitrates ; Health hazards ; Drinking water ; Water quality ; Vulnerability ; Models / India / Bangladesh / Sundarban Ramsar Site
(Location: IWMI HQ Call no: e-copy only Record No: H051695)
(15.70 MB)
An attempt has been adopted to predict the As and NO3- concentration in groundwater (GW) in fast-growing coastal Ramsar region in eastern India. This study is focused to evaluate the As and NO3- vulnerable areas of coastal belts of the Indo-Bangladesh Ramsar site a hydro-geostrategic region of the world by using advanced ensemble ML techniques including NB-RF, NB-SVM and NB-Bagging. A total of 199 samples were collected from the entire study area for utilizing the 12 GWQ conditioning factors. The predicted results are certified that NB-Bagging the most suitable and preferable model in this current research. The vulnerability of As and NO3- concentration shows that most of the areas are highly vulnerable to As and low to moderately vulnerable to NO3. The reliable findings of this present study will help the management authorities and policymakers in taking preventive measures in reducing the vulnerability of water resources and corresponding health risks.
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