A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Comparison Between Machine Learning and Bivariate Statistical Models for Groundwater Recharge Zones
Due to population growth and climate change, dependence on groundwater is expected to increase. This growth has put forth a major challenge for management for sustainable groundwater storage. This study illustrates a newly introduced bivariate statistical model with an ensembled data mining approach. Certainty factors (CF), evidential belief function (EBF), frequency ratio (FR) and convolutional neural network (CNN) are four bivariant statistical models. These four models are integrated with the logistic model tree (LMT) and random forest (RF). These models are used for preparing the groundwater potential map (GPM). The receiver operating characteristic (ROC) curve and area under the curve (AUC) were utilized for calculating the accuracy of the groundwater potential maps. The sequence and values of AUC obtained from the results are as CNN-RF (0.923), CF-RF (0.914), EBF-RF (0.911), FR-RF (0.904), CF-LMT (0.893), EBF-LMT (0.872) and FR-LMT (0.817). It can be concluded that the combination of bivariate statistic models and data mining techniques advance the method’s efficiency in creating a potential mapping of groundwater.
Comparison Between Machine Learning and Bivariate Statistical Models for Groundwater Recharge Zones
Due to population growth and climate change, dependence on groundwater is expected to increase. This growth has put forth a major challenge for management for sustainable groundwater storage. This study illustrates a newly introduced bivariate statistical model with an ensembled data mining approach. Certainty factors (CF), evidential belief function (EBF), frequency ratio (FR) and convolutional neural network (CNN) are four bivariant statistical models. These four models are integrated with the logistic model tree (LMT) and random forest (RF). These models are used for preparing the groundwater potential map (GPM). The receiver operating characteristic (ROC) curve and area under the curve (AUC) were utilized for calculating the accuracy of the groundwater potential maps. The sequence and values of AUC obtained from the results are as CNN-RF (0.923), CF-RF (0.914), EBF-RF (0.911), FR-RF (0.904), CF-LMT (0.893), EBF-LMT (0.872) and FR-LMT (0.817). It can be concluded that the combination of bivariate statistic models and data mining techniques advance the method’s efficiency in creating a potential mapping of groundwater.
Comparison Between Machine Learning and Bivariate Statistical Models for Groundwater Recharge Zones
Iran J Sci Technol Trans Civ Eng
Aslam, Bilal (author) / Maqsoom, Ahsen (author) / Hassan, Usman (author) / Maqsoom, Sidra (author) / Alaloul, Wesam Salah (author) / Musarat, Muhammad Ali (author) / Shahzaib, Muhammad (author) / Irfan, Muhammad (author)
2025-02-01
24 pages
Article (Journal)
Electronic Resource
English
Comparison Between Machine Learning and Bivariate Statistical Models for Groundwater Recharge Zones
| Springer Verlag | 2025
Identification of groundwater recharge zones - a case study
| British Library Online Contents | 2004
Identification of Groundwater Potential Recharge and Recharge Zones of Tumakuru District Using GIS
| Springer Verlag | 2023