This study intends to investigate the performance of boosted regression tree (BRT) and frequency ratio (FR) models in groundwater potential mapping. For this purpose, location of the springs was determined in the western parts of the Mashhad Plain using national reports and field surveys. In addition, thirteen groundwater conditioning factors were prepared and mapped for the modelling process. Those factor maps are: slope degree, slope aspect, altitude, plan curvature, profile curvature, slope length, topographic wetness index, distance from faults, distance from rivers, river density, fault density, land use, and lithology. Then, frequency ratio and boosted regression tree models were applied and groundwater potential maps (GPMs) were produced. In the last step, validation of the models was carried out implementing receiver operating characteristics (ROC) curve. According to the results, BRT had area under curve of ROC (AUC-ROC) of 87.2%, while it was seen that FR had AUC-ROC of 83.2% that implies acceptable operation of the models. According to the results of this study, topographic wetness index was the most important factor, followed by altitude, and distance from rivers. On the other hand, aspect, and plan curvature were seen to be the least important factors. The methodology implemented in this study could be used for other basins with similar conditions to cope with water resources problem.
Citation: Seyed Mohsen Mousavi, Ali Golkarian, Seyed Amir Naghibi, Bahareh Kalantar, Biswajeet Pradhan. GIS-based Groundwater Spring Potential Mapping Using Data Mining Boosted Regression Tree and Probabilistic Frequency Ratio Models in Iran[J]. AIMS Geosciences, 2017, 3(1): 91-115. doi: 10.3934/geosci.2017.1.91
This study intends to investigate the performance of boosted regression tree (BRT) and frequency ratio (FR) models in groundwater potential mapping. For this purpose, location of the springs was determined in the western parts of the Mashhad Plain using national reports and field surveys. In addition, thirteen groundwater conditioning factors were prepared and mapped for the modelling process. Those factor maps are: slope degree, slope aspect, altitude, plan curvature, profile curvature, slope length, topographic wetness index, distance from faults, distance from rivers, river density, fault density, land use, and lithology. Then, frequency ratio and boosted regression tree models were applied and groundwater potential maps (GPMs) were produced. In the last step, validation of the models was carried out implementing receiver operating characteristics (ROC) curve. According to the results, BRT had area under curve of ROC (AUC-ROC) of 87.2%, while it was seen that FR had AUC-ROC of 83.2% that implies acceptable operation of the models. According to the results of this study, topographic wetness index was the most important factor, followed by altitude, and distance from rivers. On the other hand, aspect, and plan curvature were seen to be the least important factors. The methodology implemented in this study could be used for other basins with similar conditions to cope with water resources problem.
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Figures(6) / Tables(5)
Seyed Mohsen Mousavi, Ali Golkarian, Seyed Amir Naghibi, Bahareh Kalantar, Biswajeet Pradhan. GIS-based Groundwater Spring Potential Mapping Using Data Mining Boosted Regression Tree and Probabilistic Frequency Ratio Models in Iran[J]. AIMS Geosciences, 2017, 3(1): 91-115. doi: 10.3934/geosci.2017.1.91
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