Groundwater Potential Determination on Yasouj-Sisakht area Using Random Forest and Generalized Linear Statistical Models | ||
| نشریه علمی - پژوهشی مرتع و آبخیزداری | ||
| Article 1, Volume 72, Issue 3, December 2019, Pages 609-623 PDF (1.97 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.22059/jrwm.2019.282912.1392 | ||
| Authors | ||
| Mohammad Taghi avand1; Saeed Janizadeh2; Mohsen Farzin* 3 | ||
| 1دانشجوی دکتری علوم و مهندسی آبخیزداری، دانشکده منابع طبیعی و علوم دریایی، دانشگاه تربیت مدرس، نور | ||
| 2Ph.D student of Watershed Management Engineering, Natural Resources and Marine Sciences Faculty, Tarbiat Modares Unviversity, Noor. | ||
| 3University of Yasouj | ||
| Abstract | ||
| Increasing population and agricultural development need dramatically water resources groundwater resources, therefore, are increasingly being considered, especially in arid and semi-arid regions. Aim of this research is mapping potential of groundwater resources on Yasouj-Sisakht region using data mining method Random Forest (RF) and Generalized Linear Statistical Model (GLM). For this purpose. For this purpose, information layers including slope, slope direction, slope length, aspect, topographic wetness index (TWI), distance from fault, distance from the stream, rainfall, land use, lithology, topographic position index (TPI) and stream power index (SPI) as the main factors influencing groundwater potential were identified and developed in ArcGIS and SAGAGIS software. From the distribution of 263 springs in the area, 70% (253 springs) were used as educational springs and 30% (109 springs) were used as experimental springs. The results showed that the level of underground water with low, medium, high and very high potential in the map of the random forest was 37.78, 22.22, 18.89 and 21.11%, respectively, and in the generalization linear model were 14.49, 32.04, 31.11 and 22.36%, respectively. Moreover, Sensitivity Analysis show that the factors affecting both methods are rainfall, altitude and distance from the fault factors. The accuracy of the data mining models used in this research was also evaluated using a relative performance curve (ROC). The area under curve (AUC) for both RF and GLM models is 92% and 65%, respectively. The accuracy of RF model, therefore, mapping groundwater potential in the study area is more than GLM model. | ||
| Keywords | ||
| Mapping potential; data mining; random forest; generalized linear model; Yasouj-Sisakht | ||
| References | ||
|
| ||
|
Statistics Article View: 685 PDF Download: 499 |
||