首页|Studies from National Institute of Hydrology in the Area of Machine Learning Des cribed (Prediction of Groundwater Level Changes Based On Machine Learning Techni que In Highly Groundwater Irrigated Alluvial Aquifers of South-central Punjab, I ndia)
Studies from National Institute of Hydrology in the Area of Machine Learning Des cribed (Prediction of Groundwater Level Changes Based On Machine Learning Techni que In Highly Groundwater Irrigated Alluvial Aquifers of South-central Punjab, I ndia)
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Current study results on Machine Learn ing have been published. According to news reporting out of Roorkee, India, by N ewsRx editors, research stated, "Groundwater serves as a vital resource for all living organisms. In regions extensively reliant on groundwater irrigation, hydr o -climatic factors, groundwater extraction, and the flow of surface water exhib it an indirect interdependence." Our news journalists obtained a quote from the research from the National Instit ute of Hydrology, "This study primarily aims to anticipate GWL in such highly ir rigated zones using the Machine Learning (ML) approach. To achieve this, the wid ely employed Random Forest (RF), Bagging -Reduce Error Pruning Tree (Bagging-REP Tree), and Bagging -Decision Stump Tree (Bagging-DSTree) models have been employ ed for the accurate forecasting of groundwater levels. The long-term pre -monsoo n and post -monsoon (fourteen locations) data set of South -Central Punjab state has been applied for the model calibration/training and validation/testing. Sev en statistical indices were used such as percent bias (PBIAS), root mean square error (RMSE), normalized root mean square error (nRMSE), RMSEobservation standa rd deviation ratio (RSR), mean absolute error (MAE), Nash Sutcliffe efficiency ( NSE) and correlation coefficient (CC) for the model performance analysis. The re sults revealed that the RF model outperformed in pre -monsoon (testing phase) (R MSE = 0.682, NSE = 0.958) as well as the post -monsoon (testing phase) (RMSE = 0 .150, NSE = 0.997) compared to the other two models in the station Ahmadapur and the similar trend is observed in all the stations."
RoorkeeIndiaAsiaCyborgsEmerging TechnologiesMachine LearningNational Institute of Hydrology
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