University of TehranIranian Journal of Soil and Water Research2008-479X45420141222Design of Groundwater Level Monitoring Network, Using the Model of Least Squares Support Vector Machine (LS-SVM)Design of Groundwater Level Monitoring Network, Using the Model of Least Squares Support Vector Machine (LS-SVM)3893965259110.22059/ijswr.2014.52591FAElhamRezaeiMs.C student of water resources management, University of Birjand.AbbasKhashei- SiukiAssistant Professor of Water Engineering Department., University of Birjand.AliShahidiAssistant Professor of Water Engineering Department., University of Birjand.0000-0003-0716-2144Journal Article20131001The present study presents a methodology for the design of long-term groundwater head monitoring networks to reduce spatial redundancy in which the additional wells if not sampled, the error related to groundwater level estimation would be negligible. This method is based on Support Vector Machine, and founded upon the statistical learning theory. Throughout the study, some 63 quantitative data, observation wells as well as meteorological parameters (precipitation and evaporation) of Ramhormoz plain (in a 7-year period) were employed to evaluate the performance of Least Squares Support Vector Machine model (LS-SVM) in the groundwater observation well network design concept. Different combinations of parameters affecting the ground water level were assessed using the model LS-SVM. The optimal combination of LSSVM model with RBF Kernel function carries such performance parameters as R2=0.9992, MAE=0.3405. Then, using Function Approximation Optimum, a number of 42 observation wells were pinpointed to apply the appropriate spatial monitoring in the plain of RAMHORMOZ.The present study presents a methodology for the design of long-term groundwater head monitoring networks to reduce spatial redundancy in which the additional wells if not sampled, the error related to groundwater level estimation would be negligible. This method is based on Support Vector Machine, and founded upon the statistical learning theory. Throughout the study, some 63 quantitative data, observation wells as well as meteorological parameters (precipitation and evaporation) of Ramhormoz plain (in a 7-year period) were employed to evaluate the performance of Least Squares Support Vector Machine model (LS-SVM) in the groundwater observation well network design concept. Different combinations of parameters affecting the ground water level were assessed using the model LS-SVM. The optimal combination of LSSVM model with RBF Kernel function carries such performance parameters as R2=0.9992, MAE=0.3405. Then, using Function Approximation Optimum, a number of 42 observation wells were pinpointed to apply the appropriate spatial monitoring in the plain of RAMHORMOZ.https://ijswr.ut.ac.ir/article_52591_7662c67dbe764f0e2bec6f3127cf75a9.pdf