Prediction the Groundwater Level of Bastam Plain (Iran) by Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS)
作者: Samad EmamgholizadehKhadije MoslemiGholamhosein Karami
作者单位: 1Shahrood University
刊名: Water Resources Management, 2014, Vol.28 (15), pp.5433-5446
来源数据库: Springer Journal
DOI: 10.1007/s11269-014-0810-0
关键词: Groundwater levelBastam plainAdaptive neuro-fuzzy inference systemArtificial neural network
英文摘要: Abstract(#br)Prediction of the groundwater level (GWL) fluctuations is very important in the water resource management. This study investigates the potential of two intelligence models namely, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) in the forecasting of the groundwater level of Bastam Plain in Iran. For this purpose, 9 years data-sets including hydrological and hydrogeological parameters like rainfall recharge, irrigation returned flow and also pumping rates from water wells were used as input data to predict groundwater level. The results showed that ANN and ANFIS models can predict GWL accurately. Also, it was found that the ANFIS model (with root-mean-square-error (RMSE) 0.02 m and determination coefficient (R 2 ) of 0.96) performed better...
全文获取路径: Springer  (合作)
影响因子:2.259 (2012)

  • ANN All Numeral Numbering
  • Network 网络