Department of Water Resources Research, Water Research Institute, Tehran, Iran.
Department of Irrigation and Drainage Engineering, Aburaihan Campus, University of Tehran, Tehran, Iran.
Environ Monit Assess. 2021 Mar 9;193(4):173. doi: 10.1007/s10661-021-08961-y.
Water resources management requires a proper understanding of the status of available and exploitable water. One of the useful management tools is the use of simulation models that are highly efficient in spite of the complex problems in the groundwater sector. In the present study, three data-based models, namely, group method of data handling (GMDH), Bayesian network (BN), and artificial neural network (ANN), have been investigated to simulate the groundwater levels and assess the quantitative status of aquifers. Five observation wells were selected in Birjand aquifer using spatial clustering to analyze and evaluate the aquifer. To determine the effective variables in predicting groundwater level, 10 scenarios were developed by combining several variables, including groundwater level in the previous month, aquifer exploitation, surface recharge, precipitation, temperature, and evaporation. Results showed that the GMDH model with three input variables, i.e., the groundwater level in the previous month, aquifer exploitation, and precipitation, had the highest prediction performance, RMSE, NASH, MAPE, and R of which were obtained equal to 0.074, 0.97, 0.0037, and 0.97, respectively. Furthermore, Taylor's diagram showed that the predicted values using the GMDH model had the highest correlation with the observational data. Hydrograph simulation was performed for 6 years to analyze the condition of the aquifer. The results showed that the groundwater level is in critical condition in this aquifer, and a 1.2-m groundwater loss was predicted for this aquifer. The findings of this study show that the management of the studied aquifer is necessary to improve its current situation.
水资源管理需要正确了解可用和可开采水资源的状况。一种有用的管理工具是使用模拟模型，尽管地下水部门存在复杂的问题，但这些模型的效率非常高。在本研究中，研究了三种基于数据的模型，即数据处理分组方法 (GMDH)、贝叶斯网络 (BN) 和人工神经网络 (ANN)，以模拟地下水位并评估含水层的定量状况。使用空间聚类选择了 Birjand 含水层的五个观测井，以分析和评估含水层。为了确定预测地下水位的有效变量，通过组合包括前一个月的地下水位、含水层开采、地表水补给、降水、温度和蒸发在内的多个变量，开发了 10 种情景。结果表明，具有三个输入变量（前一个月的地下水位、含水层开采和降水）的 GMDH 模型具有最高的预测性能，其 RMSE、NASH、MAPE 和 R 分别为 0.074、0.97、0.0037 和 0.97。此外，泰勒图显示，GMDH 模型预测值与观测数据具有最高的相关性。进行了 6 年的水位过程模拟以分析含水层的状况。结果表明，该含水层的地下水位处于危急状态，预计该含水层的地下水位将损失 1.2 米。本研究的结果表明，有必要对研究含水层进行管理，以改善其现状。
