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Volume 8 Issue 1
Sep.  2020
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Abdulrahman Th Mohammad, Qassem H Jalut, Nadia L Abbas. 2020: Predicting groundwater level of wells in the Diyala River Basin in eastern Iraq using artificial neural network. Journal of Groundwater Science and Engineering, 8(1): 87-96. doi: 10.19637/j.cnki.2305-7068.2020.01.009
Citation: Abdulrahman Th Mohammad, Qassem H Jalut, Nadia L Abbas. 2020: Predicting groundwater level of wells in the Diyala River Basin in eastern Iraq using artificial neural network. Journal of Groundwater Science and Engineering, 8(1): 87-96. doi: 10.19637/j.cnki.2305-7068.2020.01.009

Predicting groundwater level of wells in the Diyala River Basin in eastern Iraq using artificial neural network

doi: 10.19637/j.cnki.2305-7068.2020.01.009
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Abdulrahman Th Mohammad

  • Al-Mansourieh zone is a part of Al-Khalis city within the province of Diyala and located in the Diyala River Basin in eastern Iraq with a total area about 830 km2. Groundwater is the main water source for agriculture in this zone. Random well drilling without geological and hydraulic information has led the most of these wells to dry up quickly. Therefore, it is necessary to estimate the levels of groundwater in wells through observed data. In this study, Alyuda NeroIntelligance 2.1 software was applied to predict the groundwater levels in 244 wells using sets of measured data. These data included the coordinates of wells (x, y), elevations, well depth, discharge and groundwater levels. Three ANN structures (5-3-3-1, 5-10-10-1 and 5-11-11-1) were used to predict the groundwater levels and to acquire the best matching between the measured and ANN predicted values. The coefficient of correlation, coefficient determination (R2) and sum-square error (SSE) were used to evaluate the performance of the ANN models. According to the ANN results, the model with the three structures has a good predictability and proves more effective for determining groundwater level in wells. The best predictor was achieved in the structure 5-3-3-1, with R2 about 0.92, 0.89, 0.84 and 0.91 in training, validation, testing and all processes respectively. The minimum average error in the best predictor is achieved in validation and testing processes at about 0.130 and 0.171 respectively. On the other hand, the results indicated that the model has the potential to determine the appropriate places for drilling the wells to obtain the highest level of groundwater.
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