| Artificial Intelligence Hybrid‑Deep Learning Model for Groundwater Level Prediction Using MLP-ADAM |
| کد مقاله : 1150-IHA (R1) |
| نویسندگان |
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پژمان زرافشان *1، سامان جوادی1، عباس روزبهانی2، مهدی هاشمی1، پیام زرافشان3، حامد اعتضادی3 1دانشگاه تهران پردیس ابوریحان گروه مهندسی آب 2گروه مهندسی آب/ پردیس ابوریحان/دانشگاه تهران/پاکدشت/ایران 3دانشگاه تهران پردیس ابوریحان گروه فنی |
| چکیده مقاله |
| Groundwater is the largest storage of freshwater resources, which serves as the major inventory for most of the human consumption through agriculture, industrial, and domestic water supply. In the fields of hydrological, some researchers applied a neural network to forecast rainfall intensity in space-time and introduced the advantages of neural networks compared to numerical models. Then, researches have been conducted applying data-driven models. Some of them extended an Artificial Neural Networks model to forecast groundwater level in semi-confined glacial sand and gravel aquifer under variable state, pumping extraction, and climate conditions with significant accuracy. In this paper, a Multi-Layer Perceptron model is applied to simulate groundwater level. The adaptive moment estimation optimization algorithm is also used in this matter. This results in a hybrid MLP-ADAM model whereas the hyper-parameters of the MLP model are optimized with the ADAM method. Also, the case study aquifer is located in Najafabad plain, Gavkhoni catchment consists of 21 basins. By applying the stated hybrid model for this region, the root mean squared error, mean absolute error, mean squared error and the coefficient of determination are used to evaluate the accuracy of the simulated groundwater level. The total value of and RMSE are 0.9458 and 0.7313 respectively which are obtained from the model output. Results indicate that deep learning algorithms can demonstrate a high accuracy prediction. Although the optimization of parameters is insignificant in numbers, due to the value of time in modeling setup, it is highly recommended to apply an optimization algorithm in modeling. |
| کلیدواژه ها |
| Hybrid deep learning model; Groundwater; MLP; ADAM |
| وضعیت: پذیرفته شده |