تقرير
Artificial Intelligence Hybrid Deep Learning Model for Groundwater Level Prediction Using MLP-ADAM
| العنوان: | Artificial Intelligence Hybrid Deep Learning Model for Groundwater Level Prediction Using MLP-ADAM |
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| المؤلفون: | Zarafshan, Pejman, Javadi, Saman, Roozbahani, Abbas, Hashemy, Seyed Mehdi, Zarafshan, Payam, Etezadi, Hamed |
| سنة النشر: | 2021 |
| المجموعة: | Computer Science Physics (Other) |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Physics - Geophysics |
| الوصف: | 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, many researches have been conducted applying data-driven models. Some of them extended an Artificial Neural Networks (ANNs) 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 is applied to simulate groundwater level. The adaptive moment estimation optimization algorithm is also used to this matter. 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. 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, but due to the value of time in modelling setup, it is highly recommended to apply an optimization algorithm in modelling. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2107.13870 |
| رقم الأكسشن: | edsarx.2107.13870 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |