Relating groundwater levels with meteorological parameters using ANN technique
| Autor: | Usman Ghani, Mujahid Iqbal, Tallat Farid, Habib ur Rehman, Afaq Ahmad, Usman Ali Naeem |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Coefficient of determination
Artificial neural network Mean squared error Water table Applied Mathematics 020208 electrical & electronic engineering 010401 analytical chemistry Activation function Elevation Soil science 02 engineering and technology Condensed Matter Physics 01 natural sciences 0104 chemical sciences Polygon 0202 electrical engineering electronic engineering information engineering Relative humidity Electrical and Electronic Engineering Instrumentation Mathematics |
| Zdroj: | Measurement. 166:108163 |
| ISSN: | 0263-2241 |
| DOI: | 10.1016/j.measurement.2020.108163 |
| Popis: | In this study, the groundwater occupied within the boundaries of River Ravi and River Sutlej was investigated by using artificial neural networks (ANN) by using the meteorological parameters (data of rainfall (R), maximum temperature (Max.T), minimum temperature (Min.T), solar radiation (S.R.), wind (W), relative humidity (R.H.), the elevation of area (E), polygon area (A) and water table depths (D/W). The best efficient models were studied by using different types of network architecture, such as the number of neurons, hidden layers, and activation function with a different percent of data in training, validation, and testing. The developed Levenberg-Marquardt back-propagation ANN models were compared through statistical performance criteria: Mean Square Error (MSE), Mean Absolute Error (MAE) and Coefficient of determination (R). The results (TT-8-24-1 for pre-monsoon and TT-8-24-1 for post-monsoon) show that ANN model with single hidden layer, 24 neurons, 80% of data for training, 10% for validation, 10% for testing and using tangent sigmoid activation function was found to be optimistic ANN model with MAE, MSE and R values of 0.0338, 0.0023 and 0.97 for pre-monsoon and 0.031, 0.0021 and 0.974 for the case of post-monsoon respectively. |
| Databáze: | OpenAIRE |
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