Forecasting Lake Nokoué Water Levels Using Long Short-Term Memory Network
The forecasting of hydrological flows (rainfall depth or rainfall discharge) is becoming increasingly important in the management of hydrological risks such as floods. In this study, the Long Short-Term Memory (LSTM) network, a state-of-the-art algorithm dedicated to time series, is applied to predi...
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MDPI AG
2024-10-01
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| author | Namwinwelbere Dabire Eugene C. Ezin Adandedji M. Firmin |
| author_facet | Namwinwelbere Dabire Eugene C. Ezin Adandedji M. Firmin |
| author_sort | Namwinwelbere Dabire |
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| description | The forecasting of hydrological flows (rainfall depth or rainfall discharge) is becoming increasingly important in the management of hydrological risks such as floods. In this study, the Long Short-Term Memory (LSTM) network, a state-of-the-art algorithm dedicated to time series, is applied to predict the daily water level of Lake Nokoué in Benin. This paper aims to provide an effective and reliable method to enable the reproduction of the future daily water level of Lake Nokoué, which is influenced by a combination of two phenomena: rainfall and river flow (runoff from the Ouémé River, the Sô River, the Porto-Novo lagoon, and the Atlantic Ocean). Performance analysis based on the forecasting horizon indicates that LSTM can predict the water level of Lake Nokoué up to a forecast horizon of <i>t</i> + 10 days. Performance metrics such as Root Mean Square Error (RMSE), coefficient of correlation (R<sup>2</sup>), Nash–Sutcliffe Efficiency (NSE), and Mean Absolute Error (MAE) agree on a forecast horizon of up to <i>t</i> + 3 days. The values of these metrics remain stable for forecast horizons of <i>t</i> + 1 day, <i>t</i> + 2 days, and <i>t</i> + 3 days. The values of R<sup>2</sup> and NSE are greater than 0.97 during the training and testing phases in the Lake Nokoué basin. Based on the evaluation indices used to assess the model’s performance for the appropriate forecast horizon of water level in the Lake Nokoué basin, the forecast horizon of <i>t</i> + 3 days is chosen for predicting future daily water levels. |
| format | Article |
| id | doaj-art-915e22d741d6461b9bc30e1f7c1367da |
| institution | OA Journals |
| issn | 2306-5338 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
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| series | Hydrology |
| spelling | doaj-art-915e22d741d6461b9bc30e1f7c1367da2025-08-20T02:11:05ZengMDPI AGHydrology2306-53382024-10-01111016110.3390/hydrology11100161Forecasting Lake Nokoué Water Levels Using Long Short-Term Memory NetworkNamwinwelbere Dabire0Eugene C. Ezin1Adandedji M. Firmin2National Institute of Water (INE), African Center of Excellence for Water and Sanitation (C2EA), University of Abomey Calavi (UAC), Cotonou 01BP526, BeninInstitute of Training and Research in Computer Science (IFRI), University of Abomey Calavi (UAC), Cotonou 01BP526, BeninLaboratory of Applied Hydrology (LHA), University of Abomey Calavi (UAC), Cotonou 01BP526, BeninThe forecasting of hydrological flows (rainfall depth or rainfall discharge) is becoming increasingly important in the management of hydrological risks such as floods. In this study, the Long Short-Term Memory (LSTM) network, a state-of-the-art algorithm dedicated to time series, is applied to predict the daily water level of Lake Nokoué in Benin. This paper aims to provide an effective and reliable method to enable the reproduction of the future daily water level of Lake Nokoué, which is influenced by a combination of two phenomena: rainfall and river flow (runoff from the Ouémé River, the Sô River, the Porto-Novo lagoon, and the Atlantic Ocean). Performance analysis based on the forecasting horizon indicates that LSTM can predict the water level of Lake Nokoué up to a forecast horizon of <i>t</i> + 10 days. Performance metrics such as Root Mean Square Error (RMSE), coefficient of correlation (R<sup>2</sup>), Nash–Sutcliffe Efficiency (NSE), and Mean Absolute Error (MAE) agree on a forecast horizon of up to <i>t</i> + 3 days. The values of these metrics remain stable for forecast horizons of <i>t</i> + 1 day, <i>t</i> + 2 days, and <i>t</i> + 3 days. The values of R<sup>2</sup> and NSE are greater than 0.97 during the training and testing phases in the Lake Nokoué basin. Based on the evaluation indices used to assess the model’s performance for the appropriate forecast horizon of water level in the Lake Nokoué basin, the forecast horizon of <i>t</i> + 3 days is chosen for predicting future daily water levels.https://www.mdpi.com/2306-5338/11/10/161forecastingmachine learning algorithmsrecurrent artificial neural networkLake Nokoué |
| spellingShingle | Namwinwelbere Dabire Eugene C. Ezin Adandedji M. Firmin Forecasting Lake Nokoué Water Levels Using Long Short-Term Memory Network Hydrology forecasting machine learning algorithms recurrent artificial neural network Lake Nokoué |
| title | Forecasting Lake Nokoué Water Levels Using Long Short-Term Memory Network |
| title_full | Forecasting Lake Nokoué Water Levels Using Long Short-Term Memory Network |
| title_fullStr | Forecasting Lake Nokoué Water Levels Using Long Short-Term Memory Network |
| title_full_unstemmed | Forecasting Lake Nokoué Water Levels Using Long Short-Term Memory Network |
| title_short | Forecasting Lake Nokoué Water Levels Using Long Short-Term Memory Network |
| title_sort | forecasting lake nokoue water levels using long short term memory network |
| topic | forecasting machine learning algorithms recurrent artificial neural network Lake Nokoué |
| url | https://www.mdpi.com/2306-5338/11/10/161 |
| work_keys_str_mv | AT namwinwelberedabire forecastinglakenokouewaterlevelsusinglongshorttermmemorynetwork AT eugenecezin forecastinglakenokouewaterlevelsusinglongshorttermmemorynetwork AT adandedjimfirmin forecastinglakenokouewaterlevelsusinglongshorttermmemorynetwork |