Version 1
: Received: 20 March 2024 / Approved: 20 March 2024 / Online: 20 March 2024 (11:36:02 CET)
How to cite:
Dabire, N.; Ezin, E.C.; Firmin, A.M. Forecasting Lake Nokoué Water Levels Using Long Short-Term Memory Network. Preprints2024, 2024031218. https://doi.org/10.20944/preprints202403.1218.v1
Dabire, N.; Ezin, E.C.; Firmin, A.M. Forecasting Lake Nokoué Water Levels Using Long Short-Term Memory Network. Preprints 2024, 2024031218. https://doi.org/10.20944/preprints202403.1218.v1
Dabire, N.; Ezin, E.C.; Firmin, A.M. Forecasting Lake Nokoué Water Levels Using Long Short-Term Memory Network. Preprints2024, 2024031218. https://doi.org/10.20944/preprints202403.1218.v1
APA Style
Dabire, N., Ezin, E.C., & Firmin, A.M. (2024). Forecasting Lake Nokoué Water Levels Using Long Short-Term Memory Network. Preprints. https://doi.org/10.20944/preprints202403.1218.v1
Chicago/Turabian Style
Dabire, N., Eugene C. Ezin and Adandedji M. Firmin. 2024 "Forecasting Lake Nokoué Water Levels Using Long Short-Term Memory Network" Preprints. https://doi.org/10.20944/preprints202403.1218.v1
Abstract
The prediction 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 Nokoue in Benin. This paper aims to provide an effective and reliable method enable of reproducing the future daily water level of Lake Nokoue, 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 Nokoué Lake up to a forecast horizon of t+10 days. Performance metrics such as Root Mean Square Error (RMSE), coefficient of correlation (R²), Nash-Sutcliffe Efficiency (NSE), and Mean Absolute Error (MAE) agree on a forecast horizon of up to t+3 days. The values of these metrics remain stable for forecast horizons of t+1 days, t+2 days, and t+3 days. The values of R² and NSE are greater than 0.97 during the training and testing phases in the Nokoué Lake basin. Based on the evaluation indices used to assess the model's performance for the appropriate forecast horizon of water level in the Nokoué lake basin, the forecast horizon of t+3 days is chosen for predicting future daily water levels.
Keywords
forecasting; Machine learning algorithms; recurrent artificial neural network; lake Nokoue.
Subject
Environmental and Earth Sciences, Water Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
