, Oumar Keita1, Lonsenigbè Camara1, Gideon Eustace Rwabona2, 3, Abdoulaye Sylla11Département d’Hydrologie, Université de N’Zérékoré, BP 50, N’Zérékoré, Guinée
2School of Computational and Communication Science and Engineering, Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania
3Department of Mathematics and Statistics, Mbeya University of Science and Technology, Mbeya, Tanzania
Flood control and water resources management are two critical tasks for hydrologists, and both heavily depend on accurate river water level forecasting. However, due to the intrinsic characteristics of water level series, it is difficult to achieve good forecasting accuracy. In Guinea, the forecasting of water level by physical models, and mathematical or data-driven models remains scarce. This study aims to implement for the first time in Guinea, the autoregressive integrated moving average (ARIMA) model and propose the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) coupled with sample entropy (SE) and combined with ARIMA model namely as ICEEMDAN-SE-ARIMA to forecast Diani River monthly water level in southern guinea. The water level data of Diani hydrological station from 2000 to 2022 were used, in which the water data from 2000 to 2019 were used to build the models, and the data from 2020 to 2022 were used for validation. Seven statistical indices like Pearson’s coefficient, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Symmetric Mean Absolute Percentage Error (sMAPE), Nash-Sutcliffe coefficient (NSCE), BIAS and Kolmogorov-Smirnov coefficient (DKS) are adopted to measure and compare the performance of the single ARIMA and ICEEMDAN-SE-ARIMA hybrid models. The results indicate that: (1) during the study period, six pseudo-periodic functions and one nonlinear trend contribute differently to Diani water level series forecasting, indicating their complexity; (2) Compared to the single ARIMA model, the Pearson’s coefficient, DKS, BIAS, NSCE, RMSE, MAE and SMAPE of ICEEMDAN-SE-ARIMA were improved by 84.52%, 84.70%, 80%, 84.52%, 86%, 91%, 93%, and 80%, respectively; (3) ICEEMDAN-SE-ARIMA model outperformed the single ARIMA model. However, it seems that ICEEMDAN-SE-ARIMA model could be improved by combining ICEEMDAN-SE by other data-driven models. These findings are essential to enhance water resources management and flood mitigation in Guinea, mainly under climate change.
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