DOI: 10.11648/j.wros.20261503.12 ISSN: 2328-7993

A Hybrid STL - GEV and RNNs Models Approach for Monthly Extreme Discharge Forecasting in the Mono Basin

Sama Souvenir, Sagna Koffi, Apeke Kodjo, Etho Salomon
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Forecasting monthly extreme flows is a major challenge in hydrology due to their rarity and high intensity, particularly in tropical basins vulnerable to climate change. This study proposes an innovative hybrid approach combining STL decomposition, generalized extreme value (GEV) theory, and LSTM and GRU architectures to predict river flow: the case of the Mono River in Togo. The methodology is based on isolating the residual component, modeled by a GEV distribution, whose values are converted into probabilities using a cumulative distribution function. A unique feature of this approach is the incorporation of multivariate meteorological data. Unlike conventional approaches, the results show that the hybrid model particularly in its univariate sequential configuration reproduces extreme dynamics with a high degree of accuracy. The evaluation was conducted at various stations in Togo using the "Peak Over Threshold" approach, applied at the 75th percentile. At the Dotaicopé station, the model performed robustly, achieving an accuracy of 0.82, a recall of 0.74, an F1 score of 0.78, and a Kling-Gupta efficiency coefficient of 0.75. At the Tététou station, the multivariate model achieved an exceptional recall of 0.9, confirming its superior ability to detect critical thresholds in areas with high hydrological variability; the univariate model, on the other hand, performed less well in this regard, thereby demonstrating the significant contribution of climatic parameters. However, the study highlights a limitation related to data asymmetry, as climate forcings are only available starting in 1981, whereas discharge records date back to 1952. These results validate the potential of both univariate and multivariate probabilistic hybrid models for better characterization of hydrological regimes and early flood risk prevention.

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