What question did this study set out to answer?

The aim is to evaluate various deep learning models for forecasting daily discharge in the Upper Awash Basin amidst climate variability.

February 5, 2026Open Access

Inter-Comparison of Deep Learning Models for Flood Forecasting in Ethiopia’s Upper Awash Basin

Key Points

The aim is to evaluate various deep learning models for forecasting daily discharge in the Upper Awash Basin amidst climate variability.
Benchmark five deep learning architectures: CNN, LSTM, GRU, BiLSTM, Hybrid CNN–LSTM.
Use 40 years of hydrometeorological data (1981–2020) for model training.
Evaluate model performance against baseline HBV and climatology approaches using standard hydrological metrics.
All deep learning models exceeded test NSE values of 0.83, demonstrating significant predictive improvement.
The Hybrid CNN–LSTM model achieved the best balanced performance with NSE ≈ 0.84.
High accuracy (over 89%) in detecting extreme-flow occurrences was noted with deep learning models.

Abstract

Flood events driven by climate variability and change pose significant risks for socio-economic activities in the Awash Basin, necessitating advanced forecasting tools. This study benchmarks five deep learning (DL) architectures, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (BiLSTM), and a Hybrid CNN–LSTM, for daily discharge forecasting for the Hombole catchment in the Upper Awash Basin (UAB) using 40 years of hydrometeorological observations (1981–2020). Rainfall, lagged discharge, and seasonal indicators were used as predictors. Model performance was evaluated against two baseline approaches, a conceptual HBV rainfall–runoff model as well as a climatology, using standard and hydrological metrics. Of the two baselines (climatology and HBV), the climatology showed limited skill with large bias and negative NSE, whereas the HBV model achieved moderate skill (NSE = 0.64 and KGE = 0.82). In contrast, all DL models substantially improved predictive performance, achieving test NSE values above 0.83 and low overall bias. Among them, the Hybrid CNN–LSTM provided the most balanced performance, combining local temporal feature extraction with long-term memory and yielding stable efficiency (NSE ≈ 0.84, KGE ≈ 0.90, and PBIAS ≈ −2%) across flow regimes. The LSTM and GRU models performed comparably, offering strong temporal learning and robust daily predictions, while BiLSTM improved flood timing through bidirectional sequence modeling. The CNN captured short-term variability effectively but showed weaker representation of extreme peaks. Analysis of peak-flow metrics revealed systematic underestimation of extreme discharge magnitudes across all models. However, a post-processing flow-regime classification based on discharge quantiles demonstrated high extreme-event detection skill, with deep learning models exceeding 89% accuracy in identifying extreme-flow occurrences on the test set. These findings indicate that, while magnitude errors remain for rare floods, DL models reliably discriminate flood regimes relevant for early warning. Overall, the results show that deep learning models provide clear improvements over climatology and conceptual baselines for daily streamflow forecasting in the UAB, while highlighting remaining challenges in peak-flow magnitude prediction. The study indicates promising results for the integration of deep learning methods into flood early-warning workflows; however, these results could be further improved by adopting a probabilistic forecasting framework that accounts for model uncertainty.

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