Using Tropical Rainfall Measuring Mission for Accurate Daily Runoff Modeling using intelligent systems: The Case of Chelif Basin (Wadi ouahrane basin in Northen Algeria)
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Date
2024
Authors
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Publisher
Bilel Zerouali
Abstract
Predicting tools for river flow using machine learning and deep learning are considered essential for
sustainable planning in water resource management. This study evaluates the effectiveness of
Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), and XGBoost models in predicting
daily flows using data from the Tropical Rainfall Measuring Mission (TRMM) in the Cheliff basin in
northern Algeria. Our assessment, based on the Root Mean Square Error (RMSE), includes a series of
scenarios involving 9 different models. Our results show that the MLP performs competitively,
achieving the lowest RMSE in scenario M8 (RMSE = 2.485) when using TRMM data. Moreover, in
comparison with in-situ data, both LSTM and MLP models demonstrate reasonable performance. In
conclusion, this study highlights the effectiveness of LSTM in using satellite-derived precipitation data
for accurate daily flow prediction
Description
Sector: Hydraulic
Specialty: Urban Hydraulic
Keywords
Flow prediction, MLP, XGBoost, Bilel Zerouali