Issue |
E3S Web Conf.
Volume 489, 2024
4th International GIRE3D Congress “Participatory and Integrated Management of Water Resources in Arid Zones” (GIRE3D 2023)
|
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Article Number | 04010 | |
Number of page(s) | 9 | |
Section | Numerical Modeling, Remote Sensing, Geomatic & Application of Intelligence Artificielle | |
DOI | https://doi.org/10.1051/e3sconf/202448904010 | |
Published online | 09 February 2024 |
Enhancing Precipitation Prediction in the Ziz Basin: A Comprehensive Review of Traditional and Machine Learning Approaches
1 Informatics and Applications Laboratory, Science Faculty of Meknes, Moulay Ismail University, Meknes, Morocco
2 Ibn Tofail University, National School of Applied Sciences, Kenitra, Morocco
3 Laboratory of Geo-Engineering and Environment, Faculty of Sciences, Moulay Ismail University, Meknes, Morocco
4 Department of Environment, Functional Ecology and Environmental Engineering Laboratory, Faculty of Sciences and Technology, Sidi Mohamed Ben Abdellah University, Fez, Morocco
* Corresponding author: saray.bouziane@gmail.com
Accurate precipitation forecasting is paramount for various sectors. Traditional methods for rainfall prediction involve understanding physical processes, historical weather data, and statistical models. These methods utilize observations from ground-based weather stations, satellites, and weather radars to assess current conditions and predict future precipitation. However, accurate precipitation prediction remains challenging due to the intricate and non-linear characteristics of rainfall. Over the past few years, machine learning (ML) algorithms have shown promise in improving precipitation prediction accuracy. This research provides an overview of both traditional methods and advanced ML models applicable to rainfall prediction, including regression, classification, and time series models. We conducted a comprehensive review of related works that explore the impact of using ML algorithms for rainfall estimation. Through this analysis, we identified the strengths and limitations of ML models in this context and highlighted advancements in rainfall prediction using these algorithms. We possess a comprehensive dataset, spanning data from 1996 to 2015, comprising historical weather data from the Ziz basin, our designated study area. This dataset contains five key meteorological features: precipitation, humidity, wind, temperature, and evaporation. In terms of perspective, we plan to utilize this dataset and conduct a comprehensive comparative study to evaluate the performance of different ML models. Our objective is to demonstrate the effectiveness and potential of these algorithms in improving weather forecasting capabilities and enhancing the accuracy of rainfall estimation methods in the specific study area.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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