Enhancing Precipitation Prediction in the Ziz Basin: A Comprehensive Review of Traditional and Machine Learning Approaches
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 r...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2024-01-01
|
| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/19/e3sconf_gire3d2024_04010.pdf |
| _version_ | 1797315642912669696 |
|---|---|
| author | Bouziane Sara Aghoutane Badraddine Moumen Aniss Sahlaoui Ali El Ouali Anas |
| author_facet | Bouziane Sara Aghoutane Badraddine Moumen Aniss Sahlaoui Ali El Ouali Anas |
| author_sort | Bouziane Sara |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T03:06:45Z |
| format | Article |
| id | doaj.art-41c8ec78ffab4c40ab020ff1bab36e78 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-08T03:06:45Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-41c8ec78ffab4c40ab020ff1bab36e782024-02-13T08:28:20ZengEDP SciencesE3S Web of Conferences2267-12422024-01-014890401010.1051/e3sconf/202448904010e3sconf_gire3d2024_04010Enhancing Precipitation Prediction in the Ziz Basin: A Comprehensive Review of Traditional and Machine Learning ApproachesBouziane Sara0Aghoutane Badraddine1Moumen Aniss2Sahlaoui Ali3El Ouali Anas4Informatics and Applications Laboratory, Science Faculty of Meknes, Moulay Ismail UniversityInformatics and Applications Laboratory, Science Faculty of Meknes, Moulay Ismail UniversityIbn Tofail University, National School of Applied SciencesLaboratory of Geo-Engineering and Environment, Faculty of Sciences, Moulay Ismail UniversityDepartment of Environment, Functional Ecology and Environmental Engineering Laboratory, Faculty of Sciences and Technology, Sidi Mohamed Ben Abdellah UniversityAccurate 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.https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/19/e3sconf_gire3d2024_04010.pdf |
| spellingShingle | Bouziane Sara Aghoutane Badraddine Moumen Aniss Sahlaoui Ali El Ouali Anas Enhancing Precipitation Prediction in the Ziz Basin: A Comprehensive Review of Traditional and Machine Learning Approaches E3S Web of Conferences |
| title | Enhancing Precipitation Prediction in the Ziz Basin: A Comprehensive Review of Traditional and Machine Learning Approaches |
| title_full | Enhancing Precipitation Prediction in the Ziz Basin: A Comprehensive Review of Traditional and Machine Learning Approaches |
| title_fullStr | Enhancing Precipitation Prediction in the Ziz Basin: A Comprehensive Review of Traditional and Machine Learning Approaches |
| title_full_unstemmed | Enhancing Precipitation Prediction in the Ziz Basin: A Comprehensive Review of Traditional and Machine Learning Approaches |
| title_short | Enhancing Precipitation Prediction in the Ziz Basin: A Comprehensive Review of Traditional and Machine Learning Approaches |
| title_sort | enhancing precipitation prediction in the ziz basin a comprehensive review of traditional and machine learning approaches |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/19/e3sconf_gire3d2024_04010.pdf |
| work_keys_str_mv | AT bouzianesara enhancingprecipitationpredictioninthezizbasinacomprehensivereviewoftraditionalandmachinelearningapproaches AT aghoutanebadraddine enhancingprecipitationpredictioninthezizbasinacomprehensivereviewoftraditionalandmachinelearningapproaches AT moumenaniss enhancingprecipitationpredictioninthezizbasinacomprehensivereviewoftraditionalandmachinelearningapproaches AT sahlaouiali enhancingprecipitationpredictioninthezizbasinacomprehensivereviewoftraditionalandmachinelearningapproaches AT eloualianas enhancingprecipitationpredictioninthezizbasinacomprehensivereviewoftraditionalandmachinelearningapproaches |