[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126030-en":3,"doc-seo-126030-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":11,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},126030,2336474466412,"Ezra","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Evaluating machine learning models for precipitation prediction in Casablanca City - Article","Accurate precipitation forecasting supports agriculture, flood and drought preparedness, and water resource management, where rainfall uncertainty directly affects decisions and risk reduction. This study compares eight machine learning approaches for precipitation prediction in Casablanca, Morocco, including linear regression, polynomial regression, K-nearest neighbors, support vector machine, decision tree, random forest, XGBoost, and an ensemble learning model. Models are evaluated using MAE, MSE, and R-squared based on meteorological variables such as temperature, humidity, wind speed, pressure, and precipitation.","Indonesian Journal of Electrical Engineering and Computer Science  \nVol. 35, No. 2, August 2024, pp. 1325~1332  \nISSN: 2502-4752, DOI: 10. 11591/ijeecs.v35.i2 .pp1325-1332 􀂈 1325  \n\n| Evaluating machine learning models for precipitation prediction in Casablanca City\u003Cbr>Abdelouahed Tricha, Laila Moussaid\u003Cbr>E2IT group research, LRI laboratory, National School of Electrical and Mechanical Engineering (ENSEM), Hassan II University, Casablanca, Morocco |  |\n| --- | --- |\n| Article Info\u003Cbr>Article history:\u003Cbr>Received Jan 16, 2024 Revised Mar 23, 2024 Accepted Apr 13, 2024\u003Cbr>Keywords:\u003Cbr>Artificial intelligence Ensemble learning Machine learning Precipitation prediction Water management\u003Cbr>Corresponding Author: | ABSTRACT\u003Cbr>Accurate precipitation forecasting is a vital task for many domains, such as agriculture, water management, flood prevention, and crop yield estimation. The use of machine learning (ML) approaches has improved precipitation forecasting accuracy, exhibiting promising results in capturing the intricate connections between various meteorological variables and precipitation patterns. However, given the vast array of available ML models, a comparative analysis is imperative for identifying the most effective models for precipitation prediction. This study aims to examine the capacities of ML algorithms to forecast precipitation based on weather data for the city of Casablanca, Morocco, which faces challenges in water management and climate change adaptation. Eight different ML models ’ performances are compared: linear regression, polynomial regression, K-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), XGBoost, and an ensemble learning model. These models are evaluated based on their mean absolute error (MAE), mean squared error (MSE), and R-squared (R2) value to determine their effectiveness. The study showcases the potential of ML models in predicting precipitation by utilizing meteorological parameters such as temperature, humidity, wind speed, and pressure.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| Abdelouahed Tricha\u003Cbr>E2IT group research, LRI laboratory, National High School for Electricity and Mechanics (ENSEM) Hassan II University\u003Cbr>Casablanca, Morocco\u003Cbr>Email: [tricha.abdelouahed@gmail.com](tricha.abdelouahed@gmail.com) |  |\n\n1. INTRODUCTION  \nPrecipitation plays a crucial role in the water cycle and profoundly affects various aspects of our everyday life and the environment. It is a key input for hydrological modelling and forecasting and is widely used in sectors such as agriculture, flood and drought forecasting, water resource management, and so on [1],[2] . Making informed decisions about water management and preparing for floods and droughts can be made easier for communities with the help of accurate rainfall forecasts. Knowing how much rain to expect allows communities to plan and manage their water more effectively. For agriculture, knowing when and how much precipitation to expect can help farmers decide when to plant and irrigate their crops to ensure optimal growth and yields. In addition, accurate rainfall forecasts are critical for flood and drought preparedness, allowing authorities to take timely action, such as implementing flood control measures or water restrictions during droughts.  \nRecently, applying machine learning (ML) to climate forecasting has been investigated. Researchers have been inspired to use artificial intelligence (AI) methods to predict and categorize events under different  \nclimatic conditions by combining AI techniques with meteorological science [3], [4] . Time series data, gathered according to specific patterns, are an important field of research [5]-[10] . The intricate interactions between many meteorological data points, such as wind speed, temperature, humidity, pressure, and precipitation, may be modeled using ML techniques. These models may then be deployed to accurately forecast pr","cbCaidAtxjI6t3RU","https://ap.wps.com/l/cbCaidAtxjI6t3RU","pdf",489324,1,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"Which machine learning models are compared for precipitation prediction in Casablanca?\",\"answer\":\"The study compares linear regression, polynomial regression, K-nearest neighbors, support vector machine, decision tree, random forest, XGBoost, and an ensemble learning model.\"},{\"question\":\"What variables are used to predict precipitation?\",\"answer\":\"Predictions are based on meteorological parameters including temperature, humidity, wind speed, specific humidity, surface pressure, and precipitation-related data.\"},{\"question\":\"How are the models evaluated in the study?\",\"answer\":\"Model performance is assessed using mean absolute error (MAE), mean squared error (MSE), and R-squared (R2) to determine predictive effectiveness.\"}]","Evaluating machine learning models for precipitation prediction in Casablanca City - Article | PDF",1785902627,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"evaluating-machine-learning-models-for-precipitation-prediction-in-casablanca-city-article","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/evaluating-machine-learning-models-for-precipitation-prediction-in-casablanca-city-article/126030/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":11},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which machine learning models are compared for precipitation prediction in Casablanca?","Question",{"text":75,"@type":76},"The study compares linear regression, polynomial regression, K-nearest neighbors, support vector machine, decision tree, random forest, XGBoost, and an ensemble learning model.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What variables are used to predict precipitation?",{"text":80,"@type":76},"Predictions are based on meteorological parameters including temperature, humidity, wind speed, specific humidity, surface pressure, and precipitation-related data.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the models evaluated in the study?",{"text":84,"@type":76},"Model performance is assessed using mean absolute error (MAE), mean squared error (MSE), and R-squared (R2) to determine predictive effectiveness.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":45,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":45,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":125,"show_sort_weight":28,"slug":126},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":28,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]