首页|New Machine Learning Research from Mohammed V University Described (Improving mo nthly precipitation prediction accuracy using machine learning models: a multi-v iew stacking learning technique)
New Machine Learning Research from Mohammed V University Described (Improving mo nthly precipitation prediction accuracy using machine learning models: a multi-v iew stacking learning technique)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news reporting originating from Rabat, Moroc co, by NewsRx correspondents, research stated, “This research paper explores the implementation of machine learning (ML) techniques in weather and climate forec asting, with a specific focus on predicting monthly precipitation. The study ana lyzes the efficacy of six multivariate machine learning models: Decision Tree, R andom Forest, K-Nearest Neighbors (KNN), AdaBoost, XGBoost, and Long Short-Term Memory (LSTM).” The news editors obtained a quote from the research from Mohammed V University: “Multivariate time series models incorporating lagged meteorological variables w ere employed to capture the dynamics of monthly rainfall in Rabat, Morocco, from 1993 to 2018. The models were evaluated based on various metrics, including roo t mean square error (RMSE), mean absolute error (MAE), and coefficient of determ ination (R2). XGBoost showed the highest performance among the six individual mo dels, with an RMSE of 40.8 (mm). In contrast, Decision Tree, AdaBoost, Random Fo rest, LSTM, and KNN showed relatively lower performances, with specific RMSEs ra nging from 47.5 (mm) to 51 (mm). A novel multi-view stacking learning approach i s introduced, offering a new perspective on various ML strategies. This integrat ed algorithm is designed to leverage the strengths of each individual model, aim ing to substantially improve the precision of precipitation forecasts. The best results were achieved by combining Decision Tree, KNN, and LSTM to build the met a-base while using XGBoost as the second-level learner.”
Mohammed V UniversityRabatMoroccoA fricaCyborgsEmerging TechnologiesMachine Learning
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