摘要
由一名新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-调查人员发布了关于人工智能的新报告。根据NewsRx记者从摩洛哥拉巴特发回的新闻报道，Research称：“这篇研究论文探讨了机器学习(ML)技术在天气和气候预报中的应用，重点是预测月降水量。研究分析了六种多变量机器学习模型的有效性：决策树、R andom森林、k近邻(KNN)、AdaBoost、XGBoost和长短期记忆(LSTM)。”新闻编辑们从穆罕默德五世大学的研究中引用了一句话：“我们使用了包含滞后气象变量的多变量时间序列模型来捕捉摩洛哥拉巴特1993年至2018年的月降雨量动态。这些模型是根据各种指标进行评估的，包括Roo t均方误差(RMSE)，平均绝对误差(MAE)，以及XGBoost表现最好,RMSE为40.8(mm),而决策树、AdaBoost、Random Fo Rest、LSTM和KNN表现较差,具体RMSE从47.5(mm)提高到51(mm)。为各种ML策略提供了一个新的视角。这种集成算法旨在充分利用每个单独模型的优势，旨在大幅提高降水预报的精度。最好的结果是将决策树、KNN和LSTM结合起来建立MET A库，同时使用XGBoost作为二级学习者。
Abstract
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.”
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