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。据来自坦桑尼亚莫罗戈罗的新闻报道，由NewsRx Journ Alists报道，研究称：“研究区域：本研究指坦桑尼亚东部瓦米河的子流域。”新闻记者从索科因农业大学的研究中获得了一句话：“研究焦点：五机器学习(ML)算法，包括长短期记忆(LSTM)、多元自适应回归样条(MARS)、支持向量机(SVM)、极限学习机(ELM)和M5树，用于预测最广泛使用的干旱指数，标准降水指数(SPI)，”在6个月和9个月时。使用1990年至2022年期间在瓦米河子流域分布的五个气象站的月降雨量数据建立了算法：巴雷加、达卡瓦、多多马、孔瓦和曼德拉。该地区的新水文见解。使用几个统计指标评估了所有五个ML算法的预测结果，包括皮尔逊相关系数®平均绝对误差(MAE)。结果表明,LSTM对干旱条件的预测效果较好,用SPI6(6个月SPI)和SPI9(9个月SPI),除孔洼站外,其余4个站的NSE最高,分别为0.99和0.99.孔洼站的RA介于0.75～0.99.之间。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on artificial in telligence. According to news reporting from Morogoro, Tanzania, by NewsRx journ alists, research stated, "Study region: This study refers to the Wami river sub- catchments in Eastern Tanzania." The news journalists obtained a quote from the research from Sokoine University of Agriculture: "Study Focus: The five-machine learning (ML) algorithms, includi ng long short-term memory (LSTM), multivariate adaptive regression spline (MARS) , support vector machine (SVM), extreme learning machine (ELM), and M5 Tree, wer e used to predict the most widely used drought index, the standard precipitation index (SPI), at six and nine months. Algorithms were established using monthly rainfall data for the period from 1990 to 2022 at five meteorological stations d istributed across the Wami River sub-catchment: Barega, Dakawa, Dodoma, Kongwa, and Mandera stations. New hydrological insights for the region. The predicted re sults of all five ML algorithms were evaluated using several statistical metrics , including Pearson's correlation coefficient ® mean absolute error (MAE), root mean square error (RMSE), and Nash Sutcliffe efficiency (NSE). The prediction r esults revealed that LSTM perform better in predicting drought conditions using SPI6 (6-month SPI) and SPI9 (9-month SPI) with the highest NSE of 0.99 in all fi ve stations, and R of 0.99 in four stations except at Kongwa station, where R ra nge from 0.75 to 0.99."
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