首页|Researchers at Sokoine University of Agriculture Publish New Data on Machine Lea rning (Machine learning algorithms for the prediction of drought conditions in t he Wami River sub-catchment, Tanzania)
Researchers at Sokoine University of Agriculture Publish New Data on Machine Lea rning (Machine learning algorithms for the prediction of drought conditions in t he Wami River sub-catchment, Tanzania)
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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."
Sokoine University of AgricultureMorog oroTanzaniaAfricaAlgorithmsCyborgsEmerging TechnologiesMachine Learn ing
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