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DOI10.1016/j.ejrh.2024.101794
Machine learning algorithms for the prediction of drought conditions in the Wami River sub-catchment, Tanzania
Lalika, Christossy; Mujahid, Aziz Ul Haq; James, Mturi; Lalika, Makarius C. S.
发表日期2024
EISSN2214-5818
起始页码53
卷号53
英文摘要Study region: This study refers to the Wami river sub-catchments in Eastern Tanzania. Study Focus: The five-machine learning (ML) algorithms, including long short-term memory (LSTM), multivariate adaptive regression spline (MARS), support vector machine (SVM), extreme learning machine (ELM), and M5 Tree, were 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 distributed across the Wami River sub-catchment: Barega, Dakawa, Dodoma, Kongwa, and Mandera stations. New hydrological insights for the region. The predicted results of all five ML algorithms were evaluated using several statistical metrics, including Pearson's correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), and Nash Sutcliffe efficiency (NSE). The prediction results 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 five stations, and R of 0.99 in four stations except at Kongwa station, where R range from 0.75 to 0.99. These prediction results will aid decision-makers and planners to develop a drought monitoring and drought early warning system in order to strengthen the governance and resilience to the catchment and people on the impacts of water scarcity and climate change.
英文关键词Drought; Prediction; Machine learning; Rainfall; Wami River sub -catchment
语种英语
WOS研究方向Water Resources
WOS类目Water Resources
WOS记录号WOS:001235673500001
来源期刊JOURNAL OF HYDROLOGY-REGIONAL STUDIES
文献类型期刊论文
条目标识符http://gcip.llas.ac.cn/handle/2XKMVOVA/296493
作者单位Sokoine University of Agriculture; Swiss Federal Institutes of Technology Domain; ETH Zurich; African Medical & Research Foundation (AMREF); Sokoine University of Agriculture
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GB/T 7714
Lalika, Christossy,Mujahid, Aziz Ul Haq,James, Mturi,et al. Machine learning algorithms for the prediction of drought conditions in the Wami River sub-catchment, Tanzania[J],2024,53.
APA Lalika, Christossy,Mujahid, Aziz Ul Haq,James, Mturi,&Lalika, Makarius C. S..(2024).Machine learning algorithms for the prediction of drought conditions in the Wami River sub-catchment, Tanzania.JOURNAL OF HYDROLOGY-REGIONAL STUDIES,53.
MLA Lalika, Christossy,et al."Machine learning algorithms for the prediction of drought conditions in the Wami River sub-catchment, Tanzania".JOURNAL OF HYDROLOGY-REGIONAL STUDIES 53(2024).
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