气候变化领域集成服务门户(CCPortal): Machine learning algorithms for the prediction of drought conditions in the Wami River sub-catchment, Tanzania

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DOI	10.1016/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
EISSN	2214-5818
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
推荐引用方式 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).