Machine learning algorithms for the prediction of drought conditions in the Wami River sub-catchment, Tanzania
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Date
2024-04-16
Journal Title
Journal ISSN
Volume Title
Publisher
SPRINGER
Abstract
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.
Description
Journal of Hydrology: Regional Studies 53 (2024) 101794
Keywords
Drought, Prediction, Machine learning, Rainfall, Wami River sub-catchmen
Citation
www.elsevier.com/locate/ejrh