Version 1
: Received: 16 January 2023 / Approved: 30 January 2023 / Online: 30 January 2023 (13:01:38 CET)
Version 2
: Received: 18 February 2023 / Approved: 20 February 2023 / Online: 20 February 2023 (14:23:25 CET)
Mdegela, L.; Municio, E.; De Bock, Y.; Luhanga, E.; Leo, J.; Mannens, E. Extreme Rainfall Event Classification Using Machine Learning for Kikuletwa River Floods. Water2023, 15, 1021.
Mdegela, L.; Municio, E.; De Bock, Y.; Luhanga, E.; Leo, J.; Mannens, E. Extreme Rainfall Event Classification Using Machine Learning for Kikuletwa River Floods. Water 2023, 15, 1021.
Cite as:
Mdegela, L.; Municio, E.; De Bock, Y.; Luhanga, E.; Leo, J.; Mannens, E. Extreme Rainfall Event Classification Using Machine Learning for Kikuletwa River Floods. Water2023, 15, 1021.
Mdegela, L.; Municio, E.; De Bock, Y.; Luhanga, E.; Leo, J.; Mannens, E. Extreme Rainfall Event Classification Using Machine Learning for Kikuletwa River Floods. Water 2023, 15, 1021.
Abstract
Advancements in Machine Learning techniques, availability of more data-sets, and increased computing power have enabled a significant growth in a number research areas. Predicting, detecting and classifying complex events in earth systems which by nature are difficult to model is one of such areas. In this work, we investigate the application of different machine learning techniques for detecting and identifying extreme rainfall events in a sub-catchment within Pangani River Basin, found in Northern Tanzania. Identification and prediction of extreme rainfall event is a preliminary crucial task towards success in predicting rainfall-induced river floods. To identify a rain condition in the selected sub-catchment, we use data from five weather stations which have been labeled for the whole sub-catchment. In order to assess which Machine Learning technique suits better for rainfall identification, we apply five different algorithms in a historical dataset for the period of 1979 to 2014. We evaluate the performance of the models in terms of precision and recall, reporting Random Forest and XGBoost as the ones with best overall performance. However, since the class distribution is imbalanced, the generic Multi-layer Perceptron performs best when identifying the heavy rainfall events, which are eventually the main cause of rainfall-induced river floods in the Pangani River Basin
Keywords
Heavy rainfall; River floods; Machine learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
