Toward rainfall prediction by machine learning in Perfume River Basin, Thua Thien Hue Province, Vietnam

This study examines rainfall forecasting for the Perfume (Huong) River basin using the machine learning method. To be precise, statistical measurement indicators are deployed to evaluate the reliability of the actual accumulated data. At the same time, this study applied and compared two popular mod...

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Autores principales:	Giang Nguyen Hong, Wang YuRen, Hieu Tran Dinh, Tho Quan Thanh, Phuong Le Anh, Tu Do Hoang Ngo
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	distance metric k-nn rainfall prediction multi-layer perceptron stochastic optimization Geology QE1-996.5
Acceso en línea:	https://doaj.org/article/61df9b7bf6644da3abe9a3295fa9ad64
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id	oai:doaj.org-article:61df9b7bf6644da3abe9a3295fa9ad64
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spelling	oai:doaj.org-article:61df9b7bf6644da3abe9a3295fa9ad642021-12-05T14:10:49ZToward rainfall prediction by machine learning in Perfume River Basin, Thua Thien Hue Province, Vietnam2391-544710.1515/geo-2020-0276https://doaj.org/article/61df9b7bf6644da3abe9a3295fa9ad642021-08-01T00:00:00Zhttps://doi.org/10.1515/geo-2020-0276https://doaj.org/toc/2391-5447This study examines rainfall forecasting for the Perfume (Huong) River basin using the machine learning method. To be precise, statistical measurement indicators are deployed to evaluate the reliability of the actual accumulated data. At the same time, this study applied and compared two popular models of multi-layer perceptron and the k-nearest neighbors (k-NN) with different configurations. The calculated rainfall data are obtained from the Hue, Aluoi, and Namdong hydrological stations, where the rainfall demonstrated a giant impact on the downstream from 1980 to 2018. This study result shows that both models, once fine-tuned properly, enjoyed the performance with standard metrics of R_squared, mean absolute error, Nash–Sutcliffe efficiency, and root-mean-square error. In particular, once Adam stochastic is deployed, the implementation of the MLP model is significantly improving. The promising forecast results encourage us to consider applying these models with future data to help natural disaster non-stop mitigation in the Perfume River basin.Giang Nguyen HongWang YuRenHieu Tran DinhTho Quan ThanhPhuong Le AnhTu Do Hoang NgoDe Gruyterarticledistance metrick-nnrainfall predictionmulti-layer perceptronstochastic optimizationGeologyQE1-996.5ENOpen Geosciences, Vol 13, Iss 1, Pp 963-976 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	distance metric k-nn rainfall prediction multi-layer perceptron stochastic optimization Geology QE1-996.5
spellingShingle	distance metric k-nn rainfall prediction multi-layer perceptron stochastic optimization Geology QE1-996.5 Giang Nguyen Hong Wang YuRen Hieu Tran Dinh Tho Quan Thanh Phuong Le Anh Tu Do Hoang Ngo Toward rainfall prediction by machine learning in Perfume River Basin, Thua Thien Hue Province, Vietnam
description	This study examines rainfall forecasting for the Perfume (Huong) River basin using the machine learning method. To be precise, statistical measurement indicators are deployed to evaluate the reliability of the actual accumulated data. At the same time, this study applied and compared two popular models of multi-layer perceptron and the k-nearest neighbors (k-NN) with different configurations. The calculated rainfall data are obtained from the Hue, Aluoi, and Namdong hydrological stations, where the rainfall demonstrated a giant impact on the downstream from 1980 to 2018. This study result shows that both models, once fine-tuned properly, enjoyed the performance with standard metrics of R_squared, mean absolute error, Nash–Sutcliffe efficiency, and root-mean-square error. In particular, once Adam stochastic is deployed, the implementation of the MLP model is significantly improving. The promising forecast results encourage us to consider applying these models with future data to help natural disaster non-stop mitigation in the Perfume River basin.
format	article
author	Giang Nguyen Hong Wang YuRen Hieu Tran Dinh Tho Quan Thanh Phuong Le Anh Tu Do Hoang Ngo
author_facet	Giang Nguyen Hong Wang YuRen Hieu Tran Dinh Tho Quan Thanh Phuong Le Anh Tu Do Hoang Ngo
author_sort	Giang Nguyen Hong
title	Toward rainfall prediction by machine learning in Perfume River Basin, Thua Thien Hue Province, Vietnam
title_short	Toward rainfall prediction by machine learning in Perfume River Basin, Thua Thien Hue Province, Vietnam
title_full	Toward rainfall prediction by machine learning in Perfume River Basin, Thua Thien Hue Province, Vietnam
title_fullStr	Toward rainfall prediction by machine learning in Perfume River Basin, Thua Thien Hue Province, Vietnam
title_full_unstemmed	Toward rainfall prediction by machine learning in Perfume River Basin, Thua Thien Hue Province, Vietnam
title_sort	toward rainfall prediction by machine learning in perfume river basin, thua thien hue province, vietnam
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/61df9b7bf6644da3abe9a3295fa9ad64
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Toward rainfall prediction by machine learning in Perfume River Basin, Thua Thien Hue Province, Vietnam

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