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Machine learning algorithms with hydro-meteorological data for monthly streamflow forecasting of Kurau River, Malaysia

Monthly streamflow forecasting is crucial in water resources management to assess the possible future streamflow patterns. It becomes vital where streamflow of Kurau River is the primary water source to irrigate the large-scale rice scheme of Kerian, Perak, coupled with future climate change uncerta...

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Main Authors: Mohd. Adib, Muhammad Nasir, Harun, Sobri
Format: Conference or Workshop Item
Published: 2023
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/108110/
http://dx.doi.org/10.1007/978-981-99-3577-2_3
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spelling my.utm.1081102024-10-17T06:13:17Z http://eprints.utm.my/108110/ Machine learning algorithms with hydro-meteorological data for monthly streamflow forecasting of Kurau River, Malaysia Mohd. Adib, Muhammad Nasir Harun, Sobri TA Engineering (General). Civil engineering (General) Monthly streamflow forecasting is crucial in water resources management to assess the possible future streamflow patterns. It becomes vital where streamflow of Kurau River is the primary water source to irrigate the large-scale rice scheme of Kerian, Perak, coupled with future climate change uncertainty. In this context, machine learning algorithms have received outstanding attention due to their high accuracy in forecasting through high-speed input–output data processing of self-learning from physical processes. In this study, two machine learning algorithms, support vector regression (SVR) and random forest (RF), were considered to forecast the streamflow of Kurau River in Malaysia using gauged hydro-meteorological dataset for the period from 1976 to 2005. The predictions of monthly streamflows were based on hydro-meteorological data such as rainfall, minimum and maximum temperature, relative humidity, and wind speed. A comparative study is executed to evaluate the efficiency of SVR and RF in performing the streamflow predictions of Kurau River. The results show that RF outperformed the SVR in both the training and testing phases. The results have proven that machine learning algorithms, especially the RF model, can be implemented for forecasting streamflow by using only hydro-meteorological data with high accuracy, which will improve future water resources management. 2023 Conference or Workshop Item PeerReviewed Mohd. Adib, Muhammad Nasir and Harun, Sobri (2023) Machine learning algorithms with hydro-meteorological data for monthly streamflow forecasting of Kurau River, Malaysia. In: 5th International Conference on Water Resources, ICWR 2021, 23 November 2021 - 25 November 2021, Virtual, UTM Johor Bahru, Johor, Malaysia. http://dx.doi.org/10.1007/978-981-99-3577-2_3
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Mohd. Adib, Muhammad Nasir
Harun, Sobri
Machine learning algorithms with hydro-meteorological data for monthly streamflow forecasting of Kurau River, Malaysia
description Monthly streamflow forecasting is crucial in water resources management to assess the possible future streamflow patterns. It becomes vital where streamflow of Kurau River is the primary water source to irrigate the large-scale rice scheme of Kerian, Perak, coupled with future climate change uncertainty. In this context, machine learning algorithms have received outstanding attention due to their high accuracy in forecasting through high-speed input–output data processing of self-learning from physical processes. In this study, two machine learning algorithms, support vector regression (SVR) and random forest (RF), were considered to forecast the streamflow of Kurau River in Malaysia using gauged hydro-meteorological dataset for the period from 1976 to 2005. The predictions of monthly streamflows were based on hydro-meteorological data such as rainfall, minimum and maximum temperature, relative humidity, and wind speed. A comparative study is executed to evaluate the efficiency of SVR and RF in performing the streamflow predictions of Kurau River. The results show that RF outperformed the SVR in both the training and testing phases. The results have proven that machine learning algorithms, especially the RF model, can be implemented for forecasting streamflow by using only hydro-meteorological data with high accuracy, which will improve future water resources management.
format Conference or Workshop Item
author Mohd. Adib, Muhammad Nasir
Harun, Sobri
author_facet Mohd. Adib, Muhammad Nasir
Harun, Sobri
author_sort Mohd. Adib, Muhammad Nasir
title Machine learning algorithms with hydro-meteorological data for monthly streamflow forecasting of Kurau River, Malaysia
title_short Machine learning algorithms with hydro-meteorological data for monthly streamflow forecasting of Kurau River, Malaysia
title_full Machine learning algorithms with hydro-meteorological data for monthly streamflow forecasting of Kurau River, Malaysia
title_fullStr Machine learning algorithms with hydro-meteorological data for monthly streamflow forecasting of Kurau River, Malaysia
title_full_unstemmed Machine learning algorithms with hydro-meteorological data for monthly streamflow forecasting of Kurau River, Malaysia
title_sort machine learning algorithms with hydro-meteorological data for monthly streamflow forecasting of kurau river, malaysia
publishDate 2023
url http://eprints.utm.my/108110/
http://dx.doi.org/10.1007/978-981-99-3577-2_3
_version_ 1814043602967330816
score 13.211869

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