Machine learning models development for accurate multi-months ahead drought forecasting: Case study of the Great Lakes, North America.
The Great Lakes are critical freshwater sources, supporting millions of people, agriculture, and ecosystems. However, climate change has worsened droughts, leading to significant economic and social consequences. Accurate multi-month drought forecasting is, therefore, essential for effective water m...
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ftdoajarticles:oai:doaj.org/article:504eb0e49a8143c68ea86bce1306006a 2023-12-10T09:47:14+01:00 Machine learning models development for accurate multi-months ahead drought forecasting: Case study of the Great Lakes, North America. Mohammed Majeed Hameed Siti Fatin Mohd Razali Wan Hanna Melini Wan Mohtar Norinah Abd Rahman Zaher Mundher Yaseen 2023-01-01T00:00:00Z https://doi.org/10.1371/journal.pone.0290891 https://doaj.org/article/504eb0e49a8143c68ea86bce1306006a EN eng Public Library of Science (PLoS) https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0290891&type=printable https://doaj.org/toc/1932-6203 1932-6203 doi:10.1371/journal.pone.0290891 https://doaj.org/article/504eb0e49a8143c68ea86bce1306006a PLoS ONE, Vol 18, Iss 10, p e0290891 (2023) Medicine R Science Q article 2023 ftdoajarticles https://doi.org/10.1371/journal.pone.0290891 2023-11-12T01:39:50Z The Great Lakes are critical freshwater sources, supporting millions of people, agriculture, and ecosystems. However, climate change has worsened droughts, leading to significant economic and social consequences. Accurate multi-month drought forecasting is, therefore, essential for effective water management and mitigating these impacts. This study introduces the Multivariate Standardized Lake Water Level Index (MSWI), a modified drought index that utilizes water level data collected from 1920 to 2020. Four hybrid models are developed: Support Vector Regression with Beluga whale optimization (SVR-BWO), Random Forest with Beluga whale optimization (RF-BWO), Extreme Learning Machine with Beluga whale optimization (ELM-BWO), and Regularized ELM with Beluga whale optimization (RELM-BWO). The models forecast droughts up to six months ahead for Lake Superior and Lake Michigan-Huron. The best-performing model is then selected to forecast droughts for the remaining three lakes, which have not experienced severe droughts in the past 50 years. The results show that incorporating the BWO improves the accuracy of all classical models, particularly in forecasting drought turning and critical points. Among the hybrid models, the RELM-BWO model achieves the highest level of accuracy, surpassing both classical and hybrid models by a significant margin (7.21 to 76.74%). Furthermore, Monte-Carlo simulation is employed to analyze uncertainties and ensure the reliability of the forecasts. Accordingly, the RELM-BWO model reliably forecasts droughts for all lakes, with a lead time ranging from 2 to 6 months. The study's findings offer valuable insights for policymakers, water managers, and other stakeholders to better prepare drought mitigation strategies. Article in Journal/Newspaper Beluga Beluga whale Beluga* Directory of Open Access Journals: DOAJ Articles PLOS ONE 18 10 e0290891 |
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Medicine R Science Q Mohammed Majeed Hameed Siti Fatin Mohd Razali Wan Hanna Melini Wan Mohtar Norinah Abd Rahman Zaher Mundher Yaseen Machine learning models development for accurate multi-months ahead drought forecasting: Case study of the Great Lakes, North America. |
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Medicine R Science Q |
description |
The Great Lakes are critical freshwater sources, supporting millions of people, agriculture, and ecosystems. However, climate change has worsened droughts, leading to significant economic and social consequences. Accurate multi-month drought forecasting is, therefore, essential for effective water management and mitigating these impacts. This study introduces the Multivariate Standardized Lake Water Level Index (MSWI), a modified drought index that utilizes water level data collected from 1920 to 2020. Four hybrid models are developed: Support Vector Regression with Beluga whale optimization (SVR-BWO), Random Forest with Beluga whale optimization (RF-BWO), Extreme Learning Machine with Beluga whale optimization (ELM-BWO), and Regularized ELM with Beluga whale optimization (RELM-BWO). The models forecast droughts up to six months ahead for Lake Superior and Lake Michigan-Huron. The best-performing model is then selected to forecast droughts for the remaining three lakes, which have not experienced severe droughts in the past 50 years. The results show that incorporating the BWO improves the accuracy of all classical models, particularly in forecasting drought turning and critical points. Among the hybrid models, the RELM-BWO model achieves the highest level of accuracy, surpassing both classical and hybrid models by a significant margin (7.21 to 76.74%). Furthermore, Monte-Carlo simulation is employed to analyze uncertainties and ensure the reliability of the forecasts. Accordingly, the RELM-BWO model reliably forecasts droughts for all lakes, with a lead time ranging from 2 to 6 months. The study's findings offer valuable insights for policymakers, water managers, and other stakeholders to better prepare drought mitigation strategies. |
format |
Article in Journal/Newspaper |
author |
Mohammed Majeed Hameed Siti Fatin Mohd Razali Wan Hanna Melini Wan Mohtar Norinah Abd Rahman Zaher Mundher Yaseen |
author_facet |
Mohammed Majeed Hameed Siti Fatin Mohd Razali Wan Hanna Melini Wan Mohtar Norinah Abd Rahman Zaher Mundher Yaseen |
author_sort |
Mohammed Majeed Hameed |
title |
Machine learning models development for accurate multi-months ahead drought forecasting: Case study of the Great Lakes, North America. |
title_short |
Machine learning models development for accurate multi-months ahead drought forecasting: Case study of the Great Lakes, North America. |
title_full |
Machine learning models development for accurate multi-months ahead drought forecasting: Case study of the Great Lakes, North America. |
title_fullStr |
Machine learning models development for accurate multi-months ahead drought forecasting: Case study of the Great Lakes, North America. |
title_full_unstemmed |
Machine learning models development for accurate multi-months ahead drought forecasting: Case study of the Great Lakes, North America. |
title_sort |
machine learning models development for accurate multi-months ahead drought forecasting: case study of the great lakes, north america. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2023 |
url |
https://doi.org/10.1371/journal.pone.0290891 https://doaj.org/article/504eb0e49a8143c68ea86bce1306006a |
genre |
Beluga Beluga whale Beluga* |
genre_facet |
Beluga Beluga whale Beluga* |
op_source |
PLoS ONE, Vol 18, Iss 10, p e0290891 (2023) |
op_relation |
https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0290891&type=printable https://doaj.org/toc/1932-6203 1932-6203 doi:10.1371/journal.pone.0290891 https://doaj.org/article/504eb0e49a8143c68ea86bce1306006a |
op_doi |
https://doi.org/10.1371/journal.pone.0290891 |
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PLOS ONE |
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