SPI-Based Hybrid Hidden Markov–GA, ARIMA–GA, and ARIMA–GA–ANN Models for Meteorological Drought Forecasting

Drought is a severe environmental disaster that results in significant social and economic damage. As such, efficient mitigation plans must rely on precise modeling and forecasting of the phenomenon. This study was designed to enhance drought forecasting through developing and evaluating the applica...

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Autores principales:	Mohammed Alquraish, Khaled Ali. Abuhasel, Abdulrahman S. Alqahtani, Mosaad Khadr
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	drought forecasting SPI genetic algorithm HMM ARIMA Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/c6c7478596b64c1c8118cc99c6b876d2
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spelling	oai:doaj.org-article:c6c7478596b64c1c8118cc99c6b876d22021-11-25T19:02:12ZSPI-Based Hybrid Hidden Markov–GA, ARIMA–GA, and ARIMA–GA–ANN Models for Meteorological Drought Forecasting10.3390/su1322125762071-1050https://doaj.org/article/c6c7478596b64c1c8118cc99c6b876d22021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/22/12576https://doaj.org/toc/2071-1050Drought is a severe environmental disaster that results in significant social and economic damage. As such, efficient mitigation plans must rely on precise modeling and forecasting of the phenomenon. This study was designed to enhance drought forecasting through developing and evaluating the applicability of three hybrid models—the hidden Markov model–genetic algorithm (HMM–GA), the auto-regressive integrated moving average–genetic algorithm (ARIMA–GA), and a novel auto-regressive integrated moving average–genetic algorithm–ANN (ARIMA–GA–ANN)—to forecast the standard precipitation index (SPI) in the Bisha Valley, Saudi Arabia. The accuracy of the models was investigated and compared with that of classical HMM and ARIMA based on a performance evaluation and visual inspection. Furthermore, the multi-class Receiver Operating Characteristic-based Area under the Curve (ROC–AUC) was applied to evaluate the ability of the hybrid model to forecast drought events. We used data from 1968 to 2008 to train the models and data from 2009 to 2019 for validation. The performance evaluation results confirmed that the hybrid models provided superior results in forecasting the SPI one month in advance. Furthermore, the results demonstrated that the GA-induced improvement in the HMM forecasts was matched by an approximate 16.40% and 23.46% decrease in the RMSE in the training and testing results, respectively, compared to the classical HMM model. Consequently, the RMSE values of the ARIMA–GA model were reduced by an average of 10.06% and 9.36% for the training and testing processes, respectively. Finally, the ARIMA–GA–ANN, which combined the strengths of the linear stochastic model ARIMA and a non-linear ANN, achieved a greater reduction values in RMSE by an average of 32.82% and 27.47% in comparison with ARIMA in the training and testing phases, respectively. The ROC–AUC results confirmed the capability of the developed models to distinguish between events and non-events with reasonable accuracy, implying the appropriateness of these models as a tool for drought mitigation and warning systems.Mohammed AlquraishKhaled Ali. AbuhaselAbdulrahman S. AlqahtaniMosaad KhadrMDPI AGarticledrought forecastingSPIgenetic algorithmHMMARIMAEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12576, p 12576 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	drought forecasting SPI genetic algorithm HMM ARIMA Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	drought forecasting SPI genetic algorithm HMM ARIMA Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Mohammed Alquraish Khaled Ali. Abuhasel Abdulrahman S. Alqahtani Mosaad Khadr SPI-Based Hybrid Hidden Markov–GA, ARIMA–GA, and ARIMA–GA–ANN Models for Meteorological Drought Forecasting
description	Drought is a severe environmental disaster that results in significant social and economic damage. As such, efficient mitigation plans must rely on precise modeling and forecasting of the phenomenon. This study was designed to enhance drought forecasting through developing and evaluating the applicability of three hybrid models—the hidden Markov model–genetic algorithm (HMM–GA), the auto-regressive integrated moving average–genetic algorithm (ARIMA–GA), and a novel auto-regressive integrated moving average–genetic algorithm–ANN (ARIMA–GA–ANN)—to forecast the standard precipitation index (SPI) in the Bisha Valley, Saudi Arabia. The accuracy of the models was investigated and compared with that of classical HMM and ARIMA based on a performance evaluation and visual inspection. Furthermore, the multi-class Receiver Operating Characteristic-based Area under the Curve (ROC–AUC) was applied to evaluate the ability of the hybrid model to forecast drought events. We used data from 1968 to 2008 to train the models and data from 2009 to 2019 for validation. The performance evaluation results confirmed that the hybrid models provided superior results in forecasting the SPI one month in advance. Furthermore, the results demonstrated that the GA-induced improvement in the HMM forecasts was matched by an approximate 16.40% and 23.46% decrease in the RMSE in the training and testing results, respectively, compared to the classical HMM model. Consequently, the RMSE values of the ARIMA–GA model were reduced by an average of 10.06% and 9.36% for the training and testing processes, respectively. Finally, the ARIMA–GA–ANN, which combined the strengths of the linear stochastic model ARIMA and a non-linear ANN, achieved a greater reduction values in RMSE by an average of 32.82% and 27.47% in comparison with ARIMA in the training and testing phases, respectively. The ROC–AUC results confirmed the capability of the developed models to distinguish between events and non-events with reasonable accuracy, implying the appropriateness of these models as a tool for drought mitigation and warning systems.
format	article
author	Mohammed Alquraish Khaled Ali. Abuhasel Abdulrahman S. Alqahtani Mosaad Khadr
author_facet	Mohammed Alquraish Khaled Ali. Abuhasel Abdulrahman S. Alqahtani Mosaad Khadr
author_sort	Mohammed Alquraish
title	SPI-Based Hybrid Hidden Markov–GA, ARIMA–GA, and ARIMA–GA–ANN Models for Meteorological Drought Forecasting
title_short	SPI-Based Hybrid Hidden Markov–GA, ARIMA–GA, and ARIMA–GA–ANN Models for Meteorological Drought Forecasting
title_full	SPI-Based Hybrid Hidden Markov–GA, ARIMA–GA, and ARIMA–GA–ANN Models for Meteorological Drought Forecasting
title_fullStr	SPI-Based Hybrid Hidden Markov–GA, ARIMA–GA, and ARIMA–GA–ANN Models for Meteorological Drought Forecasting
title_full_unstemmed	SPI-Based Hybrid Hidden Markov–GA, ARIMA–GA, and ARIMA–GA–ANN Models for Meteorological Drought Forecasting
title_sort	spi-based hybrid hidden markov–ga, arima–ga, and arima–ga–ann models for meteorological drought forecasting
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/c6c7478596b64c1c8118cc99c6b876d2
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SPI-Based Hybrid Hidden Markov–GA, ARIMA–GA, and ARIMA–GA–ANN Models for Meteorological Drought Forecasting

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