Stochastic dynamic programming and machine learning under climate change for reservoir and irrigation operations

Anthropogenic climate change potentially causes water shortages over different spatial and temporal scales. Over the coming decades, the impact of climate change would be tangible, with significant increases in the global mean temperature, changes in the frequency and intensity of precipitation, and...

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Main Author:	Mohd. Nasir, Muhammad Adib
Format:	Thesis
Language:	English
Published:	2022
Subjects:	TA Engineering (General) Civil engineering (General)
Online Access:	http://eprints.utm.my/id/eprint/101430/1/MuhammadAdibMohdNasirPSKA2023.pdf.pdf
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spelling	my-utm-ep.1014302023-06-14T10:18:57Z Stochastic dynamic programming and machine learning under climate change for reservoir and irrigation operations 2022 Mohd. Nasir, Muhammad Adib TA Engineering (General). Civil engineering (General) Anthropogenic climate change potentially causes water shortages over different spatial and temporal scales. Over the coming decades, the impact of climate change would be tangible, with significant increases in the global mean temperature, changes in the frequency and intensity of precipitation, and rising sea levels. These changes will adversely affect the water resource system due to the increased severity of floods, droughts, timing and amount of runoff and evaporation. The reservoir systems require continuous development and revision for optimal operations to deal with the variability of future climate change. Therefore, this study attempted to develop optimal reservoir operation under the new realities of climate change in a tropical agro-hydrological watershed in Perak, Malaysia. Reservoir inflow variability due to climate change affects hydrological processes and irrigation demands at a basin scale. Meta-learning, an ensemble machine learning technique using support vector regression (SVR) and random forest (RF) coupled with the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-Global Climate Model (multi-GCM), was applied to investigate the impacts of climate change on Kurau River. Five GCMs (CanESM5, MPI-ESM1-2-LR, MRI-ESM2-0, NESM3, and NorESM2-LM) and three shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) were used. The climate sequences generated by the delta change factor method were applied as input to the meta-learning model to predict the streamflow (reservoir inflow) changes from 2021-2080. The model fitted reasonably well, with Kling–Gupta efficiency (KGE), Nash-Sutcliffe efficiency (NSE), percent bias (PBias), and Root Mean Square Error (RMSE) of 0.79, 0.83, 2.52, and 4.51, respectively, for the training period (1976-1995) and 0.72, 0.72, 5.85, and 6.90, respectively, for the testing period (1995-2005). Future projections of multi-GCM over the 2021-2080 under three SSPs predicted an increase in rainfall for all months except April-June (dry period or off-season), with a higher increase during the wet period (main-season). Temperature projections indicated an increase in maximum and minimum temperatures under all SSPs, with a higher increase of approximately 2.0°C under SSP5-8.5 during 2051-2080 relative to the baseline period of 1976-2005. The model predicted seasonal changes in the inflow by -7.5 to 7.1% and 1.2 to 5.9% during the off-season and the main-season, respectively. A significant inflow decrease was predicted in April and May for all SSPs due to high temperatures during the off-season, with SSP5-8.5 being the worst. The future rice irrigation demand changes for the Kerian Irrigation Scheme compared to the baseline period for two planting periods by -1.0 to 0.1% and -5.3 to -2.6% during the off-season and main-season, respectively. A significant irrigation water demand decrease is predicted in September and October for all SSPs due to increased rainfall during the main-season, with SSP5-8.5 being the most prominent. The stochastic dynamic programming is applied to determine the optimal release policies for Bukit Merah Reservoir considering future climate variability (15 combinations of different GCMs and SSPs for two future periods). The rule curve patterns varied under different scenarios and future periods. The patterns revealed that the reservoir will suffer from tremendous water stress in the far future (2051-2080) than in the near future (2021-2050) and significantly during the off-season. 2022 Thesis http://eprints.utm.my/id/eprint/101430/ http://eprints.utm.my/id/eprint/101430/1/MuhammadAdibMohdNasirPSKA2023.pdf.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:151566 phd doctoral Universiti Teknologi Malaysia Faculty of Engineering - School of Civil Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TA Engineering (General) Civil engineering (General)
spellingShingle	TA Engineering (General) Civil engineering (General) Mohd. Nasir, Muhammad Adib Stochastic dynamic programming and machine learning under climate change for reservoir and irrigation operations
description	Anthropogenic climate change potentially causes water shortages over different spatial and temporal scales. Over the coming decades, the impact of climate change would be tangible, with significant increases in the global mean temperature, changes in the frequency and intensity of precipitation, and rising sea levels. These changes will adversely affect the water resource system due to the increased severity of floods, droughts, timing and amount of runoff and evaporation. The reservoir systems require continuous development and revision for optimal operations to deal with the variability of future climate change. Therefore, this study attempted to develop optimal reservoir operation under the new realities of climate change in a tropical agro-hydrological watershed in Perak, Malaysia. Reservoir inflow variability due to climate change affects hydrological processes and irrigation demands at a basin scale. Meta-learning, an ensemble machine learning technique using support vector regression (SVR) and random forest (RF) coupled with the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-Global Climate Model (multi-GCM), was applied to investigate the impacts of climate change on Kurau River. Five GCMs (CanESM5, MPI-ESM1-2-LR, MRI-ESM2-0, NESM3, and NorESM2-LM) and three shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) were used. The climate sequences generated by the delta change factor method were applied as input to the meta-learning model to predict the streamflow (reservoir inflow) changes from 2021-2080. The model fitted reasonably well, with Kling–Gupta efficiency (KGE), Nash-Sutcliffe efficiency (NSE), percent bias (PBias), and Root Mean Square Error (RMSE) of 0.79, 0.83, 2.52, and 4.51, respectively, for the training period (1976-1995) and 0.72, 0.72, 5.85, and 6.90, respectively, for the testing period (1995-2005). Future projections of multi-GCM over the 2021-2080 under three SSPs predicted an increase in rainfall for all months except April-June (dry period or off-season), with a higher increase during the wet period (main-season). Temperature projections indicated an increase in maximum and minimum temperatures under all SSPs, with a higher increase of approximately 2.0°C under SSP5-8.5 during 2051-2080 relative to the baseline period of 1976-2005. The model predicted seasonal changes in the inflow by -7.5 to 7.1% and 1.2 to 5.9% during the off-season and the main-season, respectively. A significant inflow decrease was predicted in April and May for all SSPs due to high temperatures during the off-season, with SSP5-8.5 being the worst. The future rice irrigation demand changes for the Kerian Irrigation Scheme compared to the baseline period for two planting periods by -1.0 to 0.1% and -5.3 to -2.6% during the off-season and main-season, respectively. A significant irrigation water demand decrease is predicted in September and October for all SSPs due to increased rainfall during the main-season, with SSP5-8.5 being the most prominent. The stochastic dynamic programming is applied to determine the optimal release policies for Bukit Merah Reservoir considering future climate variability (15 combinations of different GCMs and SSPs for two future periods). The rule curve patterns varied under different scenarios and future periods. The patterns revealed that the reservoir will suffer from tremendous water stress in the far future (2051-2080) than in the near future (2021-2050) and significantly during the off-season.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Mohd. Nasir, Muhammad Adib
author_facet	Mohd. Nasir, Muhammad Adib
author_sort	Mohd. Nasir, Muhammad Adib
title	Stochastic dynamic programming and machine learning under climate change for reservoir and irrigation operations
title_short	Stochastic dynamic programming and machine learning under climate change for reservoir and irrigation operations
title_full	Stochastic dynamic programming and machine learning under climate change for reservoir and irrigation operations
title_fullStr	Stochastic dynamic programming and machine learning under climate change for reservoir and irrigation operations
title_full_unstemmed	Stochastic dynamic programming and machine learning under climate change for reservoir and irrigation operations
title_sort	stochastic dynamic programming and machine learning under climate change for reservoir and irrigation operations
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Engineering - School of Civil Engineering
publishDate	2022
url	http://eprints.utm.my/id/eprint/101430/1/MuhammadAdibMohdNasirPSKA2023.pdf.pdf
_version_	1776100698331545600

Stochastic dynamic programming and machine learning under climate change for reservoir and irrigation operations

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