TY - JOUR
T1 - A methodological framework for agricultural water allocation problem solving using Multistage Stochastic Programming techniques based on a Systematic Literature Review
AU - Márquez González, Juan David
AU - Talero-Sarmiento, Leonardo
A2 - Lamos-Díaz, Henry
PY - 2023/12/18
Y1 - 2023/12/18
N2 - Water allocation represents an important and active research area. It addresses the problem of distributing water precisely and efficiently, considering the multiple factors that influence water demand, such as users' needs, economic effects, and regulatory policies. In agricultural activities, making decisions about water management is critical, as they can heighten the risk and uncertainty of water availability and influence the proper allocation of available water resources in typical farm practices. Therefore, this paper aims at constructing a robust water allocation modeling framework for agriculture centered on reservoir managers and farmers considering uncertain conditions by systematically reviewing the existing literature on Multi-Stage Programming (MSP) and its applications in light of the current growth of data-driven models. This study used the PRISMA statement and Snowball Sampling Methodology, analyzing peer-reviewed case study articles from 2000-2021. The results show a higher inclination toward using Two-Stage Stochastic Programming (TSP) instead of MSP in agricultural water allocation, considering TSP offers a low-cost but less flexible application than MSP. Conversely, hybrid optimization strategies offer better modeling approaches for uncertainty and problem constraints. This research puts forward a proposal direction highlighting seven critical areas for future studies: pursuing multiple optimization objectives, examining multiple uncertainty sources, investigating different uncertainty forms, incorporating other external factors, developing more effective solution strategies, employing hybrid strategies, and utilizing advanced technology solutions. Finally, this study presents a framework as a comprehensive understanding and guiding scheme for developing optimal agricultural water allocation plans under uncertain conditions, thus making a significant contribution to sustainable water management in agriculture.
AB - Water allocation represents an important and active research area. It addresses the problem of distributing water precisely and efficiently, considering the multiple factors that influence water demand, such as users' needs, economic effects, and regulatory policies. In agricultural activities, making decisions about water management is critical, as they can heighten the risk and uncertainty of water availability and influence the proper allocation of available water resources in typical farm practices. Therefore, this paper aims at constructing a robust water allocation modeling framework for agriculture centered on reservoir managers and farmers considering uncertain conditions by systematically reviewing the existing literature on Multi-Stage Programming (MSP) and its applications in light of the current growth of data-driven models. This study used the PRISMA statement and Snowball Sampling Methodology, analyzing peer-reviewed case study articles from 2000-2021. The results show a higher inclination toward using Two-Stage Stochastic Programming (TSP) instead of MSP in agricultural water allocation, considering TSP offers a low-cost but less flexible application than MSP. Conversely, hybrid optimization strategies offer better modeling approaches for uncertainty and problem constraints. This research puts forward a proposal direction highlighting seven critical areas for future studies: pursuing multiple optimization objectives, examining multiple uncertainty sources, investigating different uncertainty forms, incorporating other external factors, developing more effective solution strategies, employing hybrid strategies, and utilizing advanced technology solutions. Finally, this study presents a framework as a comprehensive understanding and guiding scheme for developing optimal agricultural water allocation plans under uncertain conditions, thus making a significant contribution to sustainable water management in agriculture.
KW - Literature review
KW - Water management
KW - Framework
KW - Water allocation
KW - Uncertainty
KW - Stochastic Programmig
KW - Stochastic Optimization
UR - https://www.mdpi.com/2673-4583/10/1/26
M3 - Scientific Article
SN - 2673-4583
JO - Chemistry Proceedings
JF - Chemistry Proceedings
ER -