Version 1
: Received: 18 August 2024 / Approved: 20 August 2024 / Online: 20 August 2024 (16:24:17 CEST)
How to cite:
Li, W.; Finsa, M. M.; Laskey, K. B.; Houser, P.; Douglas-Bate, R.; Verner, K. Optimizing Well Placement for Sustainable Irrigation: A Two-Stage Stochastic Mixed Integer Programming Approach. Preprints2024, 2024081431. https://doi.org/10.20944/preprints202408.1431.v1
Li, W.; Finsa, M. M.; Laskey, K. B.; Houser, P.; Douglas-Bate, R.; Verner, K. Optimizing Well Placement for Sustainable Irrigation: A Two-Stage Stochastic Mixed Integer Programming Approach. Preprints 2024, 2024081431. https://doi.org/10.20944/preprints202408.1431.v1
Li, W.; Finsa, M. M.; Laskey, K. B.; Houser, P.; Douglas-Bate, R.; Verner, K. Optimizing Well Placement for Sustainable Irrigation: A Two-Stage Stochastic Mixed Integer Programming Approach. Preprints2024, 2024081431. https://doi.org/10.20944/preprints202408.1431.v1
APA Style
Li, W., Finsa, M. M., Laskey, K. B., Houser, P., Douglas-Bate, R., & Verner, K. (2024). Optimizing Well Placement for Sustainable Irrigation: A Two-Stage Stochastic Mixed Integer Programming Approach. Preprints. https://doi.org/10.20944/preprints202408.1431.v1
Chicago/Turabian Style
Li, W., Rupert Douglas-Bate and Kryštof Verner. 2024 "Optimizing Well Placement for Sustainable Irrigation: A Two-Stage Stochastic Mixed Integer Programming Approach" Preprints. https://doi.org/10.20944/preprints202408.1431.v1
Abstract
Utilizing groundwater offers a promising solution to alleviate water stress in Ethiopia, providing a dependable and sustainable water source, particularly in regions with limited or unreliable surface water availability. However, effective decision-making regarding well drilling and placement is essential to maximize groundwater resource potential, enhancing agricultural productivity, reducing hunger, and bolstering food security in Ethiopia. This study concentrates on the development of two-stage stochastic mixed integer programming (SMIP) models to optimize well placement for sustainable agricultural irrigation, considering uncertain demand scenarios. Additionally, a deterministic mixed integer programming model is formulated for comparison with the two-stage SMIP. Experiments are conducted to explore various demand scenario distributions, revealing that the optimized total cost for the two-stage SMIP generally exceeds that of a deterministic setting, aligning with the two-stage SMIP's focus on long-term benefits. Moreover, slight differences are observed in well layouts under different assumption scenarios. The study also examines the impact of selected parameters, such as fixed construction costs, per-meter drilling costs, and demand scenarios. The out-of-sample performance shows that the stochastic model is more flexible and resilient, with 11% and 4% lower costs than deterministic cases 1 and 3, respectively. This flexibility provides a more robust long-term strategy for well placement and resource allocation in groundwater management.
Environmental and Earth Sciences, Water Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
,
,
,
