Metaheuristic optimization of water resources: A case study of the Manas River irrigation district

元启发式 水资源管理 灌溉 水资源 水文学(农业) 灌区 环境科学 地质学 计算机科学 生态学 岩土工程 算法 生物
作者
Yue Pan,Hao Tian,Muhammad Arsalan Farid,Xinlin He,Tong Heng,Cecilie Hermansen,Lis Wollesen de Jonge,Fadong Li,Yongli Gao,Lijun Tian,Guang Yang
出处
期刊:Journal of Hydrology [Elsevier BV]
卷期号:639: 131640-131640 被引量:4
标识
DOI:10.1016/j.jhydrol.2024.131640
摘要

Irrigated arid oasis areas experience shortages in water resources and imbalances between supply and demand. A rational water resources allocation strategy must be devised to solve such problems; however, this remains a challenging issue to overcome. In this study, a multi-objective water resources optimization model based on a metaheuristic algorithm was established for the Manas River irrigation area in Xinjiang. First, considering future population growth and the development of the ecological environment in arid oasis irrigation areas, a multi-objective water resource optimization allocation model was established. This model was developed to derive the maximum economic benefits from water supply allocation to users, improve the degree to which ecological water demand is met for ecological environmental restoration, and reduce water shortages. The model adheres to the constraints of the total water resources in this area and can be used to effectively solve future water resources supply and demand imbalances in the Manas River irrigation area. Second, a multi-objective beluga whale optimization algorithm was selected to solve multi-objective problems. In contrast to traditional optimization algorithms, the multi-objective beluga whale optimization algorithm does not rely on the knowledge of a specific problem domain, representing a more generalized approach. Instead, this algorithm provides a general framework for searching for solutions, finding an approximate optimal solution, and generating a multi-objective solution set, taking into account the model computation time and domain. Finally, the target solution set obtained after 100 iterations is used as the basis for identifying the optimal solution. The key findings of this study are as follows: (1) The solution sets obtained by applying the multi-objective beluga whale optimization algorithm to solve the multi-objective optimal allocation model for irrigation water resources in each subirrigation district (Shihezi, Mosouwan, and Xiayedi irrigation districts), for four distinct user categories (agriculture, industry, household, and ecological water), consistently adhered to the comprehensive water resources index of the irrigation district. (2) After employing the 2030 projections for the Shihezi irrigation district as an example, the binary comparison methodology helped ascertain the objective weights (0.43, 0.35, and 0.22). The multi-objective fuzzy preference model was then used to shift through the solution set, highlighting the solution with the highest degree of superiority (ui = 0.979) as the optimal solution. (3) Under this scenario, the economic objective of the optimal solution for the Shihezi irrigation district for 2030 is 14,912.91 million yuan, with social and ecological objectives of 1186.77 and 1.22 million m3, respectively. The results of this scenario can serve as a reference for decision-makers and provide a basis for optimal water resources allocation in arid oasis irrigation districts.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
顺心秋天完成签到,获得积分10
刚刚
红与黑完成签到,获得积分10
刚刚
刚刚
卡卡完成签到,获得积分10
1秒前
风吹小白菜完成签到,获得积分10
1秒前
liky完成签到 ,获得积分10
1秒前
鱼鱼完成签到,获得积分10
1秒前
辐睿完成签到,获得积分10
1秒前
xwwwww发布了新的文献求助10
1秒前
某某发布了新的文献求助10
1秒前
低调123完成签到,获得积分10
2秒前
2秒前
ss完成签到,获得积分10
2秒前
天天快乐应助ww采纳,获得10
2秒前
小杨发布了新的文献求助10
2秒前
水分子完成签到,获得积分10
2秒前
3秒前
列昂尼多夫娜完成签到,获得积分10
3秒前
MONNA完成签到 ,获得积分10
3秒前
4秒前
是阿龙呀发布了新的文献求助20
4秒前
5秒前
zzzzzz发布了新的文献求助10
5秒前
lucky完成签到 ,获得积分10
5秒前
zzz123完成签到 ,获得积分10
5秒前
tang完成签到 ,获得积分10
6秒前
gtpking发布了新的文献求助30
6秒前
xwwwww完成签到,获得积分20
6秒前
柳柳完成签到,获得积分10
6秒前
7秒前
小柯完成签到,获得积分10
7秒前
柚子茶应助H1998采纳,获得20
7秒前
马耳完成签到,获得积分10
7秒前
dde发布了新的文献求助200
7秒前
苗条平萱完成签到,获得积分10
8秒前
WYang完成签到,获得积分10
8秒前
5762完成签到,获得积分10
8秒前
huangxiaoniu完成签到,获得积分10
8秒前
8秒前
sfhdhjf发布了新的文献求助10
8秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Principles of town planning: translating concepts to applications 1000
Navigating Normative Orders. Interdisciplinary Perspectives 800
1 Peter and Christ's Descent to the Dead in Its Early Christian Reception 700
Organizational Behavior 510
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7739131
求助须知:如何正确求助?哪些是违规求助? 9288013
关于积分的说明 20186375
捐赠科研通 7317088
什么是DOI,文献DOI怎么找? 3306031
关于科研通互助平台的介绍 2458554
邀请新用户注册赠送积分活动 2315974