A multi-step approach to evaluate the sustainable use of groundwater resources for human consumption and agriculture

地下水补给 地下水 环境科学 水文地质学 农业 水文学(农业) 含水层 水质 水资源管理 地理信息系统 水资源 地质学 遥感 地理 生态学 岩土工程 考古 生物
作者
Mojgan Bordbar,Gianluigi Busico,Maurizio Sirna,Dario Tedesco,Micòl Mastrocicco
出处
期刊:Journal of Environmental Management [Elsevier]
卷期号:347: 119041-119041 被引量:10
标识
DOI:10.1016/j.jenvman.2023.119041
摘要

The rapid decline in both quality and availability of freshwater resources on our planet necessitates their thorough assessment to ensure sustainable usage. The growing demand for water in industrial, agricultural, and domestic sectors poses significant challenges to managing both surface and groundwater resources. This study tests and proposes a hybrid evaluation approach to determine Groundwater Quality Indices (GQIs) for irrigation (IRRI), seawater intrusion (SWI), and potability (POT), finalized to the spatial distribution of groundwater suitability involving water quality indicator along with hydrogeological and socio-economic factors. Mean Decrease Accuracy (MDA) and Information Gain Ratio (IGR) were used to state the importance of chosen factors such as level of groundwater above the sea, thickness of the aquifer, land cover, distance from coastline, silt soil content, recharge, distance from river and lagoons, depth to water table from ground, distance from agricultural wells, hydraulic conductivity, and lithology for each quality index, separately. The results of both methods showed that recharge is the most important parameter for GQIIRRI and GQIPOT, while the distance from the coastline and the rivers, are the most important for GQISWI. The spatial modelling of GQIIRRI and GQIPOT in the study area has been achieved applying three machine learning (ML) algorithms: the Boosted Regression Tree (BRT), the Random Forest (RF), and the Support Vector Machine (SVM). Validation results showed that RF has the highest prediction for GQIIRRI, while the SVM model has the highest prediction for the GQIPOT index. It is worth to mention that the future utilization and testing of new algorithms could produce even better results. Finally, GQIIRRI and GQIPOT were combined and compared using two combine and overlay methods to prepare a hybrid map of multi-GQIs. The results showed that 69% of the study area is suitable for irrigation and potable use, due to both geogenic and anthropogenic activities which contribute to make some water resources unsuitable for either use. Specifically, the northern, western, and eastern portions of the study area are in the "very high and high quality" classes while the southern portion shows "very low and low quality" classes. In conclusion, the developed map and approach can serve as a practical guide for enhancing groundwater management, identifying suitable areas for various uses and pinpointing regions requiring improved management practices.

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
Danke发布了新的文献求助10
刚刚
婷婷发布了新的文献求助10
1秒前
1秒前
阿苇完成签到,获得积分10
1秒前
2025迷完成签到 ,获得积分10
1秒前
Frank完成签到,获得积分0
2秒前
2秒前
xiao发布了新的文献求助30
2秒前
今天看文献了没完成签到 ,获得积分10
2秒前
kkkkkk完成签到,获得积分10
2秒前
2秒前
Sorexking发布了新的文献求助10
2秒前
3秒前
无私迎海发布了新的文献求助10
3秒前
今后应助大群采纳,获得10
3秒前
开心青旋发布了新的文献求助10
3秒前
时尚的雨筠完成签到 ,获得积分20
4秒前
GH07355018完成签到,获得积分10
4秒前
陶猛完成签到,获得积分10
4秒前
4秒前
爆米花应助张正阳采纳,获得10
4秒前
紫气东来应助彩色橘子采纳,获得10
4秒前
4秒前
王涵应助oi采纳,获得10
5秒前
大模型应助科研通管家采纳,获得10
5秒前
残剑月应助科研通管家采纳,获得10
5秒前
思源应助科研通管家采纳,获得10
5秒前
我是老大应助科研通管家采纳,获得10
5秒前
斯文败类应助科研通管家采纳,获得10
5秒前
叮当喵发布了新的文献求助10
5秒前
情怀应助科研通管家采纳,获得10
5秒前
残剑月应助科研通管家采纳,获得10
5秒前
丘比特应助科研通管家采纳,获得10
6秒前
wanci应助科研通管家采纳,获得30
6秒前
残剑月应助科研通管家采纳,获得10
6秒前
汉堡包应助Xenia采纳,获得10
6秒前
BowieHuang应助科研通管家采纳,获得10
6秒前
pluto应助科研通管家采纳,获得10
6秒前
Jasper应助科研通管家采纳,获得10
6秒前
Hello应助科研通管家采纳,获得10
6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
Basic And Clinical Science Course 2025-2026 3000
Encyclopedia of Agriculture and Food Systems Third Edition 2000
人脑智能与人工智能 1000
花の香りの秘密―遺伝子情報から機能性まで 800
Principles of Plasma Discharges and Materials Processing, 3rd Edition 400
Pharmacology for Chemists: Drug Discovery in Context 400
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5608560
求助须知:如何正确求助?哪些是违规求助? 4693225
关于积分的说明 14877335
捐赠科研通 4717884
什么是DOI,文献DOI怎么找? 2544255
邀请新用户注册赠送积分活动 1509400
关于科研通互助平台的介绍 1472836