Fog-water harvesting Capability Index (FCI) mapping for a semi-humid catchment based on socio-environmental variables and using artificial intelligence algorithms-Reference-Cited by-同舟云学术

Fog-water harvesting Capability Index (FCI) mapping for a semi-humid catchment based on socio-environmental variables and using artificial intelligence algorithms

Published:2020-03 Issue: Volume:708 Page:135115
ISSN:0048-9697
Container-title:Science of The Total Environment
language:en
Short-container-title:Science of The Total Environment

Author:

Karimidastenaei Zahra,Torabi Haghighi Ali^ORCID,Rahmati Omid,Rasouli Kabir^ORCID,Rozbeh Sajad,Pirnia Abdollah,Pradhan Biswajeet^ORCID,Kløve Bjørn

Publisher

Elsevier BV

Subject

Pollution,Waste Management and Disposal,Environmental Chemistry,Environmental Engineering

Reference62 articles.

1. A comprehensive optimum integrated water resources management approach for multidisciplinary water resources management problems;Al-Jawad;J. Environ. Manage.,2019

2. Conflicts and security in integrated water resources management;Al-Saidi;Environ. Sci. Policy,2017

3. Models for the analysis of species’ response to environmental gradients;Austin,1987

4. Water policy implementation in the state of São Paulo, Brazil: Key challenges and opportunities;Barbosa;Environ. Sci. Policy,2016

5. Integrated water resources management: Are river basin committees in Brazil enabling effective stakeholder interaction?;Barbosa;Environ. Sci. Policy,2017

Cited by 10 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Artificial Intelligence in Agricultural Mapping: A Review;Agriculture;2024-07-03

2. A review of the methods of harvesting atmospheric moisture;Environmental Science and Pollution Research;2023-11-04

3. Vegetation vulnerability to hydrometeorological stresses in water-scarce areas using machine learning and remote sensing techniques;Ecological Informatics;2023-03

4. Mapping non‐conventional atmospheric drinking‐water harvesting opportunities in Central Eurasia: The case of Kazakhstan;Natural Resources Forum;2023-01-11

5. Large-scale dynamic flood monitoring in an arid-zone floodplain using SAR data and hybrid machine-learning models;Journal of Hydrology;2022-08