Water Scarcity Management : Toward the Application of Artificial Intelligence and Earth Observation Data
Rahmati, Omid ; Melesse, Assefa M. and Naghibi, Amir LU (2025)- Abstract
- Drought and the condition of water scarcity lead to several
socio-economic, social and environmental impacts. Whatever the
approaches of drought management, policymakers and planners require
novel methods to analyze data and model drought processes and its
connection with water scarcity. In recent years, artificial
intelligence-based and earth observation approaches have been
progressively developed and applied in domain of water-related
disasters. The target of this book is to present new advances and
achievements in the fields of drought monitoring, analyzing, and
modeling using artificial intelligence algorithms (e.g., machine
learning, deep learning, etc.), statistical indices, and a... (More) - Drought and the condition of water scarcity lead to several
socio-economic, social and environmental impacts. Whatever the
approaches of drought management, policymakers and planners require
novel methods to analyze data and model drought processes and its
connection with water scarcity. In recent years, artificial
intelligence-based and earth observation approaches have been
progressively developed and applied in domain of water-related
disasters. The target of this book is to present new advances and
achievements in the fields of drought monitoring, analyzing, and
modeling using artificial intelligence algorithms (e.g., machine
learning, deep learning, etc.), statistical indices, and a diverse range
of satellite remote sensing and geo-spatial data sets. Water Scarcity Management: Towards the Application of Artificial Intelligence and Earth Observation Data
will help students gain knowledge on drought prediction using new
free-access earth observation data and machine learning models. It will
also guide scientists, researchers, and urban planners with the
monitoring of water resources and key elements of hydrological cycle. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/65c0a4eb-f59a-4ad2-979d-520b10085849
- editor
- Rahmati, Omid ; Melesse, Assefa M. and Naghibi, Amir LU
- organization
- publishing date
- 2025
- type
- Book/Report
- publication status
- published
- subject
- pages
- 386 pages
- publisher
- Elsevier
- external identifiers
-
- scopus:105026851944
- ISBN
- 9780443267239
- 9780443267222
- DOI
- 10.1016/C2023-0-52681-3
- language
- English
- LU publication?
- yes
- id
- 65c0a4eb-f59a-4ad2-979d-520b10085849
- date added to LUP
- 2026-02-16 09:54:38
- date last changed
- 2026-02-16 09:55:36
@book{65c0a4eb-f59a-4ad2-979d-520b10085849,
abstract = {{Drought and the condition of water scarcity lead to several <br>
socio-economic, social and environmental impacts. Whatever the <br>
approaches of drought management, policymakers and planners require <br>
novel methods to analyze data and model drought processes and its <br>
connection with water scarcity. In recent years, artificial <br>
intelligence-based and earth observation approaches have been <br>
progressively developed and applied in domain of water-related <br>
disasters. The target of this book is to present new advances and <br>
achievements in the fields of drought monitoring, analyzing, and <br>
modeling using artificial intelligence algorithms (e.g., machine <br>
learning, deep learning, etc.), statistical indices, and a diverse range<br>
of satellite remote sensing and geo-spatial data sets. <i>Water Scarcity Management: Towards the Application of Artificial Intelligence and Earth Observation Data</i><br>
will help students gain knowledge on drought prediction using new <br>
free-access earth observation data and machine learning models. It will <br>
also guide scientists, researchers, and urban planners with the <br>
monitoring of water resources and key elements of hydrological cycle.}},
editor = {{Rahmati, Omid and Melesse, Assefa M. and Naghibi, Amir}},
isbn = {{9780443267239}},
language = {{eng}},
note = {{Book Editor}},
publisher = {{Elsevier}},
title = {{Water Scarcity Management : Toward the Application of Artificial Intelligence and Earth Observation Data}},
url = {{http://dx.doi.org/10.1016/C2023-0-52681-3}},
doi = {{10.1016/C2023-0-52681-3}},
year = {{2025}},
}