Application of Machine Learning and Remote Sensing in Hydrology
Mohammadi, Babak LU
(2022)
In Sustainability (Switzerland)
14(13).
- Abstract
- Water is vital to all life on earth, but its management is becoming more difficult owing to the behavior of water in nature such as water dynamics, water movements, and different forms of water in nature. In addition, population growth, the impact of climate change, and the inappropriate use of water resources add more complexity to water resource management. The integration of natural sciences necessitates creative methods in decision sciences, data processing, and modeling methodologies. The prediction of such occurrences is a highly nonlinear problem that requires the use of modern capable techniques. For handling the abovementioned items, we need to use tools capable of solving water-based issues. Machine learning and remote sensing... (More)
- Water is vital to all life on earth, but its management is becoming more difficult owing to the behavior of water in nature such as water dynamics, water movements, and different forms of water in nature. In addition, population growth, the impact of climate change, and the inappropriate use of water resources add more complexity to water resource management. The integration of natural sciences necessitates creative methods in decision sciences, data processing, and modeling methodologies. The prediction of such occurrences is a highly nonlinear problem that requires the use of modern capable techniques. For handling the abovementioned items, we need to use tools capable of solving water-based issues. Machine learning and remote sensing technologies as recently developed technologies have been considered as tools for these solving water-based issues. Additionally, the “Topic” sought to obtain a collection ofrecent studies on the application of machine learning and remote sensing in hydrology. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/dd8410c4-51c6-4103-b50d-8093b5860670
- author
- Mohammadi, Babak
LU
- organization
- publishing date
- 2022-06-22
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- hydrological modeling, machine learning, Remote sensing
- in
- Sustainability (Switzerland)
- volume
- 14
- issue
- 13
- article number
- 7586
- publisher
- MDPI AG
- external identifiers
-
- scopus:85133191647
- ISSN
- 2071-1050
- DOI
- 10.3390/su14137586
- language
- English
- LU publication?
- yes
- id
- dd8410c4-51c6-4103-b50d-8093b5860670
- date added to LUP
- 2022-07-12 08:28:56
- date last changed
- 2024-01-24 11:01:19
@misc{dd8410c4-51c6-4103-b50d-8093b5860670,
abstract = {{Water is vital to all life on earth, but its management is becoming more difficult owing to the behavior of water in nature such as water dynamics, water movements, and different forms of water in nature. In addition, population growth, the impact of climate change, and the inappropriate use of water resources add more complexity to water resource management. The integration of natural sciences necessitates creative methods in decision sciences, data processing, and modeling methodologies. The prediction of such occurrences is a highly nonlinear problem that requires the use of modern capable techniques. For handling the abovementioned items, we need to use tools capable of solving water-based issues. Machine learning and remote sensing technologies as recently developed technologies have been considered as tools for these solving water-based issues. Additionally, the “Topic” sought to obtain a collection ofrecent studies on the application of machine learning and remote sensing in hydrology.}},
author = {{Mohammadi, Babak}},
issn = {{2071-1050}},
keywords = {{hydrological modeling; machine learning; Remote sensing}},
language = {{eng}},
month = {{06}},
number = {{13}},
publisher = {{MDPI AG}},
series = {{Sustainability (Switzerland)}},
title = {{Application of Machine Learning and Remote Sensing in Hydrology}},
url = {{http://dx.doi.org/10.3390/su14137586}},
doi = {{10.3390/su14137586}},
volume = {{14}},
year = {{2022}},
}