Machine learning algorithm-based risk assessment of riparian wetlands in Padma River Basin of Northwest Bangladesh
Islam, Abu Reza Md Towfiqul ; Talukdar, Swapan ; Mahato, Susanta ; Ziaul, Sk ; Eibek, Kutub Uddin ; Akhter, Shumona ; Pham, Quoc Bao ; Mohammadi, Babak LU
; Karimi, Firoozeh
and Linh, Nguyen Thi Thuy
(2021)
In Environmental Science and Pollution Research
28.
p.34450-34471
- Abstract
Wetland risk assessment is a global concern especially in developing countries like Bangladesh. The present study explored the spatiotemporal dynamics of wetlands, prediction of wetland risk assessment. The wetland risk assessment was predicted based on ten selected parameters, such as fragmentation probability, distance to road, and settlement. We used M5P, random forest (RF), reduced error pruning tree (REPTree), and support vector machine (SVM) machine learning techniques for wetland risk assessment. The results showed that wetland areas at present are declining less than one-third of those in 1988 due to the construction of the dam at Farakka, which is situated at the upstream of the Padma River. The distance to the river and... (More)
Wetland risk assessment is a global concern especially in developing countries like Bangladesh. The present study explored the spatiotemporal dynamics of wetlands, prediction of wetland risk assessment. The wetland risk assessment was predicted based on ten selected parameters, such as fragmentation probability, distance to road, and settlement. We used M5P, random forest (RF), reduced error pruning tree (REPTree), and support vector machine (SVM) machine learning techniques for wetland risk assessment. The results showed that wetland areas at present are declining less than one-third of those in 1988 due to the construction of the dam at Farakka, which is situated at the upstream of the Padma River. The distance to the river and built-up area are the two most contributing drivers influencing the wetland risk assessment based on information gain ratio (InGR). The prediction results of machine learning models showed 64.48% of area by M5P, 61.75% of area by RF, 62.18% of area by REPTree, and 55.74% of area by SVM have been predicted as the high and very high-risk zones. The results of accuracy assessment showed that the RF outperformed than other models (area under curve: 0.83), followed by the SVM, M5P, and REPTree. Degradation of wetlands explored in this study demonstrated the negative effects on biodiversity. Therefore, to conserve and protect the wetlands, continuous monitoring of wetlands using high resolution satellite images, feeding with the ecological flow, confining built up area and agricultural expansion towards wetlands, and new wetland creation is essential for wetland management. Graphical abstract: [Figure not available: see fulltext.]
(Less)
- author
- Islam, Abu Reza Md Towfiqul
; Talukdar, Swapan
; Mahato, Susanta
; Ziaul, Sk
; Eibek, Kutub Uddin
; Akhter, Shumona
; Pham, Quoc Bao
; Mohammadi, Babak
LU
; Karimi, Firoozeh
and Linh, Nguyen Thi Thuy
- organization
- publishing date
- 2021
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Drivers of wetland conversion, Machine learning techniques, MNDWI, Wetland dynamics, Wetland risk assessment
- in
- Environmental Science and Pollution Research
- volume
- 28
- pages
- 34450 - 34471
- publisher
- Springer
- external identifiers
-
- pmid:33651294
- scopus:85102081024
- ISSN
- 0944-1344
- DOI
- 10.1007/s11356-021-12806-z
- language
- English
- LU publication?
- yes
- id
- a68dd4c7-af65-41a6-b911-fbcb6e63924d
- date added to LUP
- 2021-03-15 09:42:45
- date last changed
- 2024-04-18 04:23:52
@article{a68dd4c7-af65-41a6-b911-fbcb6e63924d,
abstract = {{<p>Wetland risk assessment is a global concern especially in developing countries like Bangladesh. The present study explored the spatiotemporal dynamics of wetlands, prediction of wetland risk assessment. The wetland risk assessment was predicted based on ten selected parameters, such as fragmentation probability, distance to road, and settlement. We used M5P, random forest (RF), reduced error pruning tree (REPTree), and support vector machine (SVM) machine learning techniques for wetland risk assessment. The results showed that wetland areas at present are declining less than one-third of those in 1988 due to the construction of the dam at Farakka, which is situated at the upstream of the Padma River. The distance to the river and built-up area are the two most contributing drivers influencing the wetland risk assessment based on information gain ratio (InGR). The prediction results of machine learning models showed 64.48% of area by M5P, 61.75% of area by RF, 62.18% of area by REPTree, and 55.74% of area by SVM have been predicted as the high and very high-risk zones. The results of accuracy assessment showed that the RF outperformed than other models (area under curve: 0.83), followed by the SVM, M5P, and REPTree. Degradation of wetlands explored in this study demonstrated the negative effects on biodiversity. Therefore, to conserve and protect the wetlands, continuous monitoring of wetlands using high resolution satellite images, feeding with the ecological flow, confining built up area and agricultural expansion towards wetlands, and new wetland creation is essential for wetland management. Graphical abstract: [Figure not available: see fulltext.]</p>}},
author = {{Islam, Abu Reza Md Towfiqul and Talukdar, Swapan and Mahato, Susanta and Ziaul, Sk and Eibek, Kutub Uddin and Akhter, Shumona and Pham, Quoc Bao and Mohammadi, Babak and Karimi, Firoozeh and Linh, Nguyen Thi Thuy}},
issn = {{0944-1344}},
keywords = {{Drivers of wetland conversion; Machine learning techniques; MNDWI; Wetland dynamics; Wetland risk assessment}},
language = {{eng}},
pages = {{34450--34471}},
publisher = {{Springer}},
series = {{Environmental Science and Pollution Research}},
title = {{Machine learning algorithm-based risk assessment of riparian wetlands in Padma River Basin of Northwest Bangladesh}},
url = {{http://dx.doi.org/10.1007/s11356-021-12806-z}},
doi = {{10.1007/s11356-021-12806-z}},
volume = {{28}},
year = {{2021}},
}