Journal ArticleDOI
Machine learning algorithm-based risk assessment of riparian wetlands in Padma River Basin of Northwest Bangladesh
Abu Reza Md. Towfiqul Islam,Swapan Talukdar,Susanta Mahato,Sk Ziaul,Kutub Uddin Eibek,Shumona Akhter,Quoc Bao Pham,Babak Mohammadi,Firoozeh Karimi,Nguyen Thi Thuy Linh +9 more
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TLDR
In this paper, the authors explored the spatiotemporal dynamics of wetlands, prediction of wetland risk assessment, and 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.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 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.read more
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Applications of various data-driven models for the prediction of groundwater quality index in the Akot basin, Maharashtra, India.
TL;DR: In this paper, four standalone methods such as additive regression (AR), M5P tree model (M5P), random subspace (RSS), and support vector machine (SVM) were employed to predict WQI based on variable elimination technique.
Journal ArticleDOI
Classification of Zambian grasslands using random forest feature importance selection during the optimal phenological period
Yi-Fang Zhao,Weiwei Zhu,Panpan Wei,P Fang,Xiwang Zhang,Nana Yan,Wenjun Liu,Hao Zhao,Qirui Wu +8 more
TL;DR: In this paper , the authors selected the Google Earth Engine platform to select 100m resolution PROBA-V remote sensing images from 2018 of Zambia, in central Africa, in order to conduct grassland resource surveys for the scientific management of grassland resources.
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Estimating reference evapotranspiration using hybrid adaptive fuzzy inferencing coupled with heuristic algorithms
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TL;DR: In this article, a hybrid adaptive neuro-fuzzy inference system (ANFIS-WCAMFO) was proposed for predicting reference evapotranspiration (ETo) in south-central Bangladesh.
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Journal ArticleDOI
Impact of wetland fragmentation due to damming on the linkages between water richness and ecosystem services.
TL;DR: In this article, the influence of wetland fragmentation due to damming on wetland water richness and the impact of changes in water richness on the ecosystem service value (ESV) of the wetlanddominated rivers of the lower Punarbhaba Basin, India, and Bangladesh, as the case.
References
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Hasnaa Harmouzi,Hakan A. Nefeslioglu,Mohamed Rouai,Ebru Akcapinar Sezer,Abdelillah Dekayir,Candan Gokceoglu +5 more
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Urban Wetland Planning and Management in Ghana: a Disappointing Implementation
TL;DR: In this paper, the authors examined the causes and extent of wetland destruction in the Greater Accra Metropolitan Area (GAMA) in Ghana and revealed that measures designed and implemented to protect wetlands in the GAMA have proved ineffective due to non-implementation and monitoring of wet land protection strategies, policies and projects.
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Constructing multimetric indices and testing ability of landscape metrics to assess condition of freshwater wetlands in the Northeastern US
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