Integrating remote sensing with swarm intelligence and artificial intelligence for modelling wetland habitat vulnerability in pursuance of damming

Khatun, Rumki; Talukdar, Swapan; Saha, Tamal Kanti; Mahato, Susanta; Debanshi, Sandipta; Mandal, Indrajit

doi:10.1016/j.ecoinf.2021.101349

Cited by 23 publications

(3 citation statements)

References 110 publications

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“…RF is a bagging-based artificial intelligence model frequently utilized for prediction and classification. For the prediction of time series datasets, we recently used this approach, and the results were impressive [54]. The RF algorithm is a nonparametric ensemble classification algorithm based on Breiman's algorithm's flexible decision tree [55].…”

Section: Machine Learning Algorithms Application Of Rfmentioning

confidence: 99%

Assessing the Impact of the Farakka Barrage on Hydrological Alteration in the Padma River with Future Insight

et al. 2022

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Climate change and human interventions (e.g., massive barrages, dams, sand mining, and sluice gates) in the Ganga–Padma River (India and Bangladesh) have escalated in recent decades, disrupting the natural flow regime and habitat. This study employed innovative trend analysis (ITA), range of variability approach (RVA), and continuous wavelet analysis (CWA) to quantify the past to future hydrological change in the river because of the building of the Farakka Barrage (FB). We also forecast flow regimes using unique hybrid machine learning techniques based on particle swarm optimization (PSO). The ITA findings revealed that the average discharge trended substantially negatively throughout the dry season (January–May). However, the RVA analysis showed that average discharge was lower than environmental flows. The CWA indicated that the FB has a significant influence on the periodicity of the streamflow regime. PSO-Reduced Error Pruning Tree (REPTree) was the best fit for average discharge prediction (RMSE = 0.14), PSO-random forest (RF) was the best match for maximum discharge (RMSE = 0.3), and PSO-M5P (RMSE = 0.18) was better for the lowest discharge prediction. Furthermore, the basin’s discharge has reduced over time, concerning the riparian environment. This research describes the measurement of hydrological change and forecasts the discharge for upcoming days, which might be valuable in developing sustainable water resource management plans in this location.

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Section: Machine Learning Algorithms Application Of Rfmentioning

confidence: 99%

Assessing the Impact of the Farakka Barrage on Hydrological Alteration in the Padma River with Future Insight

et al. 2022

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“…It is important to understand the interactions of societal factors that influence people's preferences in policy-making or decisions for sustainability (Roobavannan et al, 2020). Several studies wer e reported on the environmental sustainability aspects such as Eco-Hydrological sustainability (Khatun et al, 2021;Shiran et al, 2021), eco-environmental vulnerability (Nguyen et al, 2016;Liou et al, 2017;Venkatesh et al, 2020;and Kurn iawan et al, 2022), vegetation dynamics (Qureshi et al, 2020), Soil moisture-LST based drought characterization (Saha et al, 2018). Several hydrological, ecological, environmental, and socio-economic problems and their solutions have been addressed by analyzing the Remote Sensing based land cover changes for environmental sustainability (Liou et al, 2017;Venkatesh et al, 2020;Kurniawan et al, 2022;Saleh et al, 2022).…”

Section: Introduction 11 Environmental Sustainability Assessmentmentioning

confidence: 99%

“…The Google Earth Engine (GEE) Platform has been used for data extractio n f r o m Landsat 8 for 2020. The land cover indices, such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Bu iltup Index (NDBI), Normalized Difference Moisture Index (NDMI) and LST, are chosen as major land Remote Sensing based environmental variables that help to regulate the environmental flows, their vulnerabilities and sustainability of natural resources (Nguyen et al, 2016;Firozjaei et al, 2021;Khatun et al, 2021;Kurniawan et al, 2022). The results of these indices and LST are used to delineate the sub-catchment units as the sampling units.…”

Section: Introduction 11 Environmental Sustainability Assessmentmentioning

confidence: 99%

Environmental Sustainability Assessment of a Himalayan Catchment With Land Cover Indices and LST Relationship Using Principal Component Analysis – A Geospatial Approach

Sathyaseelan

Ghosh

Ojha

2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Environmental sustainability assessment is a crucial part of the management of natural resources. Remote Sensing based environmental land cover indices such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Built-up Index (NDBI), Normalized Difference Moisture Index (NDMI), and its associated Land Surface Temperature (LST) are the major governing factors for the environmental processes that happen on the surface of the earth . These NDVI, NDWI, NDBI, NDMI, and LST are generated for 2020 using the Landsat satellite datasets. The process-based relationship among them is complex and involves various parameters but may be easily represented by multiple linear regression models. Principal Component Analysis (PCA) is one such type that efficiently handles and evaluates the contribution of each of these factors to each other based on the sampling units. The study area is the upper Ramganga catchment in the Indian Himalayas, consisting of 117 sub-catchments. These catchment units (samples) are entangled with these environmental factors. The results of the PCA reveal the relationship between each of the environmental factors and their priority. Based on the uncorrelated factors priority suggestion from the PCA, catchment units were classified as high, moderate, or low categories based on their dominance in the relationship among the factors. These spatial variations in the environmental factors can help to assess the sustainability of resources in the Himalayan catchment.

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Hybrid Tree-Based Wetland Vulnerability Modelling

Pal

Paul

2022

Springer Natural Hazards

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Integrating remote sensing with swarm intelligence and artificial intelligence for modelling wetland habitat vulnerability in pursuance of damming

Cited by 23 publications

References 110 publications

Assessing the Impact of the Farakka Barrage on Hydrological Alteration in the Padma River with Future Insight

Assessing the Impact of the Farakka Barrage on Hydrological Alteration in the Padma River with Future Insight

Environmental Sustainability Assessment of a Himalayan Catchment With Land Cover Indices and LST Relationship Using Principal Component Analysis – A Geospatial Approach

Hybrid Tree-Based Wetland Vulnerability Modelling

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