Influences of water and sediment quality on benthic macroinvertebrates in a river with different land use types

The present study aimed to assess the impacts of land cover on groundwater quality by integrating physico-chemical data and satellite imageries. Initially, fourteen groundwater parameters of both premonsoon and post-monsoon were collected from nineteen sampled stations and water quality index (WQI) was calculated. Consequently, Google earth, Landsat-8, and Sentinel-2A imageries were considered for land cover mapping including the concentration of settlement and urban built-up, greenery coverage, and micro-level land use. Two machine learning models such as arti cial neural network (ANN) and random forest (RF) were used for pixel-based classi cation and establish spatial relation between water quality and land cover. This study trained and tested the models for the whole study area as well as 500m buffers from each sampled station. The result of model's validation including mean absolute error (MAE), root mean squared error (RMSE), Kappa statistic (K), Overall accuracy of model (OAC), and receiver operating characteristic (ROC), indicated that random forest classi er has better performance than the arti cial neural network. The results show that the testing dataset of pre-monsoon season has higher accuracy with MAE 0.343, RMSE 0.397, K-value 0.55, and ROC value 0.838 to envisage the impact of land cover on groundwater quality in comparison to post-monsoon season. The results also reveal that the classi cation accuracy is greater within 500m buffer areas in comparison to the whole study area with a close to 0 value of MAE and RMSE, and absolutely 1 value of K and ROC. Based on the above ndings, the present study suggested to consider a large scale for determining the controlling factors of groundwater degradation.

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Assessing the Impact of Land Cover on Groundwater Quality in a Smart City Using GIS and Machine Learning Algorithms

Anjum

Ali²,

Siddiqui³

2023

Water Air Soil Pollut

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Adding Cipangopaludina chinensis Altered the Effects of Submerged Macrophyte Diversity on Water Turbidity and Nitrogen Concentration

Shen,

Cao,

Tao

et al. 2023

Water Air Soil Pollut

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Decadal trends and predictive insights into aquatic ecosystem health in the Namhan River, South Korea: Longitudinal analysis of hydro-environmental factors and stream health indices (2008–2022)

Cho,

Fabian,

Lee

et al. 2024

Environmental Pollution

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Influences of water and sediment quality on benthic macroinvertebrates in a river with different land use types

Cited by 6 publications

References 34 publications

Assessing the Impact of Land Cover on Groundwater Quality in a Smart City Using GIS and Machine Learning Algorithms

Assessing the Impact of Land Cover on Groundwater Quality in a Smart City Using GIS and Machine Learning Algorithms

Adding Cipangopaludina chinensis Altered the Effects of Submerged Macrophyte Diversity on Water Turbidity and Nitrogen Concentration

Decadal trends and predictive insights into aquatic ecosystem health in the Namhan River, South Korea: Longitudinal analysis of hydro-environmental factors and stream health indices (2008–2022)

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