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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.

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 pre-monsoon 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 artificial neural network (ANN) and random forest (RF) were used for pixel-based classification 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 classifier has better performance than the artificial 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 classification 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 findings, the present study suggested to consider a large scale for determining the controlling factors of groundwater degradation.

Success of root canal therapy is primarly governed by the efficacy of disinfection of root canal system.Various methods, devices and irrigants have been suggested in past and are being introduced till now to have completely disinfected scenario. Still promising results are lacking.In this study we attempted to compare efficacy of nanosilver based endodontic irrigant ,0.05% octenidine solution and 5.25% sodium hypochlorite in disinfecting the root canal.

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