September 2016
- Journal: Science of the Total Environment
- Authors: Sangam Shrestha, Ranjana Kafle, Vishnu Prasad Pandey
This study aimed at evaluating three index-overlay methods of vulnerability assessment (i.e., DRASTIC, GOD, and SI) for estimating risk to pollution of shallow groundwater aquifer in the Kathmandu Valley, Nepal. The Groundwater Risk Assessment Model (GRAM) model was used to compute the risk of [...]
- Keywords: DRASTIC, GIS, GOD, Groundwater, Risk assessment, SI, Vulnerability assessment
February 2017
- Journal: Geomatics, Natural Hazards and Risk
- Authors: Pallav Kumar Shrestha, Narendra Man Shakya, Vishnu Prasad Pandey, Stephen J. Birkinshaw, Sangam Shrestha
This study is the first to assess land subsidence in the Kathmandu Valley, Nepal. Land subsidence simulations were based on a fully calibrated groundwater (GW)flow model developed using a coupled surface–subsurface modelling system. Subsidence is predicted to occur as a result of deep aquifer [...]
- Keywords: Land subsidence, Groundwater abstraction, Kathmandu valley, Groundwater modelling, Deep aquifer
October 2018
- Journal: Sustainable Cities and Society
- Authors: Chanapathi Tirupathi, Thatikonda Shashidhar, Vishnu Prasad Pandey, Sangam Shrestha
The objective of this research is to develop a fuzzy-based groundwater sustainability index (FGSI) model to evaluate the sustainability of groundwater system at selected cities in Asian. The new Mamdani type fuzzy-based inference system known as FGSI was developed. It contains five components [...]
- Keywords: Asian cities, Fuzzy logic, Groundwater, Sustainability
April 2020
- Journal: Acta Geophysica
- Authors: S. Mohanasundaram, Parmeshwar Udmale, Sangam Shrestha, Triambak Baghel, Smit Chetan Doshi, G. Suresh Kumar, Balaji Narasimhan
Discrete groundwater level datasets are interpolated often using kriging group of models to produce a spatially continuous groundwater level map. There is always some level of uncertainty associated with different interpolation methods. Therefore, we developed a new trend function with the mean groundwater level [...]
- Keywords: Regression kriging, Digital elevation model, Mean groundwater level, Geostatistics, Trend function, Prediction error variance
January 2019
- Journal: H2Open Journal
- Authors: S. Mohanasundaram, G. Suresh Kumar, Balaji Narasimhan
Groundwater level prediction and forecasting using univariate time series models are useful for effective groundwater management under data limiting conditions. The seasonal autoregressive integrated moving average (SARIMA) models are widely used for modeling groundwater level data as the groundwater level signals possess the seasonality [...]
- Keywords: ARMA, Confidence interval, Deseasonalization, Groundwater level, SARIMA, Seasonal component