Publications
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Read More May 31.2023
Landslide susceptibility prediction using frequency ratio model: a case study of Uttarakhand, Himalaya (India)
The purpose of this study is to develop a landslide susceptibility prediction model by applying the Frequency Ratio (FR) model and remote sensing data sets for the Northern part of Uttarakhand, India.
Read More August 31.2022
Influence of differential arsenic exposure on cellular redox homeostasis of exposed rural women of West Bengal
The metalloid arsenic (As) induces oxidative stress is a well-known fact. However, the extent of variation of oxidative stress according to different exposure levels of As.
Read More June 13.2022
Application of a Novel Hybrid Machine Learning Algorithm in Shallow Landslide Susceptibility Mapping in a Mountainous Area
Landslides can be a major challenge in mountainous areas that are influenced by climate and landscape changes.
Read More May 25.2022
Towards robust smart data-driven soil erodibility index prediction under different scenarios
Soil erosion is a major cause of damage to agricultural lands in many parts of the world and is of particular concern in semiarid parts of Iran. We use five machine learning techniques.
Read More February 01.2022
Predicting sustainable arsenic mitigation using machine learning techniques
This study evaluates state-of-the-art machine learning models in predicting the most sustainable arsenic mitigation preference.
Read More December 13.2021
Evaluating and predicting social behavior of arsenic affected communities: Towards developing arsenic resilient society
This study uses six machine learning (ML) algorithms to evaluate and predict individuals' social resilience towards arsenicosis-affected people in an arsenic-risk society of rural India. Over 50% of the surveyed communities were found to be resilient towards arsenicosis patients.