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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.

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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.

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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.

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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.

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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.

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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.