Application of novel data-mining technique based nitrate concentration susceptibility prediction approach for coastal aquifers in India

SC Pal, D Ruidas, A Saha, ARMT Islam… - Journal of cleaner …, 2022 - Elsevier
In water resource management and pollution control research, prediction of nitrate
concentration in groundwater gets utmost priority in the last few years. Thus, our current …

Evaluation of land suitability methods with reference to neglected and underutilised crop species: A scoping review

H Mugiyo, VGP Chimonyo, M Sibanda, R Kunz… - Land, 2021 - mdpi.com
In agriculture, land use and land classification address questions such as “where”,“why” and
“when” a particular crop is grown within a particular agroecology. To date, there are several …

Flash flood susceptibility modeling using new approaches of hybrid and ensemble tree-based machine learning algorithms

SS Band, S Janizadeh, S Chandra Pal, A Saha… - Remote Sensing, 2020 - mdpi.com
Flash flooding is considered one of the most dynamic natural disasters for which measures
need to be taken to minimize economic damages, adverse effects, and consequences by …

GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran

SA Naghibi, HR Pourghasemi, B Dixon - Environmental monitoring and …, 2016 - Springer
Groundwater is considered one of the most valuable fresh water resources. The main
objective of this study was to produce groundwater spring potential maps in the Koohrang …

Landslide susceptibility evaluation and management using different machine learning methods in the Gallicash River Watershed, Iran

A Arabameri, S Saha, J Roy, W Chen, T Blaschke… - Remote Sensing, 2020 - mdpi.com
This analysis aims to generate landslide susceptibility maps (LSMs) using various machine
learning methods, namely random forest (RF), alternative decision tree (ADTree) and …

Testing a new ensemble model based on SVM and random forest in forest fire susceptibility assessment and its mapping in Serbia's Tara National Park

L Gigović, HR Pourghasemi, S Drobnjak, S Bai - Forests, 2019 - mdpi.com
The main objectives of this paper are to demonstrate the results of an ensemble learning
method based on prediction results of support vector machine and random forest methods …

A comparative assessment between three machine learning models and their performance comparison by bivariate and multivariate statistical methods in …

SA Naghibi, HR Pourghasemi - Water resources management, 2015 - Springer
As demand for fresh groundwater in the worldwide is increasing, delineation of groundwater
spring potential zones become an increasingly important tool for implementing a successful …

Prediction of landslide susceptibility in Rudraprayag, India using novel ensemble of conditional probability and boosted regression tree-based on cross-validation …

S Saha, A Arabameri, A Saha, T Blaschke… - Science of the total …, 2021 - Elsevier
The present research examines the landslide susceptibility in Rudraprayag district of
Uttarakhand, India using the conditional probability (CP) statistical technique, the boost …

GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression …

A Arabameri, B Pradhan, K Rezaei, M Sohrabi… - Journal of Mountain …, 2019 - Springer
In this study, a novel approach of the landslide numerical risk factor (LNRF) bivariate model
was used in ensemble with linear multivariate regression (LMR) and boosted regression …

Comparison of differences in resolution and sources of controlling factors for gully erosion susceptibility mapping

Y Garosi, M Sheklabadi, HR Pourghasemi… - Geoderma, 2018 - Elsevier
Gully erosion has been identified as an important soil degradation process and sediment
source, especially in arid and semiarid areas. Thus, it is useful to identify the spatial …