Journal Articles

Machine learning model optimization for flood susceptibility zonation over the Kosi megafan, Himalayan foreland basin, India

Scientific Reports 15, 32757 (2025)

Authors: Arora, A., Durga G, P., Pandey, M. et al.

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Decadal analysis and simulation of land use and land cover changes in Taiwan using machine learning and markov chain models

Environment, Development and Sustainability (2024)

Authors: Liu, C., & Arora, A.*

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Landslide susceptibility assessment and mapping using new ensemble model

Advances in Space Research (2024)

Authors: Shen, Z., Wang, D., Arabameri, A., Santosh, M., Egbueri, J. C., & Arora, A.

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Earth fissure susceptibility mapping: Application of random subspace‐based novel ensemble approaches

Geological Journal (2024)

Authors: Santosh, M., Arabameri, A., & Arora, A.

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Landslide susceptibility mapping: application of novel hybridization of rotation forests (RF) and Java decision trees (J48)

Soft Computing, 27, 17387–17402 (2023)

Authors: Liang, L., Cui, H., Arabameri, A., Arora, A., & Seyed Danesh, A.

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Flood susceptibility prediction using multi criteria decision analysis and bivariate statistical models: a case study of Lower Kosi River Basin

Stochastic Environmental Research and Risk Assessment, 37, 1855–1875 (2022)

Authors: Arora, A.*

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Prediction of groundwater nitrate concentration in a semiarid region using hybrid Bayesian artificial intelligence approaches

Environmental Science and Pollution Research, 29, 20421–20436 (2022)

Authors: Alkindi, K. M., Mukherjee, K., Pandey, M., Arora, A., et al.

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Flood susceptibility modelling in a subtropical humid low-relief alluvial plain environment: Application of novel machine learning ensemble approach

Frontiers in Earth Science, 9, 659296 (2021)

Authors: Pandey, M., Arora, A.*, Arabameri, A., Costache, R., Lee, S.

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Comparative evaluation of geospatial scenario-based land change simulation models using landscape metrics

Ecological Indicators, 128, 107810 (2021)

Authors: Arora, A., Pandey, M., Mishra, V.N., et al.

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Optimization of State-of-the-Art Fuzzy-Metaheuristic ANFIS-Based Machine Learning Models for Flood Susceptibility Prediction Mapping

Science of the Total Environment, 750, 141565 (2021)

Authors: Arora, A., Arabameri, A., Pandey, M., et al.

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Flash-flood propagation susceptibility estimation using weights of evidence and their novel ensembles

Geocarto International, 37(25), 8361-8393 (2021)

Authors: Costache, R., ... Arora, A., et al.

Flood Susceptibility Assessment Using Novel Ensemble of Hyperpipes and Support Vector Regression Algorithms

Water, 13 (2), 1–27 (2021)

Authors: Saha, A., ... Arora, A., et al.

Climate Change Effect on Water Resources in Varanasi District, India

Meteorological Applications, 27 (1), 1–16 (2020)

Authors: Nistor, M. M., ... Arora, A., et al.

K-Fold and State-of-the-Art Metaheuristic Machine Learning Approaches for Groundwater Potential Modelling

Water Resources Management, 35, 1837–1869 (2021)

Authors: Arabameri, A., Arora, A., et al.

Flood Susceptibility Mapping Using a Novel Deep Learning Model Combining Multilayer Perceptron and Autoencoder Neural Networks

Journal of Flood Risk Management, 14 (1) (2020)

Authors: Ahmadlou, M., ... Arora, A., et al.

Book Chapters & Editorials

Evaluation of Machine Learning Approaches for Flood Hazard Mapping Over the Argens Basin, France

Advances in Hydroinformatics (SimHydro 2023), Springer Water (2024)

Authors: Arora, A.*, Nicolle, P., Payrastre, O.

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Linkage Between Forest Fires and Meteorological Parameters Using Spatial Clustering

Geo-Information for Disaster Monitoring and Management (2024)

Authors: Pandey, M., Arora, A.*, Geesupalli, P.

Co-Editor: Advances in Remote Sensing Technology and the Three Poles

Wiley Publications, USA (2022)

Co-edited the comprehensive volume and contributed to 4 chapters regarding Landscape Modeling, Spectral Indices, and Morphometry Software.

Statistical Analysis of Major Flood Events During 1980-2015 in Middle Ganga Plain

Spatial Information Science for Natural Resource Management, IGI Global (2020)

Authors: Arora, A., Siddiqui, M. A., & Pandey, M.