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Earth Science
India
2026

D-Index & Metrics

Earth Science

D-Index
49
Citations
10137
World Ranking
3558
National Ranking
12

Research.com Recognitions

  • 2026 - Research.com Earth Science in India Leader Award

Overview

Indra Prakash is affiliated with the Geological Survey of India in India. Their research spans primarily across the fields of Environmental Science and Engineering, with a focus on subfields including Global and Planetary Change, Civil and Structural Engineering, Management, Monitoring, Policy and Law, Environmental Engineering, and Water Science and Technology.

Prakash's main research topics cover a range of issues related to environmental and geotechnical challenges. These include Flood Risk Assessment and Management, Landslides and related hazards, Dam Engineering and Safety, Fire effects on ecosystems, Hydrology and Watershed Management Studies, Groundwater and Watershed Analysis, and Hydrological Forecasting Using Artificial Intelligence.

Their recent publications highlight work integrating advanced computational methods, particularly machine learning and hybrid artificial intelligence models, applied to environmental and hydrological phenomena. Notable recent papers include:

  • Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil, 2021, Mathematical Problems in Engineering
  • Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction, 2020, Symmetry
  • GIS Based Hybrid Computational Approaches for Flash Flood Susceptibility Assessment, 2020, Water
  • Development of advanced artificial intelligence models for daily rainfall prediction, 2020, Atmospheric Research
  • Flood risk assessment using hybrid artificial intelligence models integrated with multi-criteria decision analysis in Quang Nam Province, Vietnam, 2020, Journal of Hydrology

Frequent collaborators of Prakash include:

  • Binh Thai Pham
  • Hiep Van Le
  • Tran Van Phong
  • Nadhir Al-Ansari
  • Mahdis Amiri

Prakash's work has been published extensively in several venues, with multiple contributions to the following journals:

  • Journal of Science and Transport Technology
  • Mathematical Problems in Engineering
  • Geocarto International
  • Vietnam Journal of Earth Sciences
  • Applied Water Science

Best Publications

  • A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran.

    Khabat Khosravi;Binh Thai Pham;Kamran Chapi;Ataollah Shirzadi

  • A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods

    Khabat Khosravi;Himan Shahabi;Binh Thai Pham;Jan Adamowski

  • Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS

    Binh Thai Pham;Dieu Tien Bui;Indra Prakash;M.B. Dholakia

  • A comparative study of different machine learning methods for landslide susceptibility assessment

    Binh Thai Pham;Biswajeet Pradhan;Dieu Tien Bui;Indra Prakash

  • Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil

    Quang Hung Nguyen;Hai-Bang Ly;Lanh Si Ho;Nadhir Al-Ansari

  • Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods

    Binh Thai Pham;Dieu Tien Bui;Hamid Reza Pourghasemi;Prakash Indra

  • Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches

    Binh Thai Pham;Indra Prakash;Sushant K. Singh;Ataollah Shirzadi

  • Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran

    Saeid Janizadeh;Mohammadtaghi Avand;Abolfazl Jaafari;Tran Van Phong

  • A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling

    Binh Thai Pham;Abolfazl Jaafari;Indra Prakash;Dieu Tien Bui

  • Spatial prediction of landslides using a hybrid machine learning approach based on Random Subspace and Classification and Regression Trees

    Binh Thai Pham;Indra Prakash;Dieu Tien Bui

  • GIS Based Hybrid Computational Approaches for Flash Flood Susceptibility Assessment

    Binh Thai Pham;Mohammadtaghi Avand;Saeid Janizadeh;Tran Van Phong

  • A comparison study of DRASTIC methods with various objective methods for groundwater vulnerability assessment.

    Khabat Khosravi;Majid Sartaj;Frank T.-C. Tsai;Vijay P. Singh

  • A novel artificial intelligence approach based on Multi-layer Perceptron Neural Network and Biogeography-based Optimization for predicting coefficient of consolidation of soil

    Binh Thai Pham;Manh Duc Nguyen;Kien-Trinh Thi Bui;Indra Prakash

  • Development of advanced artificial intelligence models for daily rainfall prediction

    Binh Thai Pham;Lu Minh Le;Tien-Thinh Le;Kien-Trinh Thi Bui

  • A hybrid machine learning ensemble approach based on a Radial Basis Function neural network and Rotation Forest for landslide susceptibility modeling: A case study in the Himalayan area, India

    Binh Thai Pham;Ataollah Shirzadi;Dieu Tien Bui;Indra Prakash

  • Flood risk assessment using hybrid artificial intelligence models integrated with multi-criteria decision analysis in Quang Nam Province, Vietnam

    Unknown

  • A Comparative Study of Least Square Support Vector Machines and Multiclass Alternating Decision Trees for Spatial Prediction of Rainfall-Induced Landslides in a Tropical Cyclones Area

    Binh Thai Pham;Dieu Tien Bui;M. B. Dholakia;Indra Prakash

  • Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping

    Phong Tung Nguyen;Duong Hai Ha;Mohammadtaghi Avand;Abolfazl Jaafari

  • Development of artificial intelligence models for the prediction of Compression Coefficient of soil: An application of Monte Carlo sensitivity analysis.

    Binh Thai Pham;Manh Duc Nguyen;Dong Van Dao;Indra Prakash

  • Hybrid Machine Learning Approaches for Landslide Susceptibility Modeling

    Vu Viet Nguyen;Binh Thai Pham;Ba Thao Vu;Indra Prakash

  • Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping

    Binh Thai Pham;Trung Nguyen-Thoi;Chongchong Qi;Tran Van Phong

  • Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS

    Binh Thai Pham;Dieu Tien Bui;Indra Prakash;M. B. Dholakia

  • Hybrid computational intelligence models for groundwater potential mapping

    Binh Thai Pham;Abolfazl Jaafari;Indra Prakash;Sushant K. Singh

  • Bagging based Support Vector Machines for spatial prediction of landslides

    Binh Thai Pham;Dieu Tien Bui;Indra Prakash

  • Landslide Susceptibility Assessment Using Bagging Ensemble Based Alternating Decision Trees, Logistic Regression and J48 Decision Trees Methods: A Comparative Study

    Binh Thai Pham;Dieu Tien Bui;Indra Prakash

Frequent Co-Authors

Dieu Tien Bui
Dieu Tien Bui University of South-Eastern Norway
Nadhir Al-Ansari
Nadhir Al-Ansari Luleå University of Technology
Ataollah Shirzadi
Ataollah Shirzadi University of Kurdistan
Trung Nguyen-Thoi
Trung Nguyen-Thoi Van Lang University
Himan Shahabi
Himan Shahabi University of Kurdistan
Chongchong Qi
Chongchong Qi Central South University
Biswajeet Pradhan
Biswajeet Pradhan University of Technology Sydney
Le Hoang Son
Le Hoang Son Vietnam National University, Hanoi
Vijay P. Singh
Vijay P. Singh Texas A&M University
Rabin Chakrabortty
Rabin Chakrabortty Asian Institute of Technology

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