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2026

Indra Prakash publication distribution in Earth Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Earth Science in 2026. The highlighted bar marks where Indra Prakash sits on this spectrum.

38–42 publications: 1 scientists 43–47 publications: 0 scientists 48–52 publications: 3 scientists 53–57 publications: 8 scientists 58–62 publications: 11 scientists 63–67 publications: 16 scientists 68–72 publications: 24 scientists 73–77 publications: 31 scientists 78–82 publications: 49 scientists 83–87 publications: 77 scientists 88–92 publications: 86 scientists 93–97 publications: 88 scientists 98–102 publications: 92 scientists 103–107 publications: 84 scientists 108–112 publications: 102 scientists 113–117 publications: 108 scientists 118–122 publications: 117 scientists 123–127 publications: 126 scientists 128–132 publications: 146 scientists 133–137 publications: 114 scientists 138–142 publications: 102 scientists 143–147 publications: 127 scientists 148–152 publications: 102 scientists 153–157 publications: 112 scientists 158–162 publications: 102 scientists 163–167 publications: 127 scientists 168–172 publications: 121 scientists 173–177 publications: 112 scientists 178–182 publications: 109 scientists 183–187 publications: 98 scientists 188–192 publications: 92 scientists 193–197 publications: 105 scientists 198–202 publications: 77 scientists 203–207 publications: 80 scientists 208–212 publications: 89 scientists 213–217 publications: 70 scientists 218–222 publications: 74 scientists 223–227 publications: 74 scientists 228–232 publications: 70 scientists 233–237 publications: 67 scientists 238–242 publications: 59 scientists 243–247 publications: 67 scientists 248–252 publications: 51 scientists 253–257 publications: 46 scientists 258–262 publications: 41 scientists 263–267 publications: 39 scientists 268–272 publications: 34 scientists 273–277 publications: 39 scientists 278–282 publications: 35 scientists 283–287 publications: 36 scientists 288–292 publications: 35 scientists 293–297 publications: 29 scientists 298–302 publications: 23 scientists 303–307 publications: 39 scientists 308–312 publications: 34 scientists 313–317 publications: 23 scientists 318–322 publications: 18 scientists 323–327 publications: 18 scientists 328–332 publications: 21 scientists 333–337 publications: 20 scientists 338–342 publications: 15 scientists 343–347 publications: 13 scientists 348–352 publications: 24 scientists 353–357 publications: 25 scientists 358–362 publications: 10 scientists 363–367 publications: 20 scientists 368–372 publications: 17 scientists 373–377 publications: 18 scientists 378–382 publications: 15 scientists 383–387 publications: 7 scientists 388–392 publications: 22 scientists 393–397 publications: 9 scientists 398–402 publications: 11 scientists 403–407 publications: 16 scientists 408–412 publications: 4 scientists 413–417 publications: 5 scientists 418–422 publications: 14 scientists 423–427 publications: 8 scientists 428–432 publications: 7 scientists 433–437 publications: 7 scientists 438–442 publications: 10 scientists 443–447 publications: 10 scientists 448–452 publications: 3 scientists 453–457 publications: 9 scientists 458–462 publications: 11 scientists 463–467 publications: 4 scientists 468–472 publications: 6 scientists 473–477 publications: 11 scientists 478–482 publications: 8 scientists 483–487 publications: 2 scientists 488–492 publications: 3 scientists 493–497 publications: 4 scientists 498–502 publications: 4 scientists 503–507 publications: 4 scientists 508–509 publications: 4 scientists 510+ publications: 100 scientists
38 publications 510+

The last bar groups every scientist with 510 publications or more.

Indra Prakash D-index placement in Earth Science in 2026

The chart shows the D-index (discipline H-index) distribution of Earth Science scientists ranked by Research.com in 2026. The highlighted bar marks where Indra Prakash sits on this spectrum.

30 D-Index: 15 scientists 31 D-Index: 42 scientists 32 D-Index: 60 scientists 33 D-Index: 90 scientists 34 D-Index: 103 scientists 35 D-Index: 108 scientists 36 D-Index: 142 scientists 37 D-Index: 147 scientists 38 D-Index: 147 scientists 39 D-Index: 152 scientists 40 D-Index: 143 scientists 41 D-Index: 157 scientists 42 D-Index: 141 scientists 43 D-Index: 151 scientists 44 D-Index: 140 scientists 45 D-Index: 135 scientists 46 D-Index: 121 scientists 47 D-Index: 108 scientists 48 D-Index: 128 scientists 49 D-Index: 111 scientists 50 D-Index: 106 scientists 51 D-Index: 112 scientists 52 D-Index: 102 scientists 53 D-Index: 103 scientists 54 D-Index: 94 scientists 55 D-Index: 61 scientists 56 D-Index: 101 scientists 57 D-Index: 71 scientists 58 D-Index: 65 scientists 59 D-Index: 78 scientists 60 D-Index: 93 scientists 61 D-Index: 56 scientists 62 D-Index: 58 scientists 63 D-Index: 52 scientists 64 D-Index: 63 scientists 65 D-Index: 49 scientists 66 D-Index: 53 scientists 67 D-Index: 57 scientists 68 D-Index: 50 scientists 69 D-Index: 44 scientists 70 D-Index: 44 scientists 71 D-Index: 31 scientists 72 D-Index: 33 scientists 73 D-Index: 33 scientists 74 D-Index: 23 scientists 75 D-Index: 32 scientists 76 D-Index: 24 scientists 77 D-Index: 17 scientists 78 D-Index: 19 scientists 79 D-Index: 9 scientists 80 D-Index: 21 scientists 81 D-Index: 20 scientists 82 D-Index: 17 scientists 83 D-Index: 19 scientists 84 D-Index: 19 scientists 85 D-Index: 14 scientists 86 D-Index: 6 scientists 87 D-Index: 14 scientists 88 D-Index: 17 scientists 89 D-Index: 10 scientists 90 D-Index: 8 scientists 91 D-Index: 8 scientists 92 D-Index: 9 scientists 93 D-Index: 6 scientists 94+ D-Index: 98 scientists
30 D-Index 94+

The last bar groups every scientist with 94 D-Index or more.

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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