Dr Abhirup Dikshit
Research Associate

Dr Abhirup Dikshit

Science
Climate Change Research Centre

Abhirup Dikshit is a geospatial ecohydrologist using advanced remote sensing tools and machine learning models to monitor vegetation health and function in the face of climate change, land use, and other major disturbance events.
Abhirup’s Ph.D. was on examining large-scale soil-vegetation-climate interactions and processes with remotely sensed measurements from satellites under the mentorship of Prof. Biswajeet Pradhan & Prof. Alfredo Huete. Here, he received valuable professional mentoring in ecology, machine learning applications, and remote sensing to conduct research on ecological resilience, geospatial modeling, and environmental monitoring. He specializes in the use of next-generation geostationary satellites to examine extreme dry events, vegetation dynamics, and flash droughts to better understand Australia's ecosystems.

  • Book Chapters | 2022
    Dikshit A; Satyam N, 2022, 'Landslide Early Warning System in Kalimpong, West Bengal', in , pp. 261 - 267, http://dx.doi.org/10.1007/978-981-16-6456-4_28
    Book Chapters | 2021
    Dikshit A; Satyam N, 2021, 'Probabilistic assessment of Paglajhora landslide using SLOPE/W', in , pp. 129 - 136, http://dx.doi.org/10.1007/978-981-15-6233-4_9
    Book Chapters | 2020
    Dorji L; Sarkar R; Lhachey U; Sharma V; Tshewang ; Dikshit A; Kurar R, 2020, 'An Evaluation of Hydrological Modeling Using SCS-CN Method in Ungauged Om Chhu River Basin of Phuentsholing, Bhutan', in An Interdisciplinary Approach for Disaster Resilience and Sustainability, Springer Singapore, pp. 111 - 121, http://dx.doi.org/10.1007/978-981-32-9527-8_7
    Book Chapters | 2020
    Sarkar R; Narang K; Sharma P; Pal I; Dikshit A, 2020, 'Risk Identification, Assessment, and Disaster Risk Reduction of a Building Information Modeling (BIM)-Implemented Project', in An Interdisciplinary Approach for Disaster Resilience and Sustainability, Springer Singapore, pp. 289 - 309, http://dx.doi.org/10.1007/978-981-32-9527-8_17
  • Journal articles | 2023
    Pradhan B; Dikshit A; Lee S; Kim H, 2023, 'An explainable AI (XAI) model for landslide susceptibility modeling', Applied Soft Computing, 142, http://dx.doi.org/10.1016/j.asoc.2023.110324
    Journal articles | 2023
    Pradhan B; Lee S; Dikshit A; Kim H, 2023, 'Spatial flood susceptibility mapping using an explainable artificial intelligence (XAI) model', Geoscience Frontiers, 14, http://dx.doi.org/10.1016/j.gsf.2023.101625
    Journal articles | 2022
    Abdollahi A; Liu Y; Pradhan B; Huete A; Dikshit A; Nguyen Tran N, 2022, 'Short-time-series grassland mapping using Sentinel-2 imagery and deep learning-based architecture', Egyptian Journal of Remote Sensing and Space Science, 25, pp. 673 - 685, http://dx.doi.org/10.1016/j.ejrs.2022.06.002
    Journal articles | 2022
    Dikshit A; Pradhan B; Assiri ME; Almazroui M; Park HJ, 2022, 'Solving transparency in drought forecasting using attention models', Science of the Total Environment, 837, http://dx.doi.org/10.1016/j.scitotenv.2022.155856
    Journal articles | 2022
    Dikshit A; Pradhan B; Huete A; Park HJ, 2022, 'Spatial based drought assessment: Where are we heading? A review on the current status and future', Science of the Total Environment, 844, http://dx.doi.org/10.1016/j.scitotenv.2022.157239
    Journal articles | 2022
    Dikshit A; Pradhan B; Santosh M, 2022, 'Artificial neural networks in drought prediction in the 21st century–A scientometric analysis', Applied Soft Computing, 114, http://dx.doi.org/10.1016/j.asoc.2021.108080
    Journal articles | 2022
    Serbouti I; Raji M; Hakdaoui M; El Kamel F; Pradhan B; Gite S; Alamri A; Maulud KNA; Dikshit A, 2022, 'Improved Lithological Map of Large Complex Semi-Arid Regions Using Spectral and Textural Datasets within Google Earth Engine and Fused Machine Learning Multi-Classifiers', Remote Sensing, 14, http://dx.doi.org/10.3390/rs14215498
    Journal articles | 2022
    Youssef AM; Pradhan B; Dikshit A; Al-Katheri MM; Matar SS; Mahdi AM, 2022, 'Landslide susceptibility mapping using CNN-1D and 2D deep learning algorithms: comparison of their performance at Asir Region, KSA', Bulletin of Engineering Geology and the Environment, 81, http://dx.doi.org/10.1007/s10064-022-02657-4
    Journal articles | 2022
    Youssef AM; Pradhan B; Dikshit A; Mahdi AM, 2022, 'Comparative study of convolutional neural network (CNN) and support vector machine (SVM) for flood susceptibility mapping: a case study at Ras Gharib, Red Sea, Egypt', Geocarto International, 37, pp. 11088 - 11115, http://dx.doi.org/10.1080/10106049.2022.2046866
    Journal articles | 2021
    Dikshit A; Pradhan B; Alamri AM, 2021, 'Long lead time drought forecasting using lagged climate variables and a stacked long short-term memory model', Science of the Total Environment, 755, http://dx.doi.org/10.1016/j.scitotenv.2020.142638
    Journal articles | 2021
    Dikshit A; Pradhan B; Alamri AM, 2021, 'Pathways and challenges of the application of artificial intelligence to geohazards modelling', Gondwana Research, 100, pp. 290 - 301, http://dx.doi.org/10.1016/j.gr.2020.08.007
    Journal articles | 2021
    Dikshit A; Pradhan B; Huete A, 2021, 'An improved SPEI drought forecasting approach using the long short-term memory neural network', Journal of Environmental Management, 283, http://dx.doi.org/10.1016/j.jenvman.2021.111979
    Journal articles | 2021
    Dikshit A; Pradhan B, 2021, 'Explainable AI in drought forecasting', Machine Learning with Applications, 6, pp. 100192 - 100192, http://dx.doi.org/10.1016/j.mlwa.2021.100192
    Journal articles | 2021
    Dikshit A; Pradhan B, 2021, 'Interpretable and explainable AI (XAI) model for spatial drought prediction', Science of the Total Environment, 801, http://dx.doi.org/10.1016/j.scitotenv.2021.149797
    Journal articles | 2021
    Saha S; Kundu B; Paul GC; Mukherjee K; Pradhan B; Dikshit A; Abdul Maulud KN; Alamri AM, 2021, 'Spatial assessment of drought vulnerability using fuzzy-analytical hierarchical process: a case study at the Indian state of Odisha', Geomatics, Natural Hazards and Risk, 12, pp. 123 - 153, http://dx.doi.org/10.1080/19475705.2020.1861114
    Journal articles | 2021
    Saha S; Roy J; Hembram TK; Pradhan B; Dikshit A; Abdul Maulud KN; Alamri AM, 2021, 'Comparison between deep learning and tree‐based machine learning approaches for landslide susceptibility mapping', Water (Switzerland), 13, http://dx.doi.org/10.3390/w13192664
    Journal articles | 2020
    Dikshit A; Pradhan B; Alamri AM, 2020, 'Short-term spatio-temporal drought forecasting using random forests model at New South Wales, Australia', Applied Sciences (Switzerland), 10, http://dx.doi.org/10.3390/app10124254
    Journal articles | 2020
    Dikshit A; Pradhan B; Alamri AM, 2020, 'Temporal hydrological drought index forecasting for New South Wales, Australia using machine learning approaches', Atmosphere, 11, http://dx.doi.org/10.3390/atmos11060585
    Journal articles | 2020
    Dikshit A; Sarkar R; Pradhan B; Acharya S; Alamri AM, 2020, 'Spatial landslide risk assessment at Phuentsholing, Bhutan', Geosciences (Switzerland), 10, http://dx.doi.org/10.3390/geosciences10040131
    Journal articles | 2020
    Dikshit A; Sarkar R; Pradhan B; Jena R; Drukpa D; Alamri AM, 2020, 'Temporal probability assessment and its use in landslide susceptibility mapping for Eastern Bhutan', Water (Switzerland), 12, http://dx.doi.org/10.3390/w12010267
    Journal articles | 2020
    Dikshit A; Sarkar R; Pradhan B; Segoni S; Alamri AM, 2020, 'Rainfall induced landslide studies in indian himalayan region: A critical review', Applied Sciences (Switzerland), 10, http://dx.doi.org/10.3390/app10072466
    Journal articles | 2020
    Dikshit A; Satyam N; Pradhan B; Kushal S, 2020, 'Estimating rainfall threshold and temporal probability for landslide occurrences in Darjeeling Himalayas', Geosciences Journal, 24, pp. 225 - 233, http://dx.doi.org/10.1007/s12303-020-0001-3
    Journal articles | 2020
    Shukla N; Pradhan B; Dikshit A; Chakraborty S; Alamri AM, 2020, 'A review of models used for investigating barriers to healthcare access in Australia', International Journal of Environmental Research and Public Health, 17, pp. 1 - 19, http://dx.doi.org/10.3390/ijerph17114087
    Journal articles | 2020
    Tempa K; Sarkar R; Dikshit A; Pradhan B; Simonelli AL; Acharya S; Alamri AM, 2020, 'Parametric study of local site response for bedrock ground motion to earthquake in Phuentsholing, Bhutan', Sustainability (Switzerland), 12, http://dx.doi.org/10.3390/su12135273
    Journal articles | 2019
    Dikshit A; Sarkar R; Pradhan B; Acharya S; Dorji K, 2019, 'Estimating rainfall thresholds for landslide occurrence in the Bhutan Himalayas', Water (Switzerland), 11, http://dx.doi.org/10.3390/w11081616
    Journal articles | 2019
    Dikshit A; Satyam N; Pradhan B, 2019, 'Estimation of Rainfall-Induced Landslides Using the TRIGRS Model', Earth Systems and Environment, 3, pp. 575 - 584, http://dx.doi.org/10.1007/s41748-019-00125-w
    Journal articles | 2019
    Dikshit A; Satyam N, 2019, 'Probabilistic rainfall thresholds in Chibo, India: estimation and validation using monitoring system', Journal of Mountain Science, 16, pp. 870 - 883, http://dx.doi.org/10.1007/s11629-018-5189-6
    Journal articles | 2019
    Gariano SL; Sarkar R; Dikshit A; Dorji K; Brunetti MT; Peruccacci S; Melillo M, 2019, 'Automatic calculation of rainfall thresholds for landslide occurrence in Chukha Dzongkhag, Bhutan', Bulletin of Engineering Geology and the Environment, 78, pp. 4325 - 4332, http://dx.doi.org/10.1007/s10064-018-1415-2
    Journal articles | 2019
    Sarkar R; Dikshit A; Hazarika H; Yamada K; Subba K, 2019, 'Probabilistic rainfall thresholds for landslide occurrences in Bhutan', International Journal of Recent Technology and Engineering, 8, pp. 737 - 742, http://dx.doi.org/10.35940/ijrte.B1132.0982S1019
    Journal articles | 2019
    Teja TS; Dikshit A; Satyam N, 2019, 'Determination of rainfall thresholds for landslide prediction using an algorithm-based approach: Case study in the Darjeeling Himalayas, India', Geosciences (Switzerland), 9, http://dx.doi.org/10.3390/geosciences9070302
    Journal articles | 2018
    Dikshit A; Sarkar R; Satyam N, 2018, 'Probabilistic approach toward Darjeeling Himalayas landslides-A case study', Cogent Engineering, 5, pp. 1 - 11, http://dx.doi.org/10.1080/23311916.2018.1537539
    Journal articles | 2018
    Dikshit A; Satyam DN; Towhata I, 2018, 'Early warning system using tilt sensors in Chibo, Kalimpong, Darjeeling Himalayas, India', Natural Hazards, 94, pp. 727 - 741, http://dx.doi.org/10.1007/s11069-018-3417-6
    Journal articles | 2018
    Dikshit A; Satyam DN, 2018, 'Estimation of rainfall thresholds for landslide occurrences in Kalimpong, India', Innovative Infrastructure Solutions, 3, http://dx.doi.org/10.1007/s41062-018-0132-9
  • Preprints | 2017
    Dikshit A; Satyam N, 2017, Application of FLaIR model for early warning system in Chibo Pashyor, Kalimpong, India for rainfall-induced landslides, , http://dx.doi.org/10.5194/nhess-2017-295

  • ​​​​​​2021 - Recipient of the Cross-Faculty Project funded by the School of Information Systems and Modelling, UTS.
  • 2020 - Recipient of the ISM Research Incentivisation by the School of Information Systems and Modelling, UTS.
  • 2019 - Recipient of the International Research Training Program Scholarship (IRTP) funded by the Australian Government under the Department of Education and Training.
  • 2019 - Co-CI - Impact of climate change, Land use land cover, and socio-economic dynamics on landslides in South and East Asia. (Granted by International Science Council (ICSU).

  • 2022 - HDR Excellence Awards  - UTS Faculty of Engineering & IT (Commendation)
  • 2021 - Best Paper Award in Gondwana Research, Elsevier for the article titled ’Pathways and challenges of the application of artificial intelligence to geohazards modelling’. This article has been recognized as a 'Highly Cited Paper' by Clarivate Analytics, Web of Science. Link: https://doi.org/10.1016/j.gr.2022.11.009
  • 2020 - Best Paper Award in Atmosphere, MDPI for the article titled ’Temporal Hydrological Drought Index Forecasting for New South Wales, Australia Using Machine Learning Approaches’. Link: https://www.mdpi.com/journal/atmosphere/awards/1600 
  • 2020 - Won 2nd prize in the IEEE NSW Chapter computational Challenge Competition, 2020, presenting work on Drought Forecasting.
  • 2020 - Won 2nd prize in the Urban MAXAR Challenge, 2020 a national challenge competition addressing solutions to Australia's biggest challenges.

  • Understanding vegetation response during extreme dry events.
  • Analysing ecosystems function by tracking important sub-daily and daily processes.
  • Improve our understanding and modelling of unusual bushfire behaviour.
  • Analyzing the land-atmosphere feedback mechanism post bushfires.
  • Empirical modeling of of rainfall-induced landslides.