SS
Srikrishnan Siva Subramanian
Also known as: Srikrishnan, S., Subramanian, S.S.
Indian Institute of Technology Roorkee, Indian Institute of Technology Gandhinagar, Hokkaido University, Bharathidasan University, Chengdu University of Technology
0000-0003-4004-0894
Employment
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Indian Institute of Technology Roorkee Assistant Professor2021 - Present
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Indian Institute of Technology Gandhinagar Post-Doctoral Researcher2021 - 2021
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Chengdu University of Technology Researcher2018 - 2021
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Hokkaido University Post Doctoral Researcher2017 - 2018
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Central Institute of Mining and Fuel Research CSIR Project Assistant2013 - 2014
Education
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Hokkaido University Doctor of Philosophy2014 - 2017
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Bharathidasan University Masters2007 - 2012
Projects & Funding
Projects & funding information is unavailable.
Publications (45)
- ALERT: A Scalable Cloud-Native Framework for Satellite Rainfall-Driven Landslide Early Warning in Data-Scarce Regions Save
- Beyond Generalisation: Regionally Calibrated Feature Selection with Machine Learning for Data-driven Landslide Hazard Assessment in the Himalayas Save
- Reconstructing seismic landslide spatial records in data-scarce Indian Himalayas: a polygon-based inventory and susceptibility assessment for the 1999 Chamoli earthquake Save
- Extreme weather event-driven evolution of mass movements over upper, middle, and paraglacial zones of a Central Himalayan catchment Save
- Landslide Dam Susceptibility Mapping in the Indian Himalayas: A Random Forest Approach with Cross-Catchment Validation Save
- Exploring the role and connections between rainfall and soil moisture over cascading hazards in the Himalayas Save
- Design of a stability assessment approach for rainfall-induced debris slides using physical modelling and multi-scale numerical simulations Save
- Bed Wetness Controls Debris Flow Initiation and Intensity-Duration Thresholds Save
- Modelling Initiation and Intensity‐Duration Thresholds of Extreme Precipitation–Induced Debris Flows: A Case Study of the August 2020 Pettimudi Event Save
- A Data-Driven Hybrid Approach Integrating Frequency Ratio, Analytic Hierarchy Process, and Random Forest with Entropy-Based Information Gain-Derived Weights for Landslide Susceptibility Assessment Save