Dr. Subbarayan Saravanan
Civil Engineering, National Institute of Technology Tiruchirappalli, Tiruchchirappalli, India
Associate Professor, National Institute of Technology Tiruch...
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Academic Career Length
21Years (2006-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications25 March 2020 in Modeling Earth Systems and Environment
Journal Article
TSThiyagarajan Saranya
Thiyagarajan SaranyaSubbarayan Saravanan1 January 2020 in HydroResearch
Journal Article
DADevanantham Abijith
Devanantham AbijithSubbarayan SaravananLSLeelambar Singh
Leelambar SinghJJJesudasan Jacinth Jennifer
Jesudasan Jacinth Jennifer
TSThiyagarajan Saranya
Thiyagarajan Saranya
KPK.S.S. Parthasarathy
K.S.S. Parthasarathy12 September 2021 in Environmental Science and Pollution Research
Journal Article
DADevanantham Abijith
Devanantham AbijithSubbarayan Saravanan3 February 2023 in Environmental Science and Pollution Research
Journal Article
NRNagireddy Masthan Reddy
Nagireddy Masthan ReddySubbarayan Saravanan10 July 2022 in Geocarto International
Journal Article
Subbarayan SaravananDADevanantham Abijith
Devanantham AbijithFlooding is one of the most challenging and important natural disasters to predict, it is becoming more frequent and more intense. The study area is badly damaged by devastating flood in 2015. We assessed the flood susceptibility to northern coastal area of Tamil Nadu using various machine learning algorithms such as Gradient Boosting Machine (GBM), XGBoost (XGB), Rotation Forest (RTF), Support Vector Machine (SVM), and Naive Bayes (NB). Google Earth Engine (GEE) is used to demarcate flooded areas using Sentinel-l and other multi-source geospatial data to generate influential factors. Recursive Feature Elimination (RFE) removes weak factors in this study. The flood susceptibility resultant map is classified into five classes very low, low, moderate, high, and very high. The GBM algorithm attained high classification accuracy with an area under the curve (AUC) value of 92%. The study area is urbanized and vulnerable identifying flood inundation useful for effective planning and implementation.
17 May 2021 in Geocarto International
Journal Article
JJJesudasan Jacinth Jennifer
Jesudasan Jacinth Jennifer
Subbarayan SaravananIdukki district faced adverse mishappenings during the 2018 Kerala landslides due to incessant torrential rainfall. This study emphasizes developing an efficient and accurate ANN model to integrate the data, process and generate landslide susceptibility maps. Fifteen conditioning factors that influence landslides' occurrence opted in the study constitutes 49 input neurons to the ANN model (L49). Seven inputs with high robustness were identified using the sensitivity analysis approach and were adopted to generate a new ANN model (L7). Both ANN models were processed to obtain an optimal output with lesser cross-entropy error. The landslide susceptibility maps derived from these ANN models show similar trends with the region's observed landslide locations. The ANN models were validated using ROC, and it provided a very good fit with AUC values of 0.91 and 0.83 as prediction rate for ANN models L49 and L7, respectively.
About Me
Dr. Saravanan Subbarayan earned his Ph.D. in Remote Sensing and Hydrology from the Indian Institute of Technology Roorkee, specializing in the Department of Civil Engineering. Currently, he serves as a faculty member at the National Institute of Technology Trichy, India. Throughout his career, Dr. Saravanan has conducted extensive research in hydrology, climate change, and the application of remote sensing in various disaster modeling contexts.
Dr. Saravanan's research scope encompasses a wide range of areas, including Rainfall-Runoff modeling, Flood susceptibility assessment, Flash flood analysis, flood forecasting, flood damage assessment, flood warning systems, and the utilization of Hyperspectral techniques for Mangrove forest assessment, Agriculture monitoring, Crop identification, and Agriculture crop damage assessment. He has also explored Synthetic Aperture Radar (SAR) applications in scenarios like Landslide prediction, flood monitoring, Cyclone impact assessment, coastal applications, and forest fire prediction. His contributions are reflected in the publication of over 70 papers in peer-reviewed journals, with a focus on hydrology, disaster management, and the application of Remote Sensing technologies
Recognized and listed among the top 2% of scientists globally in Stanford University's 2024 and 2025 list, curated by Stanford/Elsevier in the field of Earth & Environmental Sciences
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