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    Swarnadeepa Chakraborty

    About


    Designation: Member
    Degree: Ph. D.
    Fellowship: INSTITUTE
    Enrollment No.: 122017AE05, Date: 17 Jul 2017
    Registration No.: PhD/R/2018/0078, Date: 12 Sep 2018Supervisor: Sujata Biswas

    Academic Qualification

    Ph.D.


    Research Area


    Topic: Prediction of stage and discharge by hybrid machine learning techniques and flood hazard, vulnerability and risk assessment of a catchment using HEC-RAS and GIS based MCDA

    Swarnadeepa is an Assistant Professor at Vasavi College of Engineering in Hyderabad, where she excels in both research and teaching. Her expertise spans hydrological modeling, time series forecasting using stochastic models and machine learning tools, and the application of GIS and remote sensing in water resource management. In the classroom, she is dedicated to mentoring students and delivering engaging lectures that bridge theoretical knowledge with practical applications. Her commitment to education, combined with her cutting-edge research, significantly enriches the academic experience and prepares students for careers in hydrology and environmental engineering. Swarnadeepa's work integrates advanced technology to tackle water management challenges, fostering innovation and sustainability in the field.


    Publications


    1. Chakraborty, S. and Biswas, S., River discharge prediction using wavelet based artificial neural network and long short-term memory models: a case study of Teesta River Basin, India. Stochastic Environmental Research and Risk Assessment, 37, 3163-3184, Stochastic Environmental Research and Risk Assessment, 2023

    2. Chakraborty, S. and Biswas, S., Simulation of Flow at an ungauged river site based on HEC-HMS model for a mountainous river basin, 14, Arabian Journal of Geosciences, 2021

    3. Chakraborty, S. and Biswas, S., Application of Geographic Information System and HEC-RAS in Flood Risk Mapping of a Catchment, Applications of Geomatics in Civil Engineering, Lecture Notes in Civil Engineering, Springer Nature Singapore Pte Ltd.33, 215-224, , 2018

    4. Swarndeepa Chakraborty, Sujata Biswas, Application of wavelet-based multivariate long short-term memory models in prediction of stage for Teesta River, India, Journal of Hydroinformatics, 2024