Siddik Barbhuiya
About Me
Hello! I'm Siddik Barbhuiya, a devoted scholar specializing in Hydrology and Water Resource Engineering. My expertise lies in Hydrological Modeling with a particular emphasis on integrating Artificial Intelligence/Machine Learning (AI/ML) in Water Resource Engineering.
A significant aspect of my research delves into the examination of Non-stationary Hydroclimatic Variables, a realm I believe holds immense potential for innovation. I am incessantly driven towards exploring fresh concepts, methodologies, and ideas in the water resource arena, viewing every project as a stepping stone towards advancing knowledge in this crucial domain.
Prior to my current endeavors, I earned my master's degree in Water Resource Engineering from Maulana Azad National Institute of Technology (MANIT), Bhopal. This journey fortified a robust foundation for my ongoing research endeavors and amplified my resolve to make substantial contributions to the field of water resources.
This website serves as a conduit for sharing my work, insights, and the latest breakthroughs in hydrology and water resource engineering. I extend a warm invitation to researchers, students, and all those intrigued by these topics to join me in this exhilarating expedition. Together, let's delve into the intricacies of water resources and collaborate to tackle the pressing water-centric challenges that our globe faces.
Education
PhD: Indian Institue of Technology Mandi (Pursuing)
Working on: Hydrological Modelling, Extreme events, DL based RR model, Climate Change
M.Tech: Maulana Azad National Institute of Technology(MANIT) Bhopal (2020-2022)
Thesis: Assesment of Streamflow in Ungaguged Basin Using Physical Similarity Method
B.Tech: Maulana Abul Kalam Azad University of Technology (Formerly known as WBUT)(2016-2020)
H.S: Holy Cross School, Silchar
Experience
Junior Researcher at National Institute of Hydrology (CIHRC BHOPAL) from May to Dec 2022
Project: 1 Water Availability Assesment for Project Formulation Madhya Pradesh
Calculated Physical characteristics of Catchments by using ArcGIS
Developed the Rainfall-Runoff model in Gauged catchments using
GR4J,GR5J,GR6J, and LSTM model
Project:2 Reassessment of evapotranspiration estimation for irrigation planning in MP
Reviewed the different ETo estimation methods.
Calculated the ETo using different methods and compared with station-based recorded ETo data.
Publications
Journal Paper
Barbhuiya, S., Raghuvanshi, A.S. & Tiwari, H.L. Assessment of streamflow in the ungauged basin by using physical similarity approach. Arab J Geosci 16, 672 (2023). https://doi.org/10.1007/s12517-023-11786-3
Barbhuiya, S., Ramadas, M.,& Manekar, A. (2024).Comparative Analysis of Traditional and Machine Learning Models in Streamflow Prediction: Insights from SWAT, GR4J, and Advanced ML Approaches [Accepted to Journal of Earth System and Science][Impact factor 1.912]
Book Chapter
Raghuvanshi, A.S., Barbhuiya, S.A., Tiwari, H.L. (2023). Performance Evaluation of Lumped Conceptual Rainfall-Runoff Genie Rural (GR) Hydrological Models for Streamflow Simulation. In: Timbadiya, P.V., Patel, P.L., Singh, V.P., Sharma, P.J. (eds) Hydrology and Hydrologic Modelling. HYDRO 2021. Lecture Notes in Civil Engineering, vol 312. Springer, Singapore. https://doi.org/10.1007/978-981-19-9147-9_22
Barbhuiya, S., Ramadas, M., Biswal, S.S. (2023). Nonstationary Flood Frequency Analysis: Review of Methods and Models. In: Pandey, M., Gupta, A.K., Oliveto, G. (eds) River, Sediment and Hydrological Extremes: Causes, Impacts, and Management. Disaster Resilience and Green Growth. Springer, Singapore. https://doi.org/10.1007/978-981-99-4811-6_15
Barbhuiya, S., Sharma, S., Pathania, A. and Gupta, V. (2024) ‘Assessing the Impacts of Climate Change on Hydroclimatic Regimes in Beas River Basin’, Communicated to a book titled Navigating the Nexus: Hydrology, Agriculture, Pollution, and Climate Change
Conference Attended
Barbhuiya, S., Ramadas, M., Jena, S., and Biswal, S.: Trend Analysis and Forecasting of Streamflow in the Upper Narmada Basin using Random Forest (RF) and Long Short-Term Memory (LSTM) Models, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-10952, https://doi.org/10.5194/egusphere-egu23-10952 , 2023.
Barbhuiya, S., Ramdas, M., Kartick, G. M., & Biswal, S. (2023). Runoff prediction in a tropical agricultural watershed: a comparison between machine learning-based and conceptual hydrological model. In 12th World Congress on Water Resources and Environment (EWRA 2023) “Managing Water-Energy-Land-Food under Climatic, Environmental, and Social Instability” Thessaloniki, Greece, 27 June - 1 July 2023.