MONOMOY GOSWAMI

@cit.ac.in

Professor of Civil Engineering
Central Institute of Technology Kokrajhar, Assam, INDIA

MONOMOY GOSWAMI


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https://researchid.co/monomoy
Dr. Monomoy Goswami is a Professor of Civil Engineering in Central Institute of Technology Kokrajhar, a deemed to be University under Govt. of India. His specializations include Engineering hydrology; Water resources engineering, development & management with extensive coverage of all aspects of hydropower projects; sustainability & impact studies; mathematical modelling; and Techno-economic assessment of projects.
Dr. Goswami did his PhD and Masters from the National University of Ireland Galway in Ireland having obtained a Bachelor degree in Civil Engineering from Regional Engineering College, now, NIT, Silchar in India.
His career spans more than 35 years as on November 2022 with spells of 25 years in industry, and the rest in academia.
Dr. Goswami has research publications in highly acclaimed international journals, and in proceedings of international conferences, besides chapters in edited books.

EDUCATION

PhD in Engineering Hydrology (National University of Ireland, Galway, IRELAND)
M.Sc in Hydrology (National University of Ireland, IRELAND)
Bachelor of Civil Engineering (Regional Engineering College, now, National Institute of Technology, Silchar, INDIA)

RESEARCH INTERESTS

Engineering hydrology; Water resources development, engineering & management; Hydropower project planning, design & engineering; Conceptual and data-driven modelling; Techno-economic evaluation of projects. Techno-economic assessment of projects.
8

Scopus Publications

703

Scholar Citations

10

Scholar h-index

12

Scholar i10-index

Scopus Publications

  • A "monster" that made the SMAR conceptual model "right for the wrong reasons"
    Monomoy Goswami, Kieran M. O'Connor
    Hydrological Sciences Journal, 2010
    In earlier studies involving simulation of the Fergus River flows in Ireland, the conceptual Soil Moisture Accounting and Routing (SMAR) model had been found to consistently outperform a number of black-box models. Subsequently, in investigating any loss of flow through this catchment's subsurface karstic features, it was verified from the overall long-term water balance that such losses were substantial. This raised the awkward question of why the volume-conservative SMAR model had performed so well on this considerably non-conservative catchment. Further analyses revealed that, to compensate for the excess volume of total runoff generated by the model's conservative water balance component, the memory length of the surface runoff response function had been unrealistically curtailed in the optimization process, effectively truncating that function and thereby violating the conservation property of the routing process. This embarrassing revelation called for reconsideration of the model structure to account more sensibly for actual losses, while still achieving high model efficiency. This paper highlights not only the discovery of the karstic Fergus catchment as a “hydrological monster”, in the context of the SMAR model, but also why conservative models perform poorly in such cases. In an attempt to “tame the monster”, better simulation of the observed flows was achieved by conceptually adapting the SMAR model, in a pragmatic empirical manner, by simply modifying its water balance component. Citation Goswami, M. & O'Connor, K. M. (2010) A “monster” that made the SMAR conceptual model “right for the wrong reasons”. Hydrol. Sci. J. 55(6), 913–927.
  • Rainfall-runoff modelling across southeast Australia: Datasets, models and results
    J Vaze, F H S Chiew, J M Perraud, N Viney, D Post, J Teng, B Wang, J Lerat, M Goswami
    Australian Journal of Water Resources, 2010
    This study describes a daily rainfall, potential evaporation and streamflow data set compiled for the important water resources region of southeast Australia, and the application of six commonly used lumped conceptual rainfall-runoff models to estimate daily runoff across the region. The daily climate data set and the daily modelled runoff are available from 1895 to 2008 at 0.05° grid resolution across the region. The modelling exercise indicates that the rainfall-runoff models can generally be calibrated to reproduce the daily observed streamflow (for 232 catchments in the high runoff generation areas), and the regionalisation results indicate that the use of optimised parameter values from a gauged catchment nearby can model runoff reasonably well in the ungauged areas. There are differences between the six models, but they are relatively small when used to describe aggregated results across large regions.
  • Comparative assessment of six automatic optimization techniques for calibration of a conceptual rainfall-runoff model
    MONOMOY GOSWAMI, KIERAN MICHAEL O'CONNOR
    Hydrological Sciences Journal, 2007
    In this application-based study, six automated strategies of parameter optimization are used for calibration of the conceptual soil moisture accounting and routing (SMAR) model for rainfall—runoff simulation in two catchments, one small and the other large. The methods used are: the genetic algorithm, particle swarm optimization, Rosenbrock's technique, shuffled complex evolution of the University of Arizona, simplex search, and simulated annealing. A comparative assessment is made using the Nash-Sutcliffe model efficiency index and the mean relative error (MRE) to evaluate the performance of each optimization method. It is found that the degree of variation of the values of the water balance parameters is generally less for the small catchment than for the large one. In the case of both catchments, the probabilistic global population-based optimization method of simulated annealing is considered best in terms of having the least variability of parameter values in successive tests, thereby alleviating the phenomenon of equifinality in parameter optimization, and also in producing the lowest MRE in verification.
  • Application of artificial neural networks for river flow simulation in three French catchments
    IAHS AISH Publication, 2007
  • Real-time flow forecasting in the absence of quantitative precipitation forecasts: A multi-model approach
    Monomoy Goswami, Kieran M. O’Connor
    Journal of Hydrology, 2007
  • Development of regionalisation procedures using a multi-model approach for flow simulation in an ungauged catchment
    M. Goswami, K.M. O’Connor, K.P. Bhattarai
    Journal of Hydrology, 2007
  • Flow simulation in an ungauged basin: An alternative approach to parameterization of a conceptual model using regional data
    IAHS AISH Publication, 2006
  • Assessing the performance of eight real-time updating models and procedures for the Brosna River
    M. Goswami, K. M. O'Connor, K. P. Bhattarai, A. Y. Shamseldin
    Hydrology and Earth System Sciences, 2005
    The flow forecasting performance of eight updating models, incorporated in the Galway River Flow Modelling and Forecasting System (GFMFS), was assessed using daily data (rainfall, evaporation and discharge) of the Irish Brosna catchment (1207 km2), considering their one to six days lead-time discharge forecasts. The Perfect Forecast of Input over the Forecast Lead-time scenario was adopted, where required, in place of actual rainfall forecasts. The eight updating models were: (i) the standard linear Auto-Regressive (AR) model, applied to the forecast errors (residuals) of a simulation (non-updating) rainfall-runoff model; (ii) the Neural Network Updating (NNU) model, also using such residuals as input; (iii) the Linear Transfer Function (LTF) model, applied to the simulated and the recently observed discharges; (iv) the Non-linear Auto-Regressive eXogenous-Input Model (NARXM), also a neural network-type structure, but having wide options of using recently observed values of one or more of the three data series, together with non-updated simulated outflows, as inputs; (v) the Parametric Simple Linear Model (PSLM), of LTF-type, using recent rainfall and observed discharge data; (vi) the Parametric Linear perturbation Model (PLPM), also of LTF-type, using recent rainfall and observed discharge data, (vii) n-AR, an AR model applied to the observed discharge series only, as a naïve updating model; and (viii) n-NARXM, a naive form of the NARXM, using only the observed discharge data, excluding exogenous inputs. The five GFMFS simulation (non-updating) models used were the non-parametric and parametric forms of the Simple Linear Model and of the Linear Perturbation Model, the Linearly-Varying Gain Factor Model, the Artificial Neural Network Model, and the conceptual Soil Moisture Accounting and Routing (SMAR) model. As the SMAR model performance was found to be the best among these models, in terms of the Nash-Sutcliffe R2 value, both in calibration and in verification, the simulated outflows of this model only were selected for the subsequent exercise of producing updated discharge forecasts. All the eight forms of updating models for producing lead-time discharge forecasts were found to be capable of producing relatively good lead-1 (1-day ahead) forecasts, with R2 values almost 90% or above. However, for higher lead time forecasts, only three updating models, viz., NARXM, LTF, and NNU, were found to be suitable, with lead-6 values of R2 about 90% or higher. Graphical comparisons were made of the lead-time forecasts for the two largest floods, one in the calibration period and the other in the verification period.

RECENT SCHOLAR PUBLICATIONS

  • Enhancing the Robustness of Temperature Simulations in India through a Bias-Corrected Multi-Model Ensemble Framework
    A Paul, M Goswami
    2026
  • Evaluating Future Warming Scenarios in mainland India based on Bias-Corrected CMIP6 Multi-Model Ensembles
    A Paul, M Goswami
    2026
  • Future precipitation characteristics of eight Tier I Urban Conglomerates amongst designated smart cities of India under selected Shared Socio-economic Pathways
    M Goswami, A Paul, M Goswami
    2026
  • Performance Comparison of Linear Relation Based and Machine Learning Based Rainfall-Runoff Models for Flow-Simulation for a Data-Scarce River Valley Project
    M Goswami, A Paul, M Goswami
    International Conference on Computing and Communication Networks, 56-70 , 2025
    2025
  • An Adaptive Neuro‐Fuzzy Inference System (ANFIS) and a Wavelet‐Integrated ANFIS (WANFIS) for Univariate Bias‐Correction of GCM‐Simulated Temperature
    A Paul, M Goswami
    International Journal of Climatology 45 (7), e8816 , 2025
    2025
    Citations: 1
  • Comprehensive Analysis of Landslide Susceptibility Factors in Assam: A Case
    D Saikia, M Goswami, R Zaman, M Kalita
    Soft Computing and Geospatial Techniques in Water Resources Engineering … , 2024
    2024
  • Methods of releasing environmental flow across a hydraulic structure: a case study of a ugandan project requiring no or minimal operator control
    M Goswami, A Paul, M Ao
    Proceedings of the 10th International Symposium on Hydraulic Structures … , 2024
    2024
    Citations: 1
  • Impact of Climate Change on Landslide Occurrence: A Case Study of Dima Hasao District of Northeast India
    D Saikia, M Goswami
    International Conference on Hydraulics, Water Resources and Coastal … , 2023
    2023
    Citations: 1
  • Comprehensive Analysis of Landslide Susceptibility Factors in Assam: A Case Study
    D Saikia, M Goswami, R Zaman, M Kalita
    International Conference on Hydraulics, Water Resources and Coastal … , 2023
    2023
  • Generating design flood hydrographs by parameterizing the characteristic flood hydrograph at a site using only flow data
    M Goswami
    Hydrological Sciences Journal 67 (16), 2505-2523 , 2022
    2022
    Citations: 12
  • Combined Use of Selected UX Research Techniques and Creation of User Persona for Design and Evaluation of Sustainable e-Commerce Apps—A Case Study
    I Goswami, M Goswami
    International Conference of the Indian Society of Ergonomics, 1481-1493 , 2021
    2021
    Citations: 3
  • Trend analysis of ground-water levels and rainfall to assess sustainability of groundwater in Kamrup Metropolitan District of Assam in Northeast India
    M Goswami, D Rabha
    Roorkee Water Conclave , 2020
    2020
    Citations: 16
  • Flood studies update
    K O’Connor, M Goswami, D Faulkner
    Technical Research Report 3, 186 , 2014
    2014
    Citations: 12
  • Rainfall-runoff modelling across southeast Australia: datasets, models and results
    J Vaze, FHS Chiew, JM Perraud, N Viney, D Post, J Teng, B Wang, J Lerat, ...
    Australasian Journal of Water Resources 14 (2), 101-116 , 2011
    2011
    Citations: 131
  • A “monster” that made the SMAR conceptual model “right for the wrong reasons”
    M Goswami, KM O'Connor
    Hydrological Sciences Journal–Journal des Sciences Hydrologiques 55 (6), 913-927 , 2010
    2010
    Citations: 25
  • Comparative assessment of six automatic optimization techniques for calibration of a conceptual rainfall—runoff model
    M Goswami, KM O'CONNOR
    Hydrological sciences journal 52 (3), 432-449 , 2007
    2007
    Citations: 57
  • Real-time flow forecasting in the absence of quantitative precipitation forecasts: a multi-model approach
    M Goswami, KM O’Connor
    Journal of Hydrology 334 (1-2), 125-140 , 2007
    2007
    Citations: 62
  • Development of regionalisation procedures using a multi-model approach for flow simulation in an ungauged catchment
    M Goswami, KM O’connor, KP Bhattarai
    Journal of Hydrology 333 (2-4), 517-531 , 2007
    2007
    Citations: 151
  • Application of Artificial Neural Networks for river flow simulation in three French catchments
    M Goswami, KM O Connor
    IAHS publication 310, 267 , 2007
    2007
    Citations: 7
  • Flow simulation in an ungauged basin: an alternative approach to parameterization of a conceptual model using regional data
    M Goswami, KM O Connor
    IAHS PUBLICATION 307, 149 , 2006
    2006
    Citations: 5

MOST CITED SCHOLAR PUBLICATIONS

  • Development of regionalisation procedures using a multi-model approach for flow simulation in an ungauged catchment
    M Goswami, KM O’connor, KP Bhattarai
    Journal of Hydrology 333 (2-4), 517-531 , 2007
    2007
    Citations: 151
  • Assessing the performance of eight real-time updating models and procedures for the Brosna River
    M Goswami, KM O'connor, KP Bhattarai, AY Shamseldin
    Hydrology and Earth System Sciences 9 (4), 394-411 , 2005
    2005
    Citations: 137
  • Rainfall-runoff modelling across southeast Australia: datasets, models and results
    J Vaze, FHS Chiew, JM Perraud, N Viney, D Post, J Teng, B Wang, J Lerat, ...
    Australasian Journal of Water Resources 14 (2), 101-116 , 2011
    2011
    Citations: 131
  • Real-time flow forecasting in the absence of quantitative precipitation forecasts: a multi-model approach
    M Goswami, KM O’Connor
    Journal of Hydrology 334 (1-2), 125-140 , 2007
    2007
    Citations: 62
  • Comparative assessment of six automatic optimization techniques for calibration of a conceptual rainfall—runoff model
    M Goswami, KM O'CONNOR
    Hydrological sciences journal 52 (3), 432-449 , 2007
    2007
    Citations: 57
  • Structures and performances of five rainfall-runoff models for continuous river-flow simulation
    M Goswami, KM O’connor, AY Shamseldin
    2002
    Citations: 33
  • A “monster” that made the SMAR conceptual model “right for the wrong reasons”
    M Goswami, KM O'Connor
    Hydrological Sciences Journal–Journal des Sciences Hydrologiques 55 (6), 913-927 , 2010
    2010
    Citations: 25
  • Trend analysis of ground-water levels and rainfall to assess sustainability of groundwater in Kamrup Metropolitan District of Assam in Northeast India
    M Goswami, D Rabha
    Roorkee Water Conclave , 2020
    2020
    Citations: 16
  • Generating design flood hydrographs by parameterizing the characteristic flood hydrograph at a site using only flow data
    M Goswami
    Hydrological Sciences Journal 67 (16), 2505-2523 , 2022
    2022
    Citations: 12
  • Flood studies update
    K O’Connor, M Goswami, D Faulkner
    Technical Research Report 3, 186 , 2014
    2014
    Citations: 12
  • Application of a conceptual rainfall-runoff simulation model to three European catchments characterised by non-conservative system behaviour
    M Goswami, KM O’Connor
    Proc. Int. Conf. Hydrological Perspectives for Sustainable Development, 117-130 , 2005
    2005
    Citations: 10
  • The Development of The Galway Real-Time River Flow Forecasting System (GFFS)
    KM O’Connor, M Goswami, GC Liang, RK Kachroo, AY Shamseldin
    Proc. 19th European Conference on Sustainable Use of Land and Water of the … , 2001
    2001
    Citations: 10
  • Application of the artificial neural network (ANN) in flood forecasting on a karstic catchment
    L Xiong, KM O Connor, M Goswami
    PROCEEDINGS OF THE CONGRESS-INTERNATIONAL ASSOCIATION FOR HYDRAULIC RESEARCH … , 2001
    2001
    Citations: 9
  • Application of Artificial Neural Networks for river flow simulation in three French catchments
    M Goswami, KM O Connor
    IAHS publication 310, 267 , 2007
    2007
    Citations: 7
  • A comparison of the lead-time discharge forecasts of the ‘Perfect’and ‘Naïve-AR’quantitative precipitation forecast (QPF) input scenarios, to asses the value of having good QPFs
    KM O’Connor, M Goswami, KP Bhattarai, AY Shamseldin
    Workshop Paper presented by KM O’Connor at the ESF LESC Exploratory Workshop … , 2003
    2003
    Citations: 6
  • Real-time river flow forecasting for the Brosna catchment in Ireland using eight updating models
    M Goswami, KM O'Connor, KP Bhattarai, A Shamseldin
    Proceedings of the International Conference on Advances in Flood Forecasting … , 2003
    2003
    Citations: 6
  • Rainfall–runoff modelling of two Irish catchments (one karstic and one non-karstic)
    M Goswami, KM O’Connor, AY Shamseldin
    Proceedings of the Third Inter-Celtic Colloquium on Hydrology and Management … , 2002
    2002
    Citations: 6
  • Flow simulation in an ungauged basin: an alternative approach to parameterization of a conceptual model using regional data
    M Goswami, KM O Connor
    IAHS PUBLICATION 307, 149 , 2006
    2006
    Citations: 5
  • Combined Use of Selected UX Research Techniques and Creation of User Persona for Design and Evaluation of Sustainable e-Commerce Apps—A Case Study
    I Goswami, M Goswami
    International Conference of the Indian Society of Ergonomics, 1481-1493 , 2021
    2021
    Citations: 3
  • An Adaptive Neuro‐Fuzzy Inference System (ANFIS) and a Wavelet‐Integrated ANFIS (WANFIS) for Univariate Bias‐Correction of GCM‐Simulated Temperature
    A Paul, M Goswami
    International Journal of Climatology 45 (7), e8816 , 2025
    2025
    Citations: 1