Research

My research integrates mathematical, computational, spatial, and data-driven methods to model, optimize, and improve complex water and interconnected infrastructure systems.

• Systems & Mathematical Modeling

System-of-Systems • Network/Graph Theory • Dynamic Systems • Interdependency Modeling • Reliability Analysis

• Hydrologic & Hydraulic Simulation

Water Distribution • Stormwater/Drainage • Surface–Groundwater Systems • Distributed Storage • LID/Green Infrastructure

• Optimization & Intelligent Control

Multi-objective Optimization • Genetic Algorithms • Model Predictive Control • Reinforcement Learning • Scenario Optimization

• Machine Learning & Data Analytics

Regression • SVM • Random Forest • Neural Networks • Forecasting • Missing-Data Imputation • Explainable AI

• Spatial, Risk & Decision Analytics

GIS • Spatial Statistics • Remote Sensing • MCDA • Vulnerability Mapping • Resilience & Sustainability Assessment • Uncertainty Analysis

Highlights:

• Examining the limitations of traditional centralized water systems under climate, infrastructure, and socioeconomic stresses.

• Demonstrating the operational resilience of water microgrids through lab-scale simulations under various disruption scenarios.

• Comparing centralized, decentralized, and hybrid configurations to evaluate system redundancy, flexibility, and recovery.

• Highlighting hybrid water microgrids as a promising solution for enhancing adaptive capacity, resilience, and sustainability in water supply systems.

Highlights:

• Synthesizing existing definitions, configurations, and applications of water microgrids as hybrid systems integrating centralized infrastructure with decentralized water sources, storage, treatment, and reuse.

• Identifying key benefits, research gaps, and technical, operational, economic, and regulatory challenges associated with advancing resilient and sustainable water microgrid systems.

Highlights:

• Comparing multiple spatial modeling approaches to identify and map groundwater potential zones using hydrogeological, topographic, environmental, and land-surface controlling factors.

• Evaluating model performance and spatial agreement to support more reliable groundwater-resource assessment, sustainable extraction planning, and location-specific water management in Keraniganj, Bangladesh.

Highlights:

• Understanding the current status of groundwater levels and associated geological conditions.

• Determining critical zones, recharging paths, and changing the nature of the Barind topology.

• Forecasting future groundwater levels and critical zones for sustainable Agri-heritage practice.

• Assuming sustainable futures in Agri-heritage helps local communities to become resilient.

Highlights:

• Analyzing hydrological behavior in Lewis County, NY using NRCS Curve Number (CN) modeling for the years 2001, 2011, and 2021.

• Integrating ArcGIS and Arc Hydro tools for LULC classification and CN value generation to assess infiltration and runoff potential.

• Identifying the influence of land use changes on CN classes, highlighting increased runoff risk due to urbanization.

• Emphasizing the need for improved data accuracy and future research to strengthen flood risk assessment and land management strategies.

Highlights:

• A brief analysis of route optimization methods, models along with application procedure in real world problems.

• An overview of transportation system of Bangladesh in the light of requirements needed for adapting Intelligent Transportation System.

• Compatibility of transportation system of Bangladesh to Intelligent Transportation System.

• Adaptability of transportation system of Bangladesh to Route Optimization Methods.

Highlights:

• Developing an intelligent model which can predict the number of private vehicles of the upcoming years considering multiple factors.

• Relationship with GDP, Population, Private vehicles, and other Prediction factors

• Inter-connection with Intelligent Transportation System

  Highlights:

• Weather Responsive Traffic Management (WRTM)

• Factors like weather conditions, human behavior, time period and others

• Time series data, Data behavior, Abnormal changes, Stationarity, Seasonality, Difference, Forecast, limitations, Optimization. 

• Bayesian setting, No complex iterative factor estimation, Big dataset, Grouping strategies, High-dimensional information, Countless focuses, K-Nearest Neighbor algorithm

Highlights:

• Time series analysis (AR, MA, ARMA, ARIMA, SARIMA )

• Stationary, Non-stationary, Data Differencing, Dickey Fuller test

• Trend analysis, Seasonality, Mean, Variance, Skewness, Kurtosis etc.

• ACF, PACF, ARIMA order, t-distribution

• Model Identification, Estimation, Diagnostic checking, Forecast

• AIC, BIC, Q-statistics, High R-square

• Residuals, Ljung-Box statistic, Normality, One-Sample Kolmogorov – Smirnov Test

• Artificial Neural Network, Fuzzy Neural Network, Kalman-Filtering algorithm

Highlights:

• Travel time, Normal distribution, Beta distribution, hypothesis testing

• Distribution characteristics, log-logistic, log-normal, Gompertz, Gamma, Weibull etc.

• Significance test, t-value, p-value

• Chi squared test

• Monte Carlo simulation, 14400 times simulation, Probability, Drift

• Capability of simulation technique in short term forecasting and deviation in others.

Highlights:

• Quantifying the suburbanites' observation

• Five-point scale, Likert scale analysis

• Real field data, quantitative, qualitative tools

• Compatibility of BLR model

• Result validation

• New route choice, Service quality, Safety etc.

Highlights:

• A brief review on existing ride sharing profile

• A comprehensive analysis on ride sharing mechanism, driver behavior, peoples acceptance, effectiveness and compatibility with region

• Comparison with each medium with specific indicators, Best case-worst case comparison 

Highlights:

• Analysis using uncertain factors, Prediction science

• Short, Very short, Intermediate, Long term forecast

• Multiple Linear Regression, Nonlinear Multivariable Regression

• Time Series Models, Volatility forecast models

• Monte Carlo Simulation

• Comparison, Abrupt change and their prediction capability etc.

Highlights:

• Standard and Modified Proctor test.

• Soil collection, Basic soil tests, Specific gravity, Liquid limit, Plastic limit, Plasticity index, Grain size analysis, Optimum moisture content, Maximum dry density etc.

• Classification, ACF, PACF, Linear and Non-linear Regression, Histogram, Boxplot, Cross correlogram etc.

• Support vector regression, Python coding environment.

• Optimum equation development, Validation

Highlights:

• Groundwater station data collection, Site visiting, hydrogeological study, Geostatistics

• Present condition mapping, ArcGIS, Kriging method

• Variogram, Q-Q plot, Normality check, Cross validation, Outliers 

• Exponential, Gaussian, Spherical models, ME, RMSE, ASE, RMSS

• Time series analysis, ARIMA

• Forecast, Surface mapping, Reasonable suggestion

This was an interesting topic for work. The quality of water was changing ridiculously from source to consumer in Rajshahi city through pipeline transportation. This work was needed to find out the actual cause and effect of transportation through pipeline on water quality changing.

• Pump starting hour, Running hour, Sample collection

• Samples from different distance from source

• Physical, Chemical & Biological tests

• Proper environment creation in lab, and maintaining

• Quality changing rates

Highlights:

• Different location, Different scenario of groundwater level

• Field work, Sample collection

• Physical, Chemical & Biological lab tests

• Comparison between supply water & ground water

• Numerical modelling

• Human-water intra environment relationship

Highlights:

• Groundwater sample collection

• Laboratory tests (Physical, Chemical, Biological tests)

• ArcGIS mapping

• Numerical modelling

Highlights:

• Groundwater sample collection

• Laboratory tests

• Impact Assessment

• Numerical analysis and model generating