Rajib Kumar Bhattacharjya

Solving Groundwater Flow Inverse Problem Using Spreadsheet Solver

A simple spreadsheet cell-oriented methodology is proposed for solving a groundwater flow inverse problem. Initially, the numerical simulation of the groundwater flow process is carried out by using an embedded optimization approach and spreadsheet solver. The result of the simulation is compared with the result of a GMS MODFLOW simulation. The comparison shows that the result of the spreadsheet simulation is similar to the result of the GMS MODFLOW simulation. Two groundwater inverse problems are then solved using the proposed methodology. The first problem estimates the unknown pumping rates of an aquifer, and the second problem estimates the aquifer transmissivity. Using the spreadsheet method, it is easy to define boundary conditions and to initialize starting values as well as their visualization. The methodology is simple in concept and can be used for solving graduate- and undergraduate-level groundwater simulation and inverse problems. The methodology may also be used by the practicing engineer fo...

Optimal design of unit hydrographs using probability distribution and genetic algorithms

A nonlinear optimization model is developed to transmute a unit hydrograph into a probability distribution function (PDF). The objective function is to minimize the sum of the square of the deviation between predicted and actual direct runoff hydrograph of a watershed. The predicted runoff hydrograph is estimated by using a PDF. In a unit hydrograph, the depth of rainfall excess must be unity and the ordinates must be positive. Incorporation of a PDF ensures that the depth of rainfall excess for the unit hydrograph is unity, and the ordinates are also positive. Unit hydrograph ordinates are in terms of intensity of rainfall excess on a discharge per unit catchment area basis, the unit area thus representing the unit rainfall excess. The proposed method does not have any constraint. The nonlinear optimization formulation is solved using binary-coded genetic algorithms. The number of variables to be estimated by optimization is the same as the number of probability distribution parameters; gamma and log-normal probability distributions are used. The existing nonlinear programming model for obtaining optimal unit hydrograph has also been solved using genetic algorithms, where the constrained nonlinear optimization problem is converted to an unconstrained problem using penalty parameter approach. The results obtained are compared with those obtained by the earlier LP model and are fairly similar.

Evaluation of Optimal River Training Work Using GA Based Linked Simulation-Optimization Approach

Use of structural measures for controlling a river to minimize its devastating effect and to utilize it for the benefit of mankind is a common practice all over the world. Because of high investment, such measures require prior investigation through model study. As lab based physical model study is very expensive and time consuming, mathematical modeling is generally used for investigating different alternatives of river training works. In this study, a new approach is proposed for deciding appropriate river training measure in a particular reach of a river or channel. In this methodology, an optimization model is linked with the hydrodynamic model for obtaining cost effective combination of groynes which will maintain a user defined flow speed in a pre-decided portion of a river reach. The optimization model is developed using binary coded Genetic Algorithm (GA) and the flow simulation model uses the Beam and Warming scheme for solving the two dimensional (2D) hydrodynamic equations of unsteady flow. The performance of the model is tested by applying the methodology in a rectangular channel for attaining different target speed values at a pre-defined portion of the channel and logical results have been obtained for all the tested scenarios.

Solution of source identification problem by using GMS and MATLAB

In this paper, a new methodology is proposed by linking the user-friendly commercial groundwater simulation software Groundwater Modeling System (GMS) with MATLAB-based optimization procedure for solving groundwater source identification problem. The simulation package MODFLOW and MT3DMS available in GMS is used to simulate the flow and transport processes in an aquifer. The input files for the MODFLOW and MT3DMS are generated using GMS software. A function is written to execute the MODFLOW and MT3DMS in MATLAB environment. This function is then used to obtain the objective function value for solving the source identification problem. The optimization model minimizes the difference between observed and simulated concentration for finding the locations of the sources and its concentration. A hypothetical problem has been solved to show the applicability and efficiency of the proposed methodology. The evaluation of the results shows that the proposed methodology could be a promising one in solving real-world sources identification problems.

Development of an Improved Pollution Source Identification Model Using Numerical and ANN Based Simulation-Optimization Model

The identification of unknown pollution sources is an important and challenging task for the engineers working on pollution management of a groundwater aquifer. The locations and transient magnitude of unknown contaminant sources can be identified using inverse optimization technique. In this approach, the absolute difference between the simulated and the observed contaminant concentration at the observation locations of the aquifer is minimized by using an optimization algorithm. The simulated concentrations is calculated using the aquifer simulation model. As such, there is a need to incorporate the aquifer simulation model with the optimization model. Thus the performance of the model is highly related to the aquifer simulation model. The incorporation of the sophisticated numerical simulation model will give better performance, but the model will be computationally expensive. On the other hand, the model will be computationally less expensive if an approximate simulation model is used in place of the numerical simulation model. However, in this case, the predictive performance of the model will decline. For achieving efficiency in both computational time as well as in predicting the performance, this study presents a new genetic algorithms based simulation-optimization method incorporating both the numerical and the approximate simulation models. The efficiency and field applicability of the model is demonstrated using illustrative study areas. The performance evaluation of the model shows that the proposed model has the potential for real-world field applications.

Development of Unknown Pollution Source Identification Models Using GMS ANN–Based Simulation Optimization Methodology

AbstractThe detection of groundwater pollution sources is an important but a very difficult task. The location and magnitude of groundwater pollution sources can be identified using inverse optimization technique. The technique is also known as simulation-optimization approach where the aquifer simulation model is incorporated with the optimization model for finding the unknown pollution sources in an aquifer. The efficiency of the simulation-optimization model is highly related to the performance of the simulation model. This study develops three improved methodologies for identification of unknown groundwater pollution sources. In the first approach, the groundwater modeling system (GMS) is linked with the optimization model for solving source identification problem. The optimization model is solved using the direct-search method. The incorporation of GMS with the optimization model allows for the solving of a bigger real-world pollution source identification problem. The challenge of this approach is t...

Geomorphology Based Semi-Distributed Approach for Modelling Rainfall-Runoff Process

This study presents a geomorphology based semi-distributed methodology for prediction of runoff of a catchment. In this proposed methodology, the catchment area is divided into a number of sub-catchments using the Thiessen polygon method. The rainfall records of particular rain-gauge station are considered as uniformly distributed over the entire sub-watershed. Four different weighting factors are proposed to obtain the sub-catchment’s contribution towards runoff. The weighting factors are calculated based on the geomorphological parameters of the catchment. The geomorphological parameters of the sub-watersheds are obtained from SRTM digital elevation data. The weighted contributions from all the sub-watersheds at current and previous time steps and the previous time step discharge are used to develop an Artificial Neural Network (ANN) for predicting the discharge at the basin outlet. A lump model considering average rainfall of the catchment is also developed using ANN for evaluating the performance of the proposed distributed model. For the lump model, average rainfall is calculated using Thiessen polygon method. The historic rainfall and runoff data recorded at the Dikrong basin, a sub-catchment of the river Brahmaputra is used to evaluate the efficiency of the developed methodology. The evaluation results show that the presented model is superior to the lump model and has the potential for field application. A comparative study is also carried out to obtain the most influential combination of geomorphological parameters in predicting the catchment’s runoff.

Hybrid-Optimization Approach for Estimating Parameters of a Virus Transport Process in Aquifer

AbstractSimulation of a virus transport process in a groundwater aquifer is necessary for predicting the movement of viruses in an aquifer. It is also necessary in implementing remedial measures to inactivate viruses present in groundwater. For simulating the virus transport process in a groundwater aquifer, the parameters responsible for movement of viruses in an aquifer have to be estimated. In the case of saturated porous media, the transport parameters are linear distribution coefficient, hydrodynamic dispersion coefficient, and inactivation coefficient for aqueous and sorbed viruses. These parameters can be estimated using an inverse optimization procedure that minimizes an error function represented by the difference between the experimentally observed and simulated virus concentration. Gradient-based classical optimization methods can be applied to minimize the error function for estimating the parameters of the transport process. It is reported that the error function is nonlinear and nonconvex in...

Stability Analysis of Earthen Embankment of Kollong River, Near Raha, Nagaon, Assam

Stability of an earthen slope can be analyzed using circular slip circle method. In this method, an optimal search is carried out to find the most critical slip circle corresponding to the minimum factor of safety. For obtaining the factor of safety of the critical slip circle, information about the subsurface strata is necessary. Most of the time, the slip circle is considered as homogeneous in nature. In this study, heterogeneity of the bank material is considered in the stability analysis of a river bank. A field survey is initially conducted using Ground Penetrating Radar (GPR) to acquire information about the nature of the subsurface soil strata of the embankment. Information extracted from the GPR survey was verified using actual bore log data. Stability analysis of the embankment is then carried out using circular slip circle method. The methodology is applied for the river bank analysis of the river Kollong at Raha, Assam. The evaluation shows that the methodology is effective in studying the stability analysis of river banks.

Simulation–Optimization-Based Virus Source Identification Model for 3D Unconfined Aquifer Considering Source Locations and Number as Variable

AbstractIdentification of virus sources is one of the most important activities to control the spread of epidemics. Virus sources can be identified using an inverse optimization model. The inverse optimization model minimizes the error between simulated and observed concentrations at observation locations of the aquifer. The observed concentration can be obtained from field observation of contaminants. The simulated concentration can be obtained through the flow and virus transport simulation models. As such, the flow and transport simulation models need to be incorporated into the optimization model. As a result, the complexity of the problem is related to the dimension of the simulation models. For reducing the computational burden, generally, one- or two-dimensional simulation model is considered in finding the virus sources. However, to mimic the real world situation, one has to use the three-dimensional (3D) virus transport processes. Furthermore, in earlier studies, the number and the source locatio...