Bithin Datta

Bio: Bithin Datta is an academic researcher from James Cook University. The author has contributed to research in topics: Aquifer & Saltwater intrusion. The author has an hindex of 37, co-authored 158 publications receiving 3932 citations. Previous affiliations of Bithin Datta include Cooperative Research Centre & Indian Institutes of Technology.

Optimal Monitoring Network and Ground-Water–Pollution Source Identification

Abstract: A methodology combining an optimal ground-water–quality monitoring network design and an optimal source-identification model is presented. In the first step of the three-step methodology, an embedded nonlinear optimization model is utilized for preliminary identification of pollutant sources (magnitude, location, and duration of activity) based on observed concentration data from arbitrarily located existing wells. The second step utilizes these preliminary identification results and a simulation optimization approach to design an optimal monitoring network that can be implemented in the subsequent time periods. In the third step, the observed concentration data at the designed monitoring well locations are utilized for more accurate identification of the pollutant sources. The design of the monitoring network can be dynamic in nature, with sequential installation of monitoring wells during subsequent time periods. The monitoring network can be implemented in stages, in order to utilize the updated information in the form of observed concentration data from a time-varying (dynamic) network. The performance evaluation of the proposed methodology demonstrates the potential applicability of this methodology and shows significant improvement in the identification of unknown ground-water–pollution sources with limited observation data.

Optimal Identification of Ground-Water Pollution Sources and Parameter Estimation

Abstract: An optimization-based methodology for identifying unknown sources of ground-water pollution is presented. The proposed methodology utilizes an optimization model in which the flow and transport equations are embedded as constraints. A nonlinear programming algorithm is used to obtain as solution the optimal estimates of unknown source characteristics. The input to this model includes measured pollutant concentration at observation sites. The source identification methodology is further extended to the simultaneous estimation of aquifer parameters as well as identification of unknown pollutant sources. Performance of the developed methodology is evaluated for illustrative examples considering two-dimensional flow and advective-dispersive solute transport. Different cases including variability in data availability, single and multiple potential source locations, and errors in measurement data are considered. These performance evaluations demonstrate that at least for the illustrative example problems, the p...

Identification of groundwater pollution sources using GA-based linked simulation optimization model

Abstract: The genetic algorithm (GA)–based simulation optimization approach is used for optimal identification of unknown groundwater pollution sources Simple as well as complex scenarios of multiple unknown groundwater pollution sources are considered A flow and transport simulation model is externally linked to the GA-based optimization model to simulate the physical processes involved The simulation model uses potential pollution source characteristics that are evolved by the GA and simulates the resulting concentration measurement values at observation locations These simulated spatial and temporal pollutant concentration measurement values are used to evaluate the fitness function value of the GA The main advantage of the proposed methodology is the external linking of the numerical simulation model with the optimization model This approach makes it feasible to solve the source-identification problems for complex aquifer study areas with multiple unknown pollution sources The performance of the developed methodology is evaluated for combinations of source characteristics (locations, magnitudes, and release periods), data availability conditions, and concentration measurement error levels

Identification of Unknown Groundwater Pollution Sources Using Artificial Neural Networks

Abstract: The temporal and spatial characterization of unknown groundwater pollution sources remains an important problem in effective aquifer remediation and assessment of associated health risks. The characterization of contaminated source involves identifying spatially and temporally varying source locations, injection rates, and release periods. The proposed methodology exploits the universal function approximation capability of a feed forward multilayer artificial neural network (ANN) to identify the unknown pollution sources. The ANN is trained to identify source characteristics based on simulated contaminant concentration measurement data at specified observation locations in the aquifer. These concentrations are simulated for a large set of randomly generated pollution source fluxes. The back-propagation algorithm is used for training the ANN, with each corresponding set of source fluxes and resulting concentration measurement constituting a pattern for training the ANN. Performance of this methodology is evaluated for various data availability, measurement error, and source location scenarios. The developed ANNs are capable of identifying unknown groundwater pollution sources at multiple locations using erroneous measurement data.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Reservoir Management and Operations Models: A State‐of‐the‐Art Review

Abstract: The objective of this paper is to review the state-of-the-art of mathematical models developed for reservoir operations, including simulation. Algorithms and methods surveyed include linear programming (LP), dynamic programming (DP), nonliner programming (NLP), and simulation. A general overview is first presented. The historical development of each key model is critically reviewed. Conclusions and recommendations for future research are presented.

Review of surrogate modeling in water resources

Abstract: [1] Surrogate modeling, also called metamodeling, has evolved and been extensively used over the past decades. A wide variety of methods and tools have been introduced for surrogate modeling aiming to develop and utilize computationally more efficient surrogates of high-fidelity models mostly in optimization frameworks. This paper reviews, analyzes, and categorizes research efforts on surrogate modeling and applications with an emphasis on the research accomplished in the water resources field. The review analyzes 48 references on surrogate modeling arising from water resources and also screens out more than 100 references from the broader research community. Two broad families of surrogates namely response surface surrogates, which are statistical or empirical data-driven models emulating the high-fidelity model responses, and lower-fidelity physically based surrogates, which are simplified models of the original system, are detailed in this paper. Taxonomies on surrogate modeling frameworks, practical details, advances, challenges, and limitations are outlined. Important observations and some guidance for surrogate modeling decisions are provided along with a list of important future research directions that would benefit the common sampling and search (optimization) analyses found in water resources.

State of the Art for Genetic Algorithms and Beyond in Water Resources Planning and Management

Abstract: During the last two decades, the water resources planning and management profession has seen a dramatic increase in the development and application of various types of evolutionary algorithms (EAs). This observation is especially true for application of genetic algorithms, arguably the most popular of the several types of EAs. Generally speaking, EAs repeatedly prove to be flexible and powerful tools in solving an array of complex water resources problems. This paper provides a comprehensive review of state-of-the-art methods and their applications in the field of water resources planning and management. A primary goal in this ASCE Task Committee effort is to identify in an organized fashion some of the seminal contributions of EAs in the areas of water distribution systems, urban drainage and sewer systems, water supply and wastewater treatment, hydrologic and fluvial modeling, groundwater systems, and parameter identification. The paper also identifies major challenges and opportunities for the future, ...