Meghna Babbar-Sebens

Interactive Genetic Algorithm with Mixed Initiative Interaction for multi-criteria ground water monitoring design

Design of optimal plans for environmental planning and management applications should ideally consider the multiple quantitative and qualitative criteria relevant to the problem. For example, in ground water monitoring design problems, qualitative criteria such as acceptable spatial extent and shape of the contaminant plume predicted from the monitored locations can be equally important as the typical quantitative criteria such as economic costs and contaminant prediction accuracy. Incorporation of qualitative criteria in the problem-solving process is typically done in one of two ways: (a) quantifying approximate representations of the qualitative criteria, which are then used as additional criteria during the optimization process, or (b) post-optimization analysis of designs by experts to evaluate the overall performance of the optimized designs with respect to the qualitative criteria. These approaches, however, may not adequately represent all of the relevant qualitative information that affect a human expert involved in design (e.g. engineers, stakeholders, regulators, etc.), and do not necessarily incorporate the effect of the experts own learning process on the suitability of the final design. The Interactive Genetic Algorithm with Mixed Initiative Interaction (IGAMII) is a novel approach that addresses these limitations by using a collaborative human-computer search strategy to assist users in designing optimized solutions to their applications, while also learning about their problem. The algorithm adaptively learns from the experts feedback, and explores multiple designs that meet her/his criteria using both the human expert and a simulated model of the experts responses in a collaborative fashion. The algorithm provides an introspection-based learning framework for the human expert and uses the humans subjective confidence measures to adjust the optimization search process to the transient learning process of the user. This paper presents the design and testing of this computational framework, and the benefits of using this approach for solving groundwater monitoring design problems.

On the use of multivariate statistical methods for combining in-stream monitoring data and spatial analysis to characterize water quality conditions in the White River Basin, Indiana, USA

Mechanistic hydrologic and water quality models provide useful alternatives for estimating water quality in unmonitored streams. However, developing these elaborate models for large watersheds can be time-consuming and expensive, in addition to challenges that arise during calibration when there is limited spatial and/or temporal monitored in-stream water quality data. The main objective of this research was to investigate different approaches for developing multivariate analysis models as alternative methods for rapidly assessing relationships between spatio-temporal physical attributes of the watershed and water quality conditions in monitored streams, and then using the developed relationships for estimating water quality conditions in unmonitored streams. The study compares the use of various statistical estimates (mean, geometric mean, trimmed mean, and median) of monitored water quality variables to represent annual and seasonal water quality conditions. The relationship between these estimates and the spatial data is then modeled via linear and non-linear multivariate methods. Overall, the non-linear techniques for classification outperformed the linear techniques with an average cross-validation accuracy of 79.7%. Additionally, the geometric mean based models outperformed models based on other statistical indicators with an average cross-validation accuracy of 80.2%. Dividing the data into annual and quarterly datasets also offered important insights into the behavior of certain water quality variables impacted by seasonal variations. The research provides useful guidance on the use and interpretation of the various statistical estimates and statistical models for multivariate water quality analyses.

A web-based software tool for participatory optimization of conservation practices in watersheds

WRESTORE (Watershed Restoration Using Spatio-Temporal Optimization of Resources) is a web-based, participatory planning tool that can be used to engage with watershed stakeholder communities, and involve them in using science-based, human-guided, interactive simulation-optimization methods for designing potential conservation practices on their landscape. The underlying optimization algorithms, process simulation models, and interfaces allow users to not only spatially optimize the locations and types of new conservation practices based on quantifiable goals estimated by the dynamic simulation models, but also to include their personal subjective and/or unquantifiable criteria in the location and design of these practices. In this paper, we describe the software, interfaces, and architecture of WRESTORE, provide scenarios for implementing the WRESTORE tool in a watershed communitys planning process, and discuss considerations for future developments. A participatory design tool that uses interactive and human-guided approaches to simulation-optimization.Tool can be used to engage stakeholders in assist with planning of conservation practices.Users can be engaged to view and evaluate designs based on quantifiable and un-quantifiable criteria.The software is web-based and can be used for engagement with individual users or multiple users.

Reinforcement learning for human-machine collaborative optimization: Application in ground water monitoring

In this paper, we introduce reinforcement learning as a methodology to solve complex multi-criteria optimization problems for ground water monitoring. Multiple analytical criteria are used to assess design decisions and human feedback is simulated by adding random noise. Different learning automata based reinforcement learning methods as well as a genetic algorithm based method are used in experimental studies, which demonstrate the efficiency of reinforcement learning approaches.

Decentralized and partially decentralized reinforcement learning for designing a distributed wetland system in watersheds

In this paper, we use identical-payoff games of reinforcement learning agents as a framework to solve complex multi-criteria optimization problem of watershed management. Multiple analytical criteria are used to assess design decisions for creating a distributed network of wetlands in the watershed. Decentralized game algorithms of reinforcement learning agents as well as a genetic algorithm based method are used for the analysis. Simulation studies are presented which compare the efficiency of the reinforcement learning approaches with a multi-objective genetic algorithm-based approach.

Decentralized pursuit learning automata in batch mode

Learning Automata (LA) and Genetic Algorithms (GA) have been used for a long time to solve problems in different domains. However, there is criticism that LA has slow rate of convergence and both LA and GA have the problem of getting stuck in local optima. In this paper we tried to solve the multi-objective problems using LA in batch mode to make the learning faster and more accurate. We used Decentralized pursuit learning automaton as LA and NSGA2 as GA. Problems where evaluation of fitness function is a bottleneck like SWAT, evaluation of individuals in parallel can give considerable speed-up. In the multi-objective LA, different weight pairs and individual designs can be evaluated independently. So we created their parallel versions to make them practically faster in learning and computations and extended the parallelization concept with the batch mode learning.

User Modelling for Interactive Optimization Using Neural Network

User modelling is one of the prominent research fields in information retrieval systems. In this paper, we model users preferences and search criteria using an NN (Neural Network) to solve a multiobjective optimization problem specific to environmental planning systems. We argue that some NP hard problems cannot be solved alone either by a human or by a computer. Human participation in automated search is one way of combining human intuition with algorithmic search to solve such problems. However, even humans have some limitations for participation in that they cannot participate in search completely because of human fatigue. To overcome this, in our approach, an NN tries to model the users rating criteria and preferences to help the user in rating large set of designs. Although training an NN with limited data is not always feasible, there are many situations where a simple modelling technique (e.g., linear/quadratic mapping) works better if the learning data set is small. In this paper we attempt to get more accuracy of the NN by generating data using other linear/non-linear techniques that fills the gap created by lack of sufficient training data. Also, we provided the architectural design of an HPC based framework we have proposed and compared the performance of the NN with fuzzy logic and other linear/non-linear user modelling techniques for the environmental resources optimization problem.

Interactive genetic algorithm for user-centered design of distributed conservation practices in a watershed: An examination of user preferences in objective space and user behavior

Interactive Genetic Algorithms (IGA) are advanced human-in-the-loop optimization methods that enable humans to give feedback, based on their subjective and unquantified preferences and knowledge, during the algorithms search process. While these methods are gaining popularity in multiple fields, there is a critical lack of data and analyses on (a) the nature of interactions of different humans with interfaces of decision support systems (DSS) that employ IGA in water resources planning problems and on (b) the effect of human feedback on the algorithms ability to search for design alternatives desirable to end-users. In this paper, we present results and analyses of observational experiments in which different human participants (surrogates and stakeholders) interacted with an IGA-based, watershed DSS called WRESTORE to identify plans of conservation practices in a watershed. The main goal of this paper is to evaluate how the IGA adapts its search process in the objective space to a users feedback, and identify whether any similarities exist in the objective space of plans found by different participants. Some participants focused on the entire watershed, while others focused only on specific local subbasins. Additionally, two different hydrology models were used to identify any potential differences in interactive search outcomes that could arise from differences in the numerical values of benefits displayed to participants. Results indicate that stakeholders, in comparison to their surrogates, were more likely to use multiple features of the DSS interface to collect information before giving feedback, and dissimilarities existed among participants in the objective space of design alternatives.

Fuzzy and deep learning approaches for user modeling in wetland design

Determining the optimal design of a watershed is a highly subjective process which involves the consideration of many distinct factors by several different stakeholder groups. We describe additional functionality for our watershed planning system, called WRESTORE (Watershed REstoration Using Spatio-Temporal Optimization of REsources) (http://wrestore.iupui.edu), where stakeholders can collaboratively optimize best management practices on to the watershed. WRESTORE utilizes the USDAs public domain Soil and Water Assessment Tool hydrologic model for watershed simulations. Reinforcement learning and interactive genetic algorithms are applied for the search process. The new functionality described is a user modeling component that develops a computational model of a users decision-making, based on real-time user-provided ratings for a subset of possible designs. The user modeling task utilizes neural network approaches, such as deep learning. We believe the originality of our approach centers on integrating user models in to the hydrological decision support process. This paper thus has three objectives: (i) outline current work in user modeling and watershed design, (ii) describe our system for interactive optimization of watershed design, and (iii) describe our work on implementing accurate and stable user predictive models to boost optimization performance.