Groundwater Management Optimization and Saltwater Intrusion Mitigation under Uncertainty

Groundwater is valuable to supply fresh water to the public, industries, agriculture, etc. However, excessive pumping has caused groundwater storage degradation, water quality deterioration and saltwater intrusion problems. Reliable groundwater flow and solute transport modeling is needed for sustainable groundwater management and aquifer remediation design. However, challenges exist because of highly complex subsurface environments, computationally intensive groundwater models as well as inevitable uncertainties. The first research goal is to explore conjunctive use of feasible hydraulic control approaches for groundwater management and aquifer remediation. Water budget analysis is conducted to understand how groundwater withdrawals affect water levels. A mixed integer multi-objective optimization model is constructed to derive optimal freshwater pumping strategies and investigate how to promote the optimality through regulating pumping locations. A solute transport model for the Baton Rouge multi-aquifer system is developed to assess saltwater encroachment under current condition. Potential saltwater scavenging approach is proposed to mitigate the salinization issue in the Baton Rouge area. The second research goal aims to develop robust surrogate-assisted simulation-optimization modeling methods for saltwater intrusion mitigation. Machine learning based surrogate models (response surface regression model, artificial neural network and support vector machine) were developed to replace a complex high-fidelity solute transport model for predicting saltwater intrusion. Two different methods including Bayesian model averaging and Bayesian set pair analysis are used to construct ensemble surrogates and quantify model prediction uncertainties. Besides. different optimization models that incorporate multiple ensemble surrogates are formulated to obtain optimal saltwater scavenging strategies. Chance-constrained programming is used to account for model selection uncertainty in probabilistic nonlinear concentration constraints. The results show that conjunctive use of hydraulic control approaches would be effective to mitigate saltwater intrusion but needs decades. Machine learning based ensemble surrogates can build accurate models with high computing efficiency, and hence save great efforts in groundwater remediation design. Including model selection uncertainty through multimodel inference and model averaging provides more reliable remediation strategies compared with the single-surrogate assisted approach

Conjunctive Management of Water Resources under Climate Change Projection Uncertainty

Goal of this study is to investigate the impacts of climate change projection uncertainty on conjunctive use of water resources. To pursue this goal first, a conjunctive-use model is developed for management of groundwater and surface water resources via mixed integer linear fractional programming (MILFP). The conjunctive management model maximizes the ratio of groundwater usage to reservoir water usage. A conditional head constraint is imposed to maintain groundwater sustainability. A transformation approach is introduced to transform the conditional head constraint into a set of mixed integer linear constraints in terms of groundwater head. A supply network is proposed to apply the conjunctive-use model to northern Louisiana and southern Arkansas. Then, simple model averaging (SMA), reliability ensemble averaging (REA), and hierarchical Bayesian model averaging (HBMA) are utilized as ensemble averaging methods to provide a thorough understanding of the impacts of climate change on future runoff for the study area. An ensemble of 78 hydroclimate models is formed by forcing HELP3 with climate data from combinations of 13 GCMs, 2 RCPs, and 3 downscaling methods. Runoff projections obtained from SMA, REA, and HBMA are compared. The Analysis of Variance (ANOVA) is used to quantify the sources of uncertainty of SMA projection and compare to the estimations made by HBMA. Both methods show similar contribution of uncertainty indicating that GCMs are the dominant source of uncertainty. At last, the proposed conjunctive use model is applied to optimize the conjunctive use of future surface water and groundwater resources under climate change projection. Future inflows to the reservoirs are estimated from the future runoffs projected through hydroclimate modeling, where the Variable Infiltration Capacity (VIC) model and 11 GCM RCP8.5 downscaled climate outputs are considered. Bayesian model averaging (BMA) is adopted to quantify uncertainty in future runoff projections and reservoir inflow projections due to uncertain future climate projections. The results from the developed conjunctive management model indicate that the future reservoir water even with low inflow projections at 2.5% cumulative probability would be able to counterbalance groundwater pumping reduction to satisfy demands while improving the Sparta aquifer through conditional groundwater head constraint

GENESIM : genetic extraction of a single, interpretable model

Models obtained by decision tree induction techniques excel in being interpretable.However, they can be prone to overfitting, which results in a low predictive performance. Ensemble techniques are able to achieve a higher accuracy. However, this comes at a cost of losing interpretability of the resulting model. This makes ensemble techniques impractical in applications where decision support, instead of decision making, is crucial. To bridge this gap, we present the GENESIM algorithm that transforms an ensemble of decision trees to a single decision tree with an enhanced predictive performance by using a genetic algorithm. We compared GENESIM to prevalent decision tree induction and ensemble techniques using twelve publicly available data sets. The results show that GENESIM achieves a better predictive performance on most of these data sets than decision tree induction techniques and a predictive performance in the same order of magnitude as the ensemble techniques. Moreover, the resulting model of GENESIM has a very low complexity, making it very interpretable, in contrast to ensemble techniques.Comment: Presented at NIPS 2016 Workshop on Interpretable Machine Learning in Complex System

One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure