Neural Networks
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Support vector machines for detection of electrocardiographic changes in partial epileptic patients
Engineering Applications of Artificial Intelligence
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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This paper presents a new methodology for estimating location and amount of leakage from an unknown pollution source using groundwater quality monitoring data. The proposed methodology includes a multi-objective optimization model, namely Non-dominated Sorting Genetic Algorithm-II (NSGA-II) which is linked with MODFLOW and MT3D groundwater quantity and quality simulation models. The main characteristics of an unknown groundwater pollution source are estimated using two probabilistic simulation models, namely Probabilistic Support Vector Machines (PSVMs) and Probabilistic Neural Networks (PNNs). In real-time groundwater monitoring, these trained probabilistic simulation models can present the probability mass function of an unknown pollution source location and the relative error in estimating the amount of leakage based on the observed concentrations of water quality indicator at the monitoring wells. The efficiency of the proposed methodology is demonstrated through a real-world case study.