[1] Modeling of complex hydrologic processes has resulted in models that themselves exhibit a high degree of complexity and that require the determination of various parameters through calibration. In the current application we introduce a relatively new global optimization tool, called particle swarm optimization (PSO), that has already been applied in various other fields and has been reported to show effective and efficient performance. The PSO approach initially dealt with a single-objective function but has been extended to deal with multiobjectives in a form called multiobjective particle swarm optimization (MOPSO). The algorithm is modified to account for multiobjective problems by introducing the Pareto rank concept. The new MOPSO algorithm is tested on three case studies. Two test functions are used as the first case study to generate the true Pareto fronts. The approach is further tested for parameter estimation of a well-known conceptual rainfall-runoff model, the Sacramento soil moisture accounting model having 13 parameters, for which the results are very encouraging. We also tested the MOPSO algorithm to calibrate a three-parameter support vector machine model for soil moisture prediction.
[1] A time series simulation scheme based on wavelet decomposition coupled to an autoregressive model is presented for hydroclimatic series that exhibit band-limited lowfrequency variability. Many nonlinear dynamical systems generate time series that appear to have amplitude-and frequency-modulated oscillations that may correspond to the recurrence of different solution regimes. The use of wavelet decomposition followed by an autoregressive model of each leading component is explored as a model for such time series. The first example considered is the Lorenz-84 low-order model of extratropical circulation, which has been used to illustrate how chaos and intransitivity (multiple stable solutions) can lead to low-frequency variability. The central England temperature (CET) time series, the NINO3.4 series that is a surrogate for El Nino-Southern Oscillation, and seasonal rainfall from Everglades National Park, Florida, are then modeled with this approach. The proposed simulation model yields better results than a traditional linear autoregressive (AR) time series model in terms of reproducing the time-frequency properties of the observed rainfall, while preserving the statistics usually reproduced by the AR models.
Groundwater overexploitation has caused massive groundwater depletion and raised concerns for water and food security in India. Groundwater in India also suffers from multiple water quality issues such as arsenic and fluoride contamination that pose human health risks. Here we report new data showing that the occurrence in uranium in Indian groundwater is an emerging and widespread phenomenon. We present compiled data on groundwater uranium from 16 Indian states and new data from 324 wells in the states of Rajasthan and Gujarat that show a high prevalence of uranium concentrations above the World Health Organization provisional guideline value of 30 μg/L across India. Using geochemical and uranium isotope data, we suggest factors that may drive high uranium concentrations in groundwater, including uranium content in aquifer rocks, oxidation state, and groundwater chemistry that promotes the formation of soluble uranyl carbonate complexes. While the primary source of uranium is geogenic, anthropogenic factors such as groundwater table decline and nitrate pollution may further enhance uranium mobilization. These findings suggest the need for revision of the current water quality monitoring program in India, evaluation of human health risks in areas of high uranium prevalence, development of adequate remediation technologies, and, above all, implementation of preventive management practices to address this problem.
[1] Four algorithms are outlined, each of which has interesting features for predicting contaminant levels in groundwater. Artificial neural networks (ANN), support vector machines (SVM), locally weighted projection regression (LWPR), and relevance vector machines (RVM) are utilized as surrogates for a relatively complex and time-consuming mathematical model to simulate nitrate concentration in groundwater at specified receptors. Nitrates in the application reported in this paper are due to on-ground nitrogen loadings from fertilizers and manures. The practicability of the four learning machines in this work is demonstrated for an agriculture-dominated watershed where nitrate contamination of groundwater resources exceeds the maximum allowable contaminant level at many locations. Cross-validation and bootstrapping techniques are used for both training and performance evaluation. Prediction results of the four learning machines are rigorously assessed using different efficiency measures to ensure their generalization ability. Prediction results show the ability of learning machines to build accurate models with strong predictive capabilities and hence constitute a valuable means for saving effort in groundwater contamination modeling and improving model performance.
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