Abstract-In this paper, by combining a two-person nonzero-sum game, a multi-objective genetic algorithm and a cooperative game, we present a new game theoretic methodology for trading pollution discharge permits in rivers. A trade-off curve between the average treatment level of dischargers and fuzzy risk of low water quality is gained using the optimization model. Then, by using the two-person nonzero-sum game, the best non-dominated solution is chosen from the trade-off curve. The treatment costs of dischargers corresponding to the selected solution are reallocated among dischargers participating in a coalition and side payments are calculated. The proposed model is applied to the Zarjub River in Iran to illustrate its efficiency and applicability.
The US Drought Monitor (USDM) is a hallmark in real time drought monitoring and assessment as it was developed by multiple agencies to provide an accurate and timely assessment of drought conditions in the US on a weekly basis. The map is built based on multiple physical indicators as well as reported observations from local contributors before human analysts combine the information and produce the drought map using their best judgement. Since human subjectivity is included in the production of the USDM maps, it is not an entirely clear quantitative procedure for other entities to reproduce the maps. In this study, we developed a framework to automatically generate the maps through a machine learning approach by predicting the drought categories across the domain of study. A persistence model served as the baseline model for comparison in the framework. Three machine learning algorithms, logistic regression, random forests, and support vector machines, with four different groups of input data, which formed an overall of 12 different configurations, were used for the prediction of drought categories. Finally, all the configurations were evaluated against the baseline model to select the best performing option. The results showed that our proposed framework could reproduce the drought maps to a near-perfect level with the support vector machines algorithm and the group 4 data. The rest of the findings of this study can be highlighted as: 1) employing the past week drought data as a predictor in the models played an important role in achieving high prediction scores, 2) the nonlinear models, random forest, and support vector machines had a better overall performance compared to the logistic regression models, and 3) with borrowing the neighboring grid cells information, we could compensate the lack of training data in the grid cells with insufficient historical USDM data particularly for extreme and exceptional drought conditions.
The United States (U.S.) environmental regulatory system relies heavily on self-reports to assess compliance among regulated facilities. However, the regulatory agencies have expressed concerns regarding the potential for fraud in self-reports and suggested that the likelihood of detection in the federal and state enforcement processes is low. In this paper, we apply Benford’s Law to three years of self-reported discharge parameters from wastewater treatment plant facilities in one U.S. state. We conclude that Benford’s Law alone may not be a reliable method for detecting potential data mishandling for individual facility–parameter combinations, but may provide information about the types of parameters most likely to be fraudulently reported and types of facilities most likely to do so. From a regulatory perspective, this information may help to prioritise potential fraud risks in self reporting and better direct limited resources.
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