Bayesian approach to estimating margin of safety for total maximum daily load development

Patil, Abhijit; Deng, Zhiqiang

doi:10.1016/j.jenvman.2010.10.049

Cited by 25 publications

(14 citation statements)

References 19 publications

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“…In decision-making space { } 12 ,,..., n Dddd = , x d represents decision scheme, D is the state space. Among those, 1 σ , 2 σ and 3 σ separately reflect that the situation of the market is poor, general and preferable [7], [8]. Therefore, 123 (),(),() PPP σσσ demonstrates the probability of 1 σ , 2 σ and 3 σ .…”

Section: Bayesian Risk Investment Evaluation Modelmentioning

confidence: 98%

Investment Risk Evaluation for Demand Side Management: Improved Bayesian Model

Yin¹,

Zhang²,

Ma³

et al. 2015

Proceedings of the 4th International Conference on Information Technology and Management Innovation

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KEYWORD: DSM; investment risk; Bayesian risk investment evaluation model; Entropy Weight method; Normalization method. ABSTRACT: The vigorous promotion of DSM is of great significance in improving energy conservation and power system reliability. At present, DSM technology of China has been developed, meanwhile, related projects will be carried out extensively. Therefore, investment risk evaluation for DSM projects of power enterprises is of significant urgency. To begin with we will provide investment risk evaluation system for DSM projects based on characteristics of power utilization in China. Secondly, the improved Bayesian risk investment evaluation model will be established. Compared with traditional Bayesian model, Entropy Weight method will be used to calculate the weight of every index. At the same time, Normalization method has been introduced to normalize the value of indices. Last but not least, analysis of examples is preceded to prove the practicality of the model in this paper and provide a reference for the DSM projects in power enterprises. Evaluation systemAccording to the characteristics of power utilization in China, this paper has separated the benefits and costs of DSM projects into four categories: social benefits, power users, power grid enterprises and power generation enterprises. Based on the four categories, the investment risk evaluation system for DSM projects has been established as shown in Table1.

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Section: Bayesian Risk Investment Evaluation Modelmentioning

confidence: 98%

Investment Risk Evaluation for Demand Side Management: Improved Bayesian Model

Yin¹,

Zhang²,

Ma³

et al. 2015

Proceedings of the 4th International Conference on Information Technology and Management Innovation

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show abstract

“…In order to fully reflect the effective convergence of watershed management with natural water resources catchment norm, water pollution catchment characteristics and administrative areas management, this control unit division is divided by following four regulations according to the basin full coverage principle, administrative integrity, water integrity, water-based land-dividing principles [5][6][7][8]16].…”

Section: Zoning Regulationsmentioning

confidence: 99%

“…In the watershed management domain, the control unit is defined as a spatial management unit integrating administrative area, water body and control section. The control section is a water quality monitoring section on the downstream of the water body to control and reflect the pollution of upstream water body, and the control unit is the main pollutant control area to ensure water quality standards of the control area [5][6][7][8]. The main purpose of management units zoning is to decompose the complex water environment problems into various governance units [2,5], so that the specific measures and policies can be implemented effectively to achieve environmental water quality improvement [9].…”

Section: Introductionmentioning

confidence: 99%

Zoning and Analysis of Control Units for Water Pollution Control in the Yangtze River Basin, China

et al. 2017

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Abstract:In order to meet the needs of the control-unit-based water pollution prevention problem in China, we proposed a comprehensive control unit zoning method with the combined basic administrative region and objective watershed management. The method can effectively connect the natural characteristics of watershed water sources, three-level zoning of water resources, and comprehensively consider the pollution distribution, socio-economics and many other factors. The zoning process includes four steps: (1) Generate the multi-level hydrological unit; (2) Identify the multi-element water catchment units; (3) Obtain the control unit with the administrative boundary as the boundary; (4) Check and adjust the results to meet the actual needs. Based on this method, the Yangtze River Basin was divided into 568 control units with a total area of 1.91 million km 2 . These control units were used as the basic unit to analyze the water quality status and the results show that the upper reaches have good water quality, while the lower reaches have poor water quality and more serious water pollution. Our study helps the Ministry of Environmental Protection of China develop the list of control units that are needed to improve water quality during the "13th Five-Year Plan", with a goal to provide technical support for control-unit-based water pollution prevention and control in the future.

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“…They need to establish priority rankings for waters on the lists and to develop Total Maximum Daily Loads (TMDLs). Various models have been used not only to develop TMDLs, but also to perform analyses to identify strategies to attain pollutant load limits with plans typically identifying best management practices (BMPs) to reduce loads [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

A Web-Based Model to Estimate the Impact of Best Management Practices

Park

Engel

Harbor

2014

Water

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Abstract:The Spreadsheet Tool for the Estimation of Pollutant Load (STEPL) can be used for Total Maximum Daily Load (TMDL) processes, since the model is capable of simulating the impacts of various best management practices (BMPs) and low impact development (LID) practices. The model computes average annual direct runoff using the Soil Conservation Service Curve Number (SCS-CN) method with average rainfall per event, which is not a typical use of the SCS-CN method. Five SCS-CN-based approaches to compute average annual direct runoff were investigated to explore estimated differences in average annual direct runoff computations using daily precipitation data collected from the National Climate Data Center and generated by the CLIGEN model for twelve stations in Indiana. Compared to the average annual direct runoff computed for the typical use of the SCS-CN method, the approaches to estimate average annual direct runoff within EPA STEPL showed large differences. A web-based model (STEPL WEB) was developed with a corrected approach to estimate average annual direct runoff. Moreover, the model was integrated with the Web-based Load Duration Curve Tool, which identifies the least cost BMPs for each land use and optimizes BMP selection to identify the most cost-effective BMP implementations. The integrated tools provide an easy to use approach for performing TMDL analysis and identifying cost-effective approaches for controlling nonpoint source pollution. OPEN ACCESSWater 2014, 6 456

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Bayesian approach to estimating margin of safety for total maximum daily load development

Cited by 25 publications

References 19 publications

Investment Risk Evaluation for Demand Side Management: Improved Bayesian Model

Investment Risk Evaluation for Demand Side Management: Improved Bayesian Model

Zoning and Analysis of Control Units for Water Pollution Control in the Yangtze River Basin, China

A Web-Based Model to Estimate the Impact of Best Management Practices

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