Water pollution control in river basin by interactive fuzzy interval multiobjective programming
Abstract
The potential conflict between protection of water quality and economic development by different uses of land within river basins is a common problem in regional planning. Many studies have applied multiobjective decision analysis under uncertainty to problems of this kind. This paper presents the interactive fuzzy interval multiobjective mixed integer programming (IFIMOMIP) model to evaluate optimal strategies of wastewater treatment levels within a river system by considering the uncertainties in decision analysis. The interactive fuzzy interval multiobjective mixed integer programming approach is illustrated in a case study for the evaluation of optimal wastewater treatment strategies for water pollution control in a river basin. In particular, it demonstrates how different types of uncertainty in a water pollution control system can be quantified and combined through the use of interval numbers and membership functions. The results indicate that such an approach is useful for handling system complexity and generating more flexible policies for water quality management in river basins.
- Authors:
-
- National Cheng-Kung Univ., Tainan (Taiwan, Province of China). Dept. of Environmental Engineering
- Academia Sinica, Taipei (Taiwan, Province of China). Inst. of Economics
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 566260
- Resource Type:
- Journal Article
- Journal Name:
- Journal of Environmental Engineering
- Additional Journal Information:
- Journal Volume: 123; Journal Issue: 12; Other Information: PBD: Dec 1997
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; WATER POLLUTION CONTROL; WATER QUALITY; RIVERS; MATHEMATICAL MODELS; I CODES
Citation Formats
Chang, N B, Chen, H W, Shaw, D G, and Yang, C H. Water pollution control in river basin by interactive fuzzy interval multiobjective programming. United States: N. p., 1997.
Web. doi:10.1061/(ASCE)0733-9372(1997)123:12(1208).
Chang, N B, Chen, H W, Shaw, D G, & Yang, C H. Water pollution control in river basin by interactive fuzzy interval multiobjective programming. United States. https://doi.org/10.1061/(ASCE)0733-9372(1997)123:12(1208)
Chang, N B, Chen, H W, Shaw, D G, and Yang, C H. 1997.
"Water pollution control in river basin by interactive fuzzy interval multiobjective programming". United States. https://doi.org/10.1061/(ASCE)0733-9372(1997)123:12(1208).
@article{osti_566260,
title = {Water pollution control in river basin by interactive fuzzy interval multiobjective programming},
author = {Chang, N B and Chen, H W and Shaw, D G and Yang, C H},
abstractNote = {The potential conflict between protection of water quality and economic development by different uses of land within river basins is a common problem in regional planning. Many studies have applied multiobjective decision analysis under uncertainty to problems of this kind. This paper presents the interactive fuzzy interval multiobjective mixed integer programming (IFIMOMIP) model to evaluate optimal strategies of wastewater treatment levels within a river system by considering the uncertainties in decision analysis. The interactive fuzzy interval multiobjective mixed integer programming approach is illustrated in a case study for the evaluation of optimal wastewater treatment strategies for water pollution control in a river basin. In particular, it demonstrates how different types of uncertainty in a water pollution control system can be quantified and combined through the use of interval numbers and membership functions. The results indicate that such an approach is useful for handling system complexity and generating more flexible policies for water quality management in river basins.},
doi = {10.1061/(ASCE)0733-9372(1997)123:12(1208)},
url = {https://www.osti.gov/biblio/566260},
journal = {Journal of Environmental Engineering},
number = 12,
volume = 123,
place = {United States},
year = {Mon Dec 01 00:00:00 EST 1997},
month = {Mon Dec 01 00:00:00 EST 1997}
}