New approach to site selection for WSPs with rough set theory
Publish Year: 1393
نوع سند: مقاله کنفرانسی
زبان: English
View: 970
This Paper With 5 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
ECONF02_320
تاریخ نمایه سازی: 12 دی 1393
Abstract:
Today, discharging the wastewaters into the river is the main factor of water pollution and associated with environmental impacts. There are several methods for wastewater treatment. The natural ways like wastewater stabilization pond (WSP) is apply in a lot of countries especially in under developed countries because no needed to high technology. Furthermore the WSPs are suitable for wastewater treatment in small societies and some special industries like slaughterhouse, dairy products and meat products and etc. the advantages of this way is high efficiency in treatment of pathogens, toxic and organic materials. Ecological condition and wastewater properties affect the efficiency of WSP so site location investigations should be performed to select the appropriate site for WSPs. Rough set theory is a mathematical approach for analysis of a vague description of objects presented by a well-known mathematician, Pawlak (1982, 1991). This paper explores the use of rough set theory to in site location investigating for waste water stabilization ponds. Making an appropriate decision in site location can always avoid unnecessary expensive costs which is very important in such a case. The proposed site location investigation approach is illustrated using a case study data of a WSP in the south of Shahr rey City within Tehran province. In this approach the decision rules are derived from conditional attributes in rough set analysis, in accounting for data vagueness and uncertainty and in potentially reducing data collection needs. The results of this study indicate that using this method can reduce unnecessary costs in wastewater stabilization ponds
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :