Determine of Surface Water Quality Index in Iran

In modeling complex of environmental problems, researchers often fail to define precise statements about input and outcomes of contaminants, but fuzzy logic could help to dominate this logical indecision. The goal of this work is to propose a new river water quality indicator using fuzzy logic. The proposed index combines six indicators, and not only does it exhibit a tool that accounts for the discrepancy between the two base indices, but also provides a quantifiable score for the determined water quality. These classifications with a membership grade can be of a sound support for decision-making, and can help assign each section of a river a gradual quality sub-objective to be reached. To show the applicability of the proposed approach, the new indicator was used to classify water quality in a number of stations along the basins of Qarah-chai and Siminehrood. The obtained classifications were then compared to the conventional physicochemical water quality indicator currently in use in Iran. The results revealed that the fuzzy indicator provided stringent classifications compared to the conventional index in 38% and 44% of the cases for the two basins respectively. These noted exceptions are mainly due to the big disagreement between the different quality thresholds in the two standards, especially for fecal coliform and total phosphorus. These large disparities put forward an argument for the Iranian water quality law to be upgraded. Keywords: Fuzzy logic; Qarah-chai basin; Siminehrood; Water quality inde

Rough Set Applied to Air Pollution: A New Approach to Manage Pollutions in High Risk Rate Industrial Areas

This study presents a rough set application, using together the ideas of classical rough set approach, based on the indiscernibility relation and the dominance-based rough set approach (DRSA), to air micro-pollution management in an industrial site with a high environmental risk rate, such as the industrial area of Syracuse, located in the South of Italy (Sicily). This new data analysis tool has been applied to different decision problems in various fields with considerable success, since it is able to deal both with quantitative and with qualitative data and the results are expressed in terms of decision rules understandable by the decision-maker. In this chapter, some issue related to multi-attribute sorting (i.e. preference-ordered classification) of air pollution risk is presented, considering some meteorological variables, both qualitative and quantitative as attributes, and criteria describing the different objects (pollution occurrences) to be classified, that is, different levels of sulfur oxides (SOx), nitrogen oxides (NOx), and methane (CH4) as pollution indicators. The most significant results obtained from this particular application are presented and discussed: examples of ‘if, … then’ decision rules, attribute relevance as output of the data analysis also in terms of exchangeable or indispensable attributes/criteria, of qualitative substitution effect and interaction between them

MODELLING THE URBAN SUSTAINABLE DEVELOPMENT BY USING FUZZY SETS

The sustainable urban development is a subject of interest for regional policy makers and it needs appropriate assessment based on futile instruments for research, and for practical reasonsl (planning and decision making). Even if the sustainability’s attainment is a research topic field for academia and urban planners and managers and, as well, an ambitious goal for any resource administrator, yet there is no precise way of defining and measuring it. The sustainability of the urban development policy implies multiple and diversified aspects from rational exploitation of the local resources and well-structured workforce to environmental issues, endowment of modern urban facilities and infrastructure elements. As the urban sustainability is measured using a multitude of basic indicators, needing proper information to make long term management decision and planning, the subject is treated with fuzzy setsseen as an appropriate manner to deal with ambiguity, subjectivity and imprecision in the human reasoning when processing large volumes of data, eventually unstructured and complex. The paper proposed a modeling approach based on fuzzy sets inspired by the SAFE (Sustainability Assessment by Fuzzy Evaluation), a model which provides a mechanism for measuring development sustainability. The papers intends presenting a quantitative methodology in assessing the potential sustainability of urban development (in terms of adequacy) by pointing the failures in pursuing trends that are associated to a robust growth in the urban areas. The advantages of such approach are derived from taking into account the multi-criteria and uncertainty facets of the phenomenon; also, having in mind that the sustainability remains a non-straight-cut concept, being vaguely defined it implies a non-deterministic character by using the fuzzy set logic. The proposed model is designed to assess the divergence from desired trajectories, the weak point in reaching indicators’ target (as they are commonly regardedd as appropriate in what is understood as a good practices), it may then be addressed for policy makers in indicating some action measures in urban administration as they intendenly strive towards increasingly sustainable development on the long term.sustainability, urban management, indicators, fuzzy approach.

Estimation of Performance Indices for the Planning of Sustainable Transportation Systems

In the context of sustainable transportation systems, previous studies have either focused only on the transportation systemor have not used a methodology that enables the treatment of incomplete, vague, and qualitative information associated with the available data. This study proposes a system of systems (SOS) and a fuzzy logic modeling approach. The SOS includes the Transportation, Activity, and Environment systems. The fuzzy logic modeling approach enables the treatment of the vagueness associated with some of the relevant data. Performance Indices (PIs) are computed for each system using a number of performance measures. The PIs illustrate the aggregated performance of each system as well as the interactions among them. The proposed methodology also enables the estimation of a Composite Sustainability Index to summarize the aggregated performance of the overall SOS. Existing data was used to analyze sustainability in the entire United States. The results showed that the Transportation and Activity systems follow a positive trend, with similar periods of growth and contractions; in contrast, the environmental system follows a reverse pattern. The results are intuitive and are associated with a series of historic events, such as depressions in the economy as well as policy changes and regulations

Estimation of Performance Indices for the Planning of Sustainable Transportation Systems

In the context of sustainable transportation systems, previous studies have either focused only on the transportation system or have not used a methodology that enables the treatment of incomplete, vague, and qualitative information associated with the available data. This study proposes a system of systems (SOS) and a fuzzy logic modeling approach. The SOS includes the Transportation, Activity, and Environment systems. The fuzzy logic modeling approach enables the treatment of the vagueness associated with some of the relevant data. Performance Indices (PIs) are computed for each system using a number of performance measures. The PIs illustrate the aggregated performance of each system as well as the interactions among them. The proposed methodology also enables the estimation of a Composite Sustainability Index to summarize the aggregated performance of the overall SOS. Existing data was used to analyze sustainability in the entire United States. The results showed that the Transportation and Activity systems follow a positive trend, with similar periods of growth and contractions; in contrast, the environmental system follows a reverse pattern. The results are intuitive and are associated with a series of historic events, such as depressions in the economy as well as policy changes and regulations

An interval fuzzy model for magnetic monitoring: estimation of a pollution index

In this contribution, a methodology is reported in order to build an interval fuzzy model for the pollution index PLI (a composite index using relevant heavy metal concentration) with magnetic parameters as input variables. In general, modelling based on fuzzy set theory is designed to mimic how the human brain tends to classify imprecise information or data. The ??interval fuzzy model?? reported here, based on fuzzy logic and arithmetic of fuzzy numbers, calculates an ??estimation interval?? and seems to be an adequate mathematical tool for this nonlinear problem. For this model, fuzzy c-means clustering is used to partition data, hence the membership functions and rules are built. In addition, interval arithmetic is used to obtain the fuzzy intervals. The studied sets are different examples of pollution by different anthropogenic sources, in two different study areas: (a) soil samples collected in Antarctica and (b) road-deposited sediments collected in Argentina. The datasets comprise magnetic and chemical variables, and for both cases, relevant variables were selected: magnetic concentration-dependent variables, magnetic features-dependent variables and one chemical variable. The model output gives an estimation interval; its width depends on the data density, for the measured values. The results show not only satisfactory agreement between the estimation interval and data, but also provide valued information from the rules analysis that allows understanding the magnetic behaviour of the studied variables under different conditions.Fil: Chaparro, Mauro Alejandro Eduardo. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto Multidisciplinario de Ecosistemas y Desarrollo Sustentable; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; ArgentinaFil: Chaparro, Marcos Adrián Eduardo. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Física Arroyo Seco; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; ArgentinaFil: Sinito, Ana Maria. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Física Arroyo Seco; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentin