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Using Fuzzy Analytic Hierarchy Process for Southern Johor River Ranking

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Using Fuzzy Analytic Hierarchy Process for Southern Johor River Ranking Using Fuzzy Analytic Hierarchy Process for Southern Johor River Ranking Mohamad Ashari Alias Siti Zaiton Mohd Hashim Supiah Samsudin Faculty of Computer Science Faculty of Computer Science Faculty of Civil Engineering, and Information Systems, and Information Systems, University Teknologi Malaysia, University Teknologi Malaysia. University Teknologi Malaysia. Malaysia Malaysia Malaysia 607-5531581 607-5532458 607-5532458 [email protected] [email protected] [email protected] ABSTRACT rivers into Class I, II, III, IV or V based on Water Quality This paper presents the application of fuzzy set theory in Index (WQI) and Interim National Water Quality multi criteria decision making (MCDM). Fuzzy Analytic Standards for Malaysia (INWQS) respectively. However, Hierarchy Process (FAHP) technique will be used to rank these classification schemes did not consider other alternatives to find the most reasonable and efficient use aspects such as water quantity, land use and economy of river system. The overall aspects of river system which directly influence the final result in finding the including both qualitative and quantitative are most appropriate use of water system. Therefore, this emphasized. Four criteria and 20 sub-criteria have been study included water quantity, land use and economy identified and compared. Fuzzy numbers and linguistic aspect in the decision making. variables are presented to address inherently uncertain or imprecise data. The technique is tested to real rivers data. Previous study on river ranking ([1], [30]) had used point A comparison between the proposed technique and the value to represent the subjective data. This approach is previous results obtained using conventional techniques found to be adequate when the absolute point value can is presented. exactly represent the DMs preferences. However, this point value cannot represent the degree of preference of the DMs and also the degree of risk tolerance that the Categories and Subject Descriptors DMs are ready to take. Also, in real situation, the I.2.1. [Computing Methodologies] : Artificial absolute point value is not always adequate to represent Intelligence: Applications and Expert Systems the DMs preference naturally. Decision makers usually find it more convenient to express interval judgments General Terms than fixed value judgments due to the fuzzy nature of the Algorithms comparison process [2]. Therefore, this paper proposed fuzzy set defuzzification technique to address vague data Keywords using triangular fuzzy number (TFN) and to represent MCDM, Fuzzy AHP, fuzzy logic DMs degree of confidence and degree of risk that the DMs are ready to take. 1. INTRODUCTION The purpose of this study are threefold; 1. To construct Nowadays, water resource management should be structural hierarchy that considers various aspects of river managed in integrated manner including water quality, basins, 2. To rank rivers for Southern Johor to find the water quantity, land use and economy, especially when most appropriate used of water system and 3. To compare we wanted to find the highest potential river to be the result with the previous work using water quality developed for efficient use of water systems. Water Index (WQI) and Hipre 3+. resources planning and management should consider various aspects of river basins.. Current river ranking 2. LITERATURE REVIEW techniques namely, Water Quality Index (WQI) and There a numerous multi criteria decision making National Water Quality Standard (INWQS) were found to (MCDM) techniques had been developed to date. One of only consider water quality aspects. Other important the most common MCDM techniques is AHP ([3]-[8]). water related aspects were neglected. Furthermore, the The use of AHP will keep increasing because of the more the aspects are to be considered, the more difficult AHP’s advantages such as ease of use, great flexibility, to obtain exact preference value when multiple units of and wide applicability [3]. In this study, AHP will be data are used. The difficulties also faced by the decision used together with fuzzy set to solve river ranking maker (DM) when there exist qualitative data in the problem. decision making process. In this study, a special focus is given on the method that deals with vague qualitative Numerous authors have presented different ranking data which the DMs accounted during data acquisition. methods to rank alternatives under fuzzy environment during the last two decades [8]. Bottani and Rizzi [9] had Currently, in Malaysia, water quality data were used to used fuzzy logic to deal with vagueness of human thought determine the water quality status weather in clean, and AHP to make a selection the most suitable dyad slightly polluted or polluted category and to classify the 105 supplier/purchased item. Buyukozkan et al. [10] had used extensively dealing with such problems [32]. proposed fuzzy AHP method to evaluate e-logistics-based Problems such as ranking, evaluation and comparing strategic alliance partners. Efendigil et al. [2] proposed river to find the best management strategies is considered two-phase model based on artificial neural networks and as multiple criteria decision making. Ranking, comparing fuzzy AHP to select a third-party reverse logistics and evaluating existing river basin to find the most provider. Cascales and Lamata [11], proposed fuzzy AHP efficient and reasonable use of water system need a for management maintenance processes where only proper algorithm. linguistic information was available. Pan [12] used fuzzy AHP for selecting the suitable bridge construction The challenge is when the data consist of vague data method. Sheu [13], proposed a hybrid neuro-fuzzy besides exact data values. Fuzzy logic has been the most methodology to identify appropriate global logistics widely used AI technique used in MCDM ([8], [34], [35], operational modes used for global supply chain [36]). Fuzzy logic also had been widely used to represent management. Tsai et al. [14] used fuzzy AHP for market data that cannot be given by exact (crisp) value. positioning and developing strategy in order to improve Therefore, the present of variety of units involved in the service quality in department stores. Wu et al. [15], evaluation process has motivated this work in handling proposed fuzzy AHP for measurement non-profit vague data involving qualitative data. organizational performance. Huang et al. [16] had applied fuzzy AHP to represent subjective expert judgements in 4. FUZZY AHP APPROACH government-sponsored R&D project selection. Lee et al. The propose approach consists of 3 stages: Data [17] had constructed fuzzy AHP to evaluate performance gathering, Fuzzy-AHP calculation and Decision making. of IT department in the manufacturing industry in Steps taken in each stage are described as follows: Taiwan. Chang C W et al. [18, 19], used fuzzy AHP to evaluate and controlling silicon wafer slicing quality. Chang and Wang [20] had proposed consistent fuzzy Stage 1: Data Gathering. preference relation in a comparison matrix. Chen et al. Step 1: Determine objective and choosing alternatives. [21], proposed combination of fuzzy AHP with multi This is done through literature survey and discussion with dimensional scaling in identifying the preference knowledgeable experts. During this step, we do the similarity of alternatives. following: • Define the problem clearly with specifications Chen and Qu [22], had proposed fuzzy AHP to evaluate on its multi-criteria aspects. the selection of logistics centre location. Dagdeviren and • Determine the overall goal and sub-goals, Yuksel [23], developed fuzzy AHP for behavior-based identifying the evaluation criteria. safety management. Nagahanumaiah et al. [24], using fuzzy AHP to identify problem features for injection Step 2: Determines criteria to be used in the ranking mold development. Duran and Aguilo [25], used fuzzy process. AHP for machine-tool selection. Onut et al. [26], In this step, we identified the candidate alternatives. This proposed a combined fuzzy AHP and fuzzy TOPSIS is also done with the confirmation from the knowledge approach for machine tool selection problem. Yang et al. experts. 4 criteria namely water quality, water quantity, [27], proposed fuzzy AHP for Vendor selection by land use and economy have been identified. 20 sub- integrated fuzzy MCDM techniques with independence criteria namely biochemical oxygen demand (BOD), and interdependence. suspended solid (SS), pH, dissolve oxygen (DO), chemical oxygen demand (COD), ammonia nitrogen A significant finding from all the researchers is they used (AN), temperature, iron, flow rate, length of river, width triangular fuzzy number (TFN) to represent vague data or of river, residential, industry, agriculture, forest, fishery, linguistic variables. It is important to note that the extent industry, recreation and reservoir have been chosen. analysis method used by [23] and [26] was found cannot estimate the true weights from a fuzzy comparison matrix Step 3: Structuring decision hierarchy. [28]. In this step, the decision problem is structured into a hierarchical model, in which the overall goal (usually the selection of the best alternative) is situated at the highest 3. MOTIVATION level; elements with similar features (usually evaluation Water is the most vital resource for all living things on criteria) are grouped at the same interim level and earth. River is one of the main sources of fresh water on decision variables (usually alternatives) are situated at the earth besides lake
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