alexa Water Quality Index Modelling For An Administrative Block In New Delhi Using M-FIS Technique
ISSN: 2157-7587

Hydrology: Current Research
Open Access

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3rd International Conference on Hydrology & Meteorology
September 15-16, 2014 Hyderabad International Convention Centre, India

Rama Mehta and C K Jain
Accepted Abstracts: Hydrol Current Res
DOI: 10.4172/2157-7587.S1.015
Abstract
The problem of ground water quality is more acute in densely populated and industrial areas and on areas where ground water is withdrawn from shallow aquifers in Delhi. Soft computing technique as Mamdani-Fuzzy Inference System (M-FIS) is being used to study the ground water quality for Alipur block. Data for eight different physico-chemical water quality parameters (pH, TDS, Chloride (Cl), Sulphate (SO4), Nitrate (NO3), Calcium (Ca), Fluoride (F) and Magnesium (Mg)) have been used with 39 samples. Mamdani Fuzzy Water Quality Index (WQI), Guidelines given by Canadian Council of Ministry of Environment for Water Quality Index (CCME_WQIG) and Empirical method are applied in order to assess the degree of drinking water resources. The problem of water quality classification can be approached using combination of Degree of Match and the Fuzzy Rule-based System. In M-FIS, input data are categorised into three linguistic terms (?Desirable?, ?Acceptable?, & ?Not-Acceptable?) based on water quality standards for drinking water, whereas the output data are categorized into six classes (excellent, very good, good, fair, marginal, & poor) based on WQI. Total three models have been developed using three methods/ techniques. Results calculated by Empirical method, is being used as an alternative to the expert knowledge in M-FIS technique. Comparative results have been shown by different graphs with all three methods. Through Performance indices table, CCME _WQI is better than FWQI but still the use of FWQI is recommended for future use because once the model is developed, its FIS file can be used to calculate WQI just be giving the observed values of the input parameters without considering the environmental changes of study area.
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