Assessment of Water Quality using Fuzzy-AHP and TOPSIS

• Main Authors: Sonavane Asmita, Narkhede Devyani, Pawar Shruti, Maktum Tabassum
• Format: Article
• Language: English
• Published: EDP Sciences 2021-01-01
• Series: ITM Web of Conferences
• Subjects: water quality; ahp; fuzzy-ahp; standard error calculation; gwqi; mcdm; topsis; bwm topsis
• Online Access: https://www.itm-conferences.org/articles/itmconf/pdf/2021/05/itmconf_icacc2021_02002.pdf

The quality of water available to people has deteriorated because of the vast increase in global industries. The overall quality of water has reduced due to the high use of fertilizers in farms and chemicals in sectors such as mining and construction. The quality of water has a direct impact on the health of humans and hence it is necessary to evaluate the quality of drinking water. The quality of water is dependent on various substances like pH, ammonia, iron, arsenic etc. present in water. If these parameters are available in an appropriate amount in water then only the water is considered as of good quality. The problem of assessing water quality is usually considered as multi-criteria decision problem. In this paper an approach to evaluate quality of water using Fuzzy Analytical Hierarchical Process (Fuzzy-AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is proposed. The parameters which are considered for evaluation include Arsenic, Ammonia , Iron , Nitrate , Dissolved Oxygen and Ph. The relative importance of these parameters is utilized while applying the Fuzzy-AHP process and weights for each parameter are computed. The various datasets containing values for considered parameters are collected and the weights are used to assign quality levels to these water samples. The paper also gives the performance analysis of the proposed method in terms of accuracy. The accuracy is measured in terms of how many water samples are assigned correct quality level and the results show that the proposed method has better accuracy as compared to traditional AHP and Fuzzy-AHP method.