Information entropy as a tool in surface water quality assessment

Water quality monitoring programs have become quintessential for developing a keen insight into water quality processes for decision makers to comprehend, interpret and utilize this information in developing conservation strategies for the water resource. However, a global challenge has emerged in handling the large sets of random data generated in these monitoring programs and utilizing them to derive useful information about the water quality of the resource. Recent studies have emphasized upon the effectiveness of probabilistic and stochastic approaches in interpretation of these random variables. The information content of a random variable can be quantified by information entropy which measures the uncertainty associated with the occurrence of the variable. In the present study, Shannon entropy has been utilized as a tool for water quality assessment as per its end use and has been illustrated by a case study on the Beki river, Assam (India). Entropy weights have been utilized for development of entropy weighted water quality index (EWQI) with respect to drinking purpose to optimize the design of water treatment system. A multi-criteria decision-making approach-Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), on the basis of entropy weights, has been employed for identifying ideal locations along the river stretch with water quality pertaining to irrigation standards and can be considered as a major criterion for constructing intake structures for irrigation canals. The proposed approach depicted the water quality of the Beki river ranging from “excellent” (EWQI < 50) to “poor” (EWQI between 150 and 200). Furthermore, TOPSIS analysis using the parameters EC, TDS, pH, HCO₃⁻, SAR, Cl⁻ and Na⁺ provided the relative suitability of water quality pertaining to irrigation standards. Risk assessment to human health with respect to heavy metals using Hazard Index (HI) depicted Chromium (Cr) and Lead (Pb) as the potential risk to human health of nearby communities. This comprehensive study illustrated the importance of Shannon entropy in water quality monitoring and management.