R. Bhutiani

A recursive approach for numerically identified DO–BOD interaction and kinetic formulation for water quality probability

In the late 1960s, the mounting public pressure in the developing countries for control of pollution stimulated investment in a host of special studies to find the best alternative ways of protecting the aquatic environment. One of the most important parameters for knowing the water quality is Dissolved Oxygen (DO) because various other parameters are interrelated with it and changes accordingly with DO. Therefore, in the present study, an attempt has been made to summarise the dissolved oxygen-biochemical oxygen demand interaction models to manage the quality of natural water bodies that are subject to pollutant inputs and one must be able to predict the degradation in quality that results from those inputs.

Development of an Automated Water Quality Classification Model for the River Ganga

Recently, Water Quality (WQ) comes out to be the central point of concern all around the globe. The purpose of this work is to develop an automated procedure that can be used to classify the water quality of the River Ganga proficiently in the stretch from Devprayag to Roorkee Uttarakhand, India. The monthly data sets of five water quality parameters temperature, pH, dissolved oxygen (DO), biochemical oxygen demand (BOD) and total coliform (TC) for the time period from 2001 to 2015 is used for this research work. The proposed method involves developing various water quality classification models using one of the concept of data mining called decision tree (DT) for evaluating the WQ classes. The experiments are conducted using Weka data mining tool. Models first developed using (60–40)% data division approach and then using (80–20)% approach of data division. Five different decision tree models are developed named J48 (C4.5), Random Forest, Random Tree, LMT (logistic model tree) and Hoeffding Tree. These classifiers were analyzed to determine the most accurate classifier model for the present dataset by evaluating their performance via measures like Accuracy, Kappa Statistics, Recall, Precision, F-Measure, Mean absolute error and Root mean squared error. The results concluded that the random forest model outperforms all other classifiers with a great accuracy rate of 100% in both approaches and least error rate when developed using the second approach. Such a highly acceptable and attractive results may be helpful for the decision makers in water management and planning.