Physico-Chemical Assessment of Drinking Water Available to the Inhabitants of Low Income and Thickly Populated Areas of Karachi City

The aim of this study was to investigate the physico-chemical properties of drinking water available to the population of low income areas of Karachi city. The study incorporated the attention towards the fluoride content in water being used for domestic and drinking purpose by the inhabitants of low income and thickly populated areas of Karachi. Samples were collected from selected locations from all the districts of Karachi city. Laboratory tests were performed to analyze both physical and chemical characteristics of drinking water. It was observed in this study that except few of the locations, fluoride content was present either in low concentration or in high concentration. Medical data of the areas under study was collected through questionnaires and survey forms. The consequence of the variation of fluoride concentration was found to be in agreement with the findings of medical data analyzed from concerned areas where both cases of Fluorosis and dental cavities were reported. Correlation of fluoride with other parameters was analyzed using principle component analysis determined PC1 & PC2 as most significant components. PC1 showed dominance of TDS with salts while PC2 indicated loadings were temperature DO & pH. Monitoring of fluoride ion concentration and other health related parameters are essential for the development of efficient water management system. Fluoride content in drinking water should be regulated by periodic assessment and elevated levels can be controlled by adsorption or membrane techniques. Keywords: Physico-chemical properties, drinking water, districts of Karachi, fluoride variation, correlation analysis, principle component analysis, water management system. DOI: 10.7176/JNSR/11-14-01 Publication date:July 31st 202

Neural Network Approaches for Computation of Soil Thermal Conductivity

The effective thermal conductivity (ETC) of soil is an essential parameter for the design and unhindered operation of underground energy transportation and storage systems. Various experimental, empirical, semi-empirical, mathematical, and numerical methods have been tried in the past, but lack either accuracy or are computationally cumbersome. The recent developments in computer science provided a new computational approach, the neural networks, which are easy to implement, faster, versatile, and reasonably accurate. In this study, we present three classes of neural networks based on different network constructions, learning and computational strategies to predict the ETC of the soil. A total of 384 data points are collected from literature, and the three networks, Artificial neural network (ANN), group method of data handling (GMDH) and gene expression programming (GEP), are constructed and trained. The best accuracy of each network is measured with the coefficient of determination (R2) and found to be 91.6, 83.2 and 80.5 for ANN, GMDH and GEP, respectively. Furthermore, two sands with 80% and 99% quartz content are measured, and the best performing network from each class of ANN, GMDH and GEP is independently validated. The GEP model provided the best estimate for 99% quartz sand and GMDH with 80%