Watershed Contamination Management through Prediction and Quantitative Analysis using Emerging Techniques of Data Engineering
Maqbool Ali, ALi Mustafa Qamar, Bilal Ali
Journal of Information & Systems Management, Vol. 3, No. 4, 2013.
DOI: 
Link: http://www.dline.info/jism/fulltext/v3n4/2.pdf
Abstract:
Watershed areas in underdeveloped countries are strategic resources for agriculture and domestic purposes. These sheds are not adequately protected from contamination, caused by anthropogenic activities. The contaminated water is adversely affecting the ecosystem. So the quality parameter is of serious concern for all water resources management authorities. Conventional methods cannot cope with the root cause analysis of water reservoir contamination. For cost effective and predictive water management, it is essential to analyze different aspects of water quality with emerging modeling, mining and learning techniques. The quality indices are analyzed by the combination of supervised and un-supervised machine learning techniques. As a case-study we selected the Rawal watershed area used for irrigation and domestic purposes of twin city Islamabad and Rawalpindi of Pakistan. Different regression models based on monthly and quarterly datasets, to check the seasonal water quality trends were developed. In order to determine how much parameter satisfies the WHO quality standards, the parametric satisfactory analysis was carried out. For quality indexing, Hierarchical Clustering and Multilayer Perceptron have been found more accurate techniques. Higher values of fecal coliforms were found in the months of March, June, July, and October.