Abstract:
Watershed areas in underdeveloped countries are strategic resources for agri-
culture and domestic purposes. These sheds are not adequately protected
from contamination, caused by anthropogenic activities and spillways' dis-
charge. The contaminated water is adversely a ecting the ecosystem. So the
quality and storage parameters are of serious concern for all water resources
management authorities. Conventional methods cannot cope with the root
cause analysis of water reservoir contamination and discharge. For cost ef-
fective and predictive water management, it is essential to analyze di erent
aspects of water quality and storage 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 pur-
poses of twin city Islamabad and Rawalpindi of Pakistan. Di erent regression
models based on monthly and quarterly datasets, to check the seasonal water
quality trends were developed. In order to determine how much parameters
satisfy the WHO quality standards, the parametric satisfactory analysis was
carried out. For quality indexing, Hierarchical Clustering and Multilayer
Perceptron has been found more accurate technique. Higher values of fecal
coliforms were found in the months of March, June, July, and October. The
predictions of hydrological parameters to manage water discharge were made
for the year 2013 using regression and time series forecast models. The results
show that August is a crucial month for in
ow and spillways discharge; while
September and October are critical for level and storage capacity. J48 tree
classi cation technique has been found more accurate supervised machine
learning technique for discharge management. Similarly, in order to forecast
the water level, the improved SVM technique has been found to be more ac-
curate. Finally, using regression and forecast models, it has been found that
in year 2013, water level as well as the storage capacity will remain below the
spillways gates opening threshold values.
