ABSTRACT
Normally, decision makers use number of pipe broken in specific sections of water distribution systems to determine the pipes to be replaced. This index cannot solely evaluate hydraulic and quality parameters of the system and effects of pipe renovation on the system performance. In this study, a methodology is presented to manage the rehabilitation and replacement of water distribution network using hydraulic and geospatial information systems models. A preprocessor subroutine is developed to link geospatial information systems and hydraulic software. Hydraulic parameters together with the attribute data of pipes are used to determine the required renovation schemes based on several criteria. The proposed indices consist of pipe breaks and leakage analyses, hydraulic and quality performance and mechanical reliability of the network. A novel approach is also introduced to calculate leakage values throughout the network. Results of a real case study by the developed model introduced replacement of 4 km 40 mm galvanized pipes instead of 11 km 100 mm asbestos-cement pipes from conventional method. It is observed that the suggested geographic information system based model produces more realistic results with less cost for renovation schemes in comparison with conventional method which just consider number of bursts as a key criterion
Subject(s)
Rehabilitation , Water , Water MovementsABSTRACT
Urban Expansion Model [UEM] was adapted to simulate urbanization which implements Geospatial Information Systems [GIS], Artificial Neural Networks [ANNs] and Remote Sensing [RS]. Two satellite imageries with specific time interval, socio-economic and environmental variables have been employed in order to simulate urban expansion. Socio-economic and environmental variables were used as inputs while construction and non-construction areas were used as outputs to train the neural network. Calibration of proposed model was performed with area under the ROC Curve [AROC] and Kappa Statistic [KS] which are non-shape performance metric. A real-life case study of Tehran Metropolitan Area [TMA] is presented to demonstrate the process. This paper presents a version of the UEM which parameterized for TMA and explores how factors such as road, building area, service centre, green space, elevation, aspect and slope can influence urbanization. Having urban expansion model with specific time interval and assuming the existence of the same rate of urbanization, new construction areas of region can be predicted. The overall accuracy of the model to predict new construction areas was 80% and 78% with AROC and KS, respectively