Forecasting the Urban Expansion Effects on the Design Storm Hydrograph and Sediment Yield Using Artificial Neural Networks

Abstract

Urban expansion substantially alters the impervious areas in a catchment, which in turn affects surface runoff and sediment yield in the downstream areas. In this study, the Land Transformation Model (LTM) was used to forecast the urban land expansion in a catchment, whilst future land use maps were employed according to the Soil Conservation Service Curve Number method (SCS-CN) and the Modified Universal Soil Loss Equation (MUSLE) model, so as to examine the urbanization effects on runoff and sediment yield production respectively. Compared to pristine conditions, urban land is anticipated to increase from 6% in 1979 to 31% by 2027. The latter expansion pointed to an increase of peak discharge by 2.2-2.6 times and of flood volume by 1.6-2.1 times, with the sediment yield ranging between 0.47 to 1.05 t/ha for the upcoming 2027 period. Furthermore, the urban sprawl effects on all the latter variables were more profound during short duration storm events. Forecasting urban expansion through integrated artificial neural networks (ANN) and geographic information system (GIS) techniques, in order to calculate the associated design storm hydrograph and sediment yield, is of great importance, in order to properly plan and design hydraulic works that can sustain future urban development.have been negatively impacted by the degree of urbanization [5] and the hydrologic cycle and in stream process have been altered [6]. These changes could seriously impact the mountain rivers sediment production and transport [7] with significant changes in the bed morphology [8] and floods with a high content of transported sediments and debris floods can occur [9]. Moreover, the temporal and spatial scales of these phenomena may vary greatly during intense precipitation events with respect to normal conditions [10].In recent decades, a water budget or hydrologic model have been commonly utilized [11,12] to evaluate the effects of the recorded urban expansion on the hydrology of catchments, so as to model the effects of different land use change scenarios on runoff and sediment yield [13,14]. However, since land use planners and policy makers are more and more interested in predicting urban expansion and its effect on natural resources, much of the research in recent years has focused on the coupling of hydrologic models with sophisticated spatial land use change (LUC) models, that are able to project LUC changes in the near future. LUC models can be used as tools for studying the human effect on the Earth's surface and to forecast land change into the future [15].LUC models use a variety of techniques, including linear extrapolation, suitability mapping, genetic algorithms, neural networks, scenario analysis, expert opinions, public participation and agent-based modelling, in order to produce a prediction map for the near future [16]. However, there are three main stream approaches for LUC models: models which rely primarily on economic models and data [17][18][19], models that rely equally on multiple approaches and data to determine ca...

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