Next Article in Journal
Ecohydrological Changes and Resilience of a Shallow Lake Ecosystem under Intense Human Pressure and Recent Climate Change
Previous Article in Journal
Three-Dimensional Turbulence Numerical Simulation of Flow in a Stepped Dropshaft
Previous Article in Special Issue
Conceptual Framework for Sediment Management in Torrents
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Water 2019, 11(1), 31; https://doi.org/10.3390/w11010031

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

1
School of Forestry and Natural Environment, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
2
Forest Research Institute, Hellenic Agricultural Organization ‘Demeter’, Vasilika, 57006 Thessaloniki, Greece
*
Author to whom correspondence should be addressed.
Received: 25 October 2018 / Revised: 17 December 2018 / Accepted: 18 December 2018 / Published: 24 December 2018
(This article belongs to the Special Issue Erosion and Torrent Control)
  |  
PDF [4981 KB, uploaded 25 December 2018]
  |  

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. View Full-Text
Keywords: LTM; urban expansion; ANN; SCS-CN; MUSLE LTM; urban expansion; ANN; SCS-CN; MUSLE
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Myronidis, D.; Ioannou, K. Forecasting the Urban Expansion Effects on the Design Storm Hydrograph and Sediment Yield Using Artificial Neural Networks. Water 2019, 11, 31.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Water EISSN 2073-4441 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top