Rainfall-runoff modelling using hydrological connectivity index and artificial neural network approach

Haniyeh Asadi; Kaka Shahedi; Ben Jarihani; Roy C Sidle

doi:10.3390/w11020212

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Rainfall-runoff modelling using hydrological connectivity index and artificial neural network approach

Journal article

Open access

Peer reviewed

Rainfall-runoff modelling using hydrological connectivity index and artificial neural network approach

Haniyeh Asadi, Kaka Shahedi, Ben Jarihani and Roy C Sidle

Water, Vol.11(2), 212

2019

DOI: https://doi.org/10.3390/w11020212

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Abstract

artificial neural network

index of Connectivity

input selection

rainfall-runoff modelling

The input selection process for data-driven rainfall-runoff models is critical because input vectors determine the structure of the model and, hence, can influence model results. Here, hydro-geomorphic and biophysical time series inputs, including Normalized Difference Vegetation Index (NDVI) and Index of Connectivity (IC; a type of hydrological connectivity index), in addition to climatic and hydrologic inputs were assessed. Selected inputs were used to develop Artificial Neural Networks (ANNs) in the Haughton River catchment and the Calliope River catchment, Queensland, Australia. Results show that incorporating IC as a hydro-geomorphic parameter and remote sensing NDVI as a biophysical parameter, together with rainfall and runoff as hydro-climatic parameters, can improve ANN model performance compared to ANN models using only hydro-climatic parameters. Comparisons amongst different input patterns showed that IC inputs can contribute to further improvement in model performance, than NDVI inputs. Overall, ANN model simulations showed that using IC along with hydro-climatic inputs noticeably improved model performance in both catchments, especially in the Calliope catchment. This improvement is indicated by a slight increase (9.77% and 11.25%) in the Nash-Sutcliffe efficiency and noticeable decrease (24.43% and 37.89%) in the root mean squared error of monthly runoff from Haughton River and Calliope River, respectively. Here, we demonstrate the significant effect of hydro-geomorphic and biophysical time series inputs for estimating monthly runoff using ANN data-driven models, which are valuable for water resources planning and management.

Details

Title: Rainfall-runoff modelling using hydrological connectivity index and artificial neural network approach
Authors: Haniyeh Asadi (Author) - Sari Agricultural Sciences and Natural Resources University, Iran
Kaka Shahedi (Author) - Sari Agricultural Sciences and Natural Resources University, Iran
Ben Jarihani (Author) - University of the Sunshine Coast
Roy C Sidle (Author) - University of the Sunshine Coast
Publication details: Water, Vol.11(2), 212
Publisher: MDPI AG
Date published: 2019
DOI: 10.3390/w11020212
ISSN: 2073-4441
Copyright note: Copyright © 2019 The Authors. 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).
Organisation Unit: University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Sustainability Research Cluster
Language: English
Record Identifier: 99450766402621
Output Type: Journal article

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Collaboration types: Domestic collaboration; International collaboration
Web Of Science research areas: Environmental Sciences; Water Resources

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