Title

Assessment of Artificial Neural Networks and IHACRES models for simulating streamflow in Marillana catchment in the Pilbara, Western Australia

Document Type

Journal Article

Publisher

Taylor & Francis

School

School of Engineering

RAS ID

21086

Comments

Originally published as: Ahooghalandari, M., Khiadani, M., & Kothapalli, G. (2016). Assessment of Artificial Neural Networks and IHACRES models for simulating streamflow in Marillana catchment in the Pilbara, Western Australia. Australian Journal of Water Resources, 19(2), pp. 1-11. Original article available here.

Abstract

A wide range of techniques are available to model hydrological processes. Choosing a model is not a straightforward task considering geographical and climate conditions. Although this is an exercise with some level of uncertainty it does help to understand hydrological processes which are used for the management and development of water resources. In Western Australia with its arid or semi-arid climate, water resource management is a key issue for future sustainable development. In this study, IHACRES, a physical-based hydrology model, and Artificial Neural Networks were used to simulate the daily water discharge from Marillana Creek catchment in the Pilbara in Western Australia. Although, these models did not produce satisfactory results, the comparison of the results show that the ANN model can be a feasible alternative for complex hydrology systems with poor recorded data. Also, data from two neighbouring gauges were successfully used to improve the result of Artificial Neural Networks over the IHACRES model.

DOI

10.1080/13241583.2015.1116183

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