Study on Orthogonal Basis NN-Based Storage Modelling for Lake Hume of Upper Murray River, Australia

Abstract

The Murray-Darling Basin is Australia’s most iconic and the largest catchment. It is also one of the largest river systems in the world and one of the driest. For managing the sustainable use of the Basin’s water, hydrological modelling plays important role. The main models in use are the mathematical represented models which are difficult of containing full relationship between rainfall runoff, flow routing, upstream storage, evaporation and other water losses. Hume Reservoir is the main supply storage and one of the two major headwater storages for the River Murray system. It is crucial in managing flows and securing water supplies along the entire River Murray System, including Adelaide. In this paper, two Orthogonal Basis NN-Based storage models for Hume Reservoir are developed by using flow data from upstream gauge stations. One is only considering flow data from upstream gauge stations. Another is considering both upstream flow data and rainfall. The Neural Network (NN) learning algorithm is based on Ying Li’s previous research outcome. The modelling results proved that the approach has high accuracy, good adaptability and extensive applicability.