Exploring the application of artificial neural network in rural streamflow prediction - A feasibility study
- Authors: Choudhury, Tanveer , Wei, Jackie , Barton, Andrew , Kandra, Harpreet , Aziz, Abdul
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 27th IEEE International Symposium on Industrial Electronics, ISIE 2018; Cairns, Australia; 13th-15th June 2018 Vol. 2018-June, p. 753-758
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- Description: Streams and rivers play a critical role in the hydrologic cycle with their management being essential to maintaining a balance across social, economic and environmental outcomes. Accurate streamflow predictions can provide benefits in many different ways such as water allocation decision making, flood forecasting and environmental watering regimes. This is particularly important in regional areas of Australia where rivers can play a critical role in irrigated agriculture, recreation and social wellbeing, major floods and sustainable environments. There are several hydrological parameters that effect stream flows in rivers and a major challenge with any prediction methodology, is to understand these parameter interdependencies, correlations and their individual effects. A robust methodology is, thus, required for accurate prediction of streamflow under usually unique, waterway-specific conditions using available data. This research employs an approach based on Artificial Neural Network (ANN) to provide this robust methodology. Data from readily available sources has been selected to provide appropriate input and output parameters to train, validate and optimise the neural network. The optimisation steps of the methodology are discussed and the predicted outputs are compared and analysed with respect to the actual collected values. © 2018 IEEE.
- Description: IEEE International Symposium on Industrial Electronics
Prediction of clogging in stormwater filters using artificial neural network
- Authors: Lin, Junlin , Kandra, Harpreet , Choudhury, Tanveer , Barton, Andrew
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 27th IEEE International Symposium on Industrial Electronics, ISIE 2018; Cairns, Australia; 13th-15th June 2018 Vol. 2018-June, p. 771-776
- Full Text: false
- Reviewed:
- Description: Stormwater filtration technologies play a significant role in improving water quality and making treated water available for non-potable uses. However, during treatment processes, contaminants such as suspended solids would lead to clogging in storm water filters, especially those with high infiltration rates. There are several parameters that affect clogging of filters, and a major challenge is to understand the parameter interdependencies, correlations and their individual effects. A robust methodology is, thus, required to accurately predict clogging which would contribute to the development of filtration technologies and in predictive maintenance. This research employs the use of Artificial Neural Network (ANN) model to predict clogging performance of stormwater filters under different operational conditions using experimental data from previous work. A single hidden layer ANN model with 19 hidden layer neurons was developed in this preliminary work.
- Description: IEEE International Symposium on Industrial Electronics