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SPN7, University of Sheffield 29/8/13 PREDICTING CSO CHAMBER DEPTH USING ARTIFICIAL NEURAL NETWORKS WITH RAINFALL RADAR DATA Dr. Steve Mounce Mr. Gavin.

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Presentation on theme: "SPN7, University of Sheffield 29/8/13 PREDICTING CSO CHAMBER DEPTH USING ARTIFICIAL NEURAL NETWORKS WITH RAINFALL RADAR DATA Dr. Steve Mounce Mr. Gavin."— Presentation transcript:

1 SPN7, University of Sheffield 29/8/13 PREDICTING CSO CHAMBER DEPTH USING ARTIFICIAL NEURAL NETWORKS WITH RAINFALL RADAR DATA Dr. Steve Mounce Mr. Gavin Sailor Dr. Will Shepherd Dr. James Shucksmith and Prof. Adrian Saul Pennine Water Group, University of Sheffield, UK

2 Presentation structure 1. Introduction and aims 2. Case study data 3. Methodology 4. Results 5. Conclusions and Further Work

3 Introduction CSOs are common assets in the UKs combined urban drainage system Designed to discharge excess water during heavier rainfall events directly to a receiving watercourse Potential for unconsented spill events and pollution at CSO Possible causes include downstream blockage This work investigates a data driven method for performance assessment to tackle this problem

4 Background and objectives Increasing amounts of hydraulic field data from wastewater networks are being collected via monitors and telemetry systems alongside higher quality weather data Standard deterministic models require understanding of the hydrological and hydraulic processes to predict performance of the sewer network Previous work (Kurth et al. 2008, Guo and Saul 2011) has explored using Artificial Neural Networks with CSO depth and rain gauge data to predict future depth This work incorporates rainfall radar data for a case study

5 Case study CSO is terminal flow control to a treatment works at the bottom of a steep combined urban drainage catchment (~20 km² area) Water level data within the CSO was recorded using an ultrasonic depth monitor (with 100% signifying the spill level) and rainfall intensity data (mm/hr) from 20 rainfall radar pixels, with a resolution of 1 km² (15 min resolution for six month period) QiQi QsQs QcQc 100 %

6 426 427 428 429 430 399401400398397396395 1 23 4 5 6 7 9 8 10 11 13 14 12 15 16 17 18 20 19 CSO Case study Schematic with rainfall radar squares: river / canal overlay (blue), urban blocks (grey) and tree areas (green).

7 Example of relationship Time delay due to storm runoff arriving at CSO chambe r

8 Correlation Used to investigate the lags between different rainfall radar squares and the CSO depth to select model inputs Serial correlation is a measure of the similarity of a variable with a lagged version of itself – used for depth The correlation values decrease gradually with increasing lag time

9 Correlation Cross-correlation is a measure of the similarity of two variables (signals) as a function of a time lag between them – used on CSO depth and rainfall data Maximum indicates the point in time where the signals are best aligned: either lag -4 or -5 The larger maximum correlation squares were 1, 3, 6 and 7 Delay of -5 was observed in the far western grid squares (4, 5 and 10).

10 Artificial Neural Network Parallel computational models consisting of densely interconnected adaptive processing units which transform a set of inputs into a set of outputs Universal function approximators Static architectures can be used to make a time series prediction Turns a temporal sequence into a spatial pattern encoded on the input layer of the network using sliding window No explicit reference to the temporal nature of time. This work uses a straightforward static ANN: a single layer feed-forward network with single output Can be trained with ADALINE rule or Moore– Penrose pseudoinverse

11 Training and testing Model Predicted CSO Chamber Water depth n time steps forward Correlation analysis helps to select the lags Rainfall intensity parameter U was always one data step ahead of the chamber water depth parameter Y Prediction 1 to 5 time steps ahead (up to 1 hr 15 mins) Six month data set bisected into training and testing sets containing both dry and wet weather periods Various ANN models applied

12 Results One time step ahead prediction for unseen test data

13 Results Increase in test error as prediction forecast horizon (p) increases Less than 5% error for predictions 5 time steps ahead (75 minutes) for unseen data This improves on previous work which showed less than 5% error for 3 time steps ahead prediction (rain gauges with 5m sampling) but increased above this further into the future. RadarArchitecture Test RMSE Test % Grid square u delay y delay p ANN-1611813.97 1.99 ANN-268613.97 1.98 ANN-36151013.97 2.00 ANN-4611824.54 2.72 ANN-5611835.42 3.84 ANN-6611846.11 3.97 ANN-7611856.58 4.28 ANN-8511813.94 1.97 ANN-9511856.35 4.32 ANN-101811813.98 2.27 ANN-111811855.71 4.07

14 Results Prediction output shown four time steps advanced ANN-1 predicting chamber depth one hour in future – spilling after rainfall

15 Conclusions For the case study, chamber depth was found to be at a correlation maximum with rainfall radar at a lag of 60 to 75 minutes An ANN model trained with the pseudo-inverse rule to learn the response to rainfall was shown to be capable of providing prediction of CSO depth with less than 5% error for predictions 5 time steps ahead (75 minutes) for unseen data The tool offers the potential benefit of early detection of unexpected or abnormal performance behaviour and the identification of various failure modes in both dry and wet weather conditions thus enabling pollution incidents to be managed more proactively

16 Future work The water utility company is exploring a wider roll out of daily download for CSO assets and a six month project to develop an automated online pilot system to incorporate rainfall radar data will shortly commence Online data processing could allow the prediction of CSO failures (unconsented spill events) much earlier - potentially in real time Possible deviations between predicted and measured performance signify anomalies which could be highlighted using fuzzy logic, Bayesian inference systems or a BED There is significant potential for application to other sewerage asset types such as Detention Tanks and Sewer Pumping Stations with a view to enabling wider network performance visibility.

17 Future work CSO Analytics – Phase II System development and trial CSO telemetry system Rainfall Radar data 50 CSOs ANN hydraulic performance prediction model Daily data import ANN engine Predicted depth Classification module Lower than weir height Safe Beyond weir height Spill Interfacing from / to existing water company IT infrastructure

18 Thank you! Any Questions? With Thanks To:


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