Forecasting watermain failure using artificial neural network modelling

Figures & data

Figure 1 Number of failures and length of CML and CP per year.

Figure 2 Monthly number of failures from 1984 to 2003.

Figure 3 Scatter plot of MLR calculated failure rate vs. observedor a) MLR prediction and b) MLr cross-validation.

Figure 4 R² values for the training, testing and validation of the ANN model. a) training, b) testing, c) validation, and d) all data.

Figure 5 Comparison of current failure rate and after ten years for the pipelines which CML or CP have been implemented.

Table 1. The influence ranking of input variables on the output ANN model.

Input variable	Symbol	Rank
Break category	BC	1
Log Length	LL	2
Age	Age	3
CP year	YCP	4
Diameter	D	5
Soil Type	ST	6
CML year	YCML	7
Material	M	8