When creating an asset management plan, missing data is perceived to be a huge problem, especially when the event data (breaks in water distribution pipes as an example) are not tracked. The lack of tracking makes it ...When creating an asset management plan, missing data is perceived to be a huge problem, especially when the event data (breaks in water distribution pipes as an example) are not tracked. The lack of tracking makes it difficult to determine which factors are the critical ones. Many utilities lack the resources for examining buried infrastructure, so other methods of data collection are needed. The concept for this paper was to develop a means to acquire data on the assets for a condition assessment (buried pipe is not visible and in most cases, cannot really be assessed). What was found was that for buried infrastructure, much more information was known than anticipated. Knowing exact information is not really necessary. However, there was a need to track event-breaks, flooding etc.—what would indicate a “failure”.?The latter would be useful for predicting future maintenance needs and the most at-risk assets.展开更多
Shellfish farms are closed for harvest when microbial pollutants are present.Such pollutants are typically present in rainfall runoff from various land uses in catchments.Experts currently use a number of observable p...Shellfish farms are closed for harvest when microbial pollutants are present.Such pollutants are typically present in rainfall runoff from various land uses in catchments.Experts currently use a number of observable parameters(river flow,rainfall,salinity)as proxies to determine when to close farms.We have proposed using the short term historical rainfall data as a time-series prediction problem where we aim to predict the closure of shellfish farms based only on rainfall.Time-series event prediction consists of two steps:(i)feature extraction,and(ii)prediction.A number of data mining challenges exist for these scenarios:(i)which feature extraction method best captures the rainfall pattern over successive days that leads to opening or closure of the farms?,(ii)The farm closure events occur infrequently and this leads to a class imbalance problem;the question is what is the best way to deal with this problem?In this paper we have analysed and compared different combinations of balancing methods(under-sampling and over-sampling),feature extraction methods(cluster profile,curve fitting,Fourier Transform,Piecewise Aggregate Approximation,and Wavelet Transform)and learning algorithms(neural network,support vector machine,k-nearest neighbour,decision tree,and Bayesian Network)to predict closure events accurately considering the above data mining challenges.We have identified the best combination of techniques to accurately predict shellfish farm closure from rainfall,given the above data mining challenges.展开更多
文摘When creating an asset management plan, missing data is perceived to be a huge problem, especially when the event data (breaks in water distribution pipes as an example) are not tracked. The lack of tracking makes it difficult to determine which factors are the critical ones. Many utilities lack the resources for examining buried infrastructure, so other methods of data collection are needed. The concept for this paper was to develop a means to acquire data on the assets for a condition assessment (buried pipe is not visible and in most cases, cannot really be assessed). What was found was that for buried infrastructure, much more information was known than anticipated. Knowing exact information is not really necessary. However, there was a need to track event-breaks, flooding etc.—what would indicate a “failure”.?The latter would be useful for predicting future maintenance needs and the most at-risk assets.
文摘Shellfish farms are closed for harvest when microbial pollutants are present.Such pollutants are typically present in rainfall runoff from various land uses in catchments.Experts currently use a number of observable parameters(river flow,rainfall,salinity)as proxies to determine when to close farms.We have proposed using the short term historical rainfall data as a time-series prediction problem where we aim to predict the closure of shellfish farms based only on rainfall.Time-series event prediction consists of two steps:(i)feature extraction,and(ii)prediction.A number of data mining challenges exist for these scenarios:(i)which feature extraction method best captures the rainfall pattern over successive days that leads to opening or closure of the farms?,(ii)The farm closure events occur infrequently and this leads to a class imbalance problem;the question is what is the best way to deal with this problem?In this paper we have analysed and compared different combinations of balancing methods(under-sampling and over-sampling),feature extraction methods(cluster profile,curve fitting,Fourier Transform,Piecewise Aggregate Approximation,and Wavelet Transform)and learning algorithms(neural network,support vector machine,k-nearest neighbour,decision tree,and Bayesian Network)to predict closure events accurately considering the above data mining challenges.We have identified the best combination of techniques to accurately predict shellfish farm closure from rainfall,given the above data mining challenges.
