Extreme learning machine model for water network management

Abstract

A novel failure rate prediction model is developed by the extreme learning machine (ELM) to provide key information needed for optimum ongoing maintenance/ rehabilitation of a water network, meaning the estimated times for the next failures of individual pipes within the network. The developed ELM model is trained using more than 9500 instances of pipe failure in the Greater Toronto Area, Canada from 1920 to 2005 with pipe attributes as inputs, including pipe length, diameter, material, and previously recorded failures. The models show recent, extensive usage of pipe coating with cement mortar and cathodic protection has significantly increased their lifespan. The predictive model includes the pipe protection method as pipe attributes and can reflect in its predictions, the effect of different pipe protection methods on the expected time to the next pipe failure. The developed ELM has a superior prediction accuracy relative to other available machine learning algorithms such as feed-forward artificial neural network that is trained by backpropagation, support vector regression, and non-linear regression. The utility of the models provides useful inputs when planning and budgeting for watermain inspection, maintenance, and rehabilitation.

Hydraulic Modeling and Evaluation Equations for the Incipient Motion of Sandbags for Levee Breach Closure Operations

“…Using the calibrated and developed equations for sandbag incipient motion together with the experimental measurements, a quantitative assessment for the uncertainty in the prediction of critical velocity can be presented. The uncertainty analysis defines the prediction error in log cycles as [55][56][57][58][59][60][61][62][63][64][65] e i = log 10 (P i ) − log 10 (T i ) (10) where e i is the prediction error, P i is the predicted value of parameter, and T i is the measured value of the parameter. Data were then used to calculate main indicators defined as mean prediction error e = ∑ n i = 1 e i , the width of uncertainty band, B ub = ± 1.96S e and the confidence band around the predicted value:…”

A Markov chain analysis for BIST participation index

“…They obtained prediction accuracies for the two neural networks of about 94% in the training sample and 92% in the testing sample. In [20], the authors modelled the management of the water network using a single-layer extreme learning machine (ELM). The developed ELM model gave results with coefficients of determination ranging from 0.67 to 0.82.…”

Geoweaver: Advanced Cyberinfrastructure for Managing Hybrid Geoscientific AI Workflows

“…The application scope of machine learning is expanding rapidly in geospatial research. In recent studies, machine learning has been proven effective for extracting information from the immense amount of data collected within the geosciences [21][22][23][24][25]. The advantages of machine learning can support our efforts to develop the best possible predictive understanding of how the complex interacting geosystem works [23].…”