Event-based stormwater management pond runoff temperature model

Sabouri, F.; Gharabaghi, Bahram; Sattar, Ahmed A.; Thompson, Anita M.

doi:10.1016/j.jhydrol.2016.06.017

Cited by 28 publications

(19 citation statements)

References 38 publications

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“…As an alternative, the extreme learning machine (ELM) calculates optimum weights in a single hidden layer feed-forward artificial neural network [5]. Hence, ELM-ANN differs from the traditional FFBP-ANN method, as the optimum weights in the network are calculated analytically, resulting in high performance capacity and fast training for large data sets [6][7][8][9][10][11][12][13][14][15][16][17][18]. However, although having many desirable features, the authors have not identified any application of ELM-ANN to water pipe networks.…”

Section: Introductionmentioning

confidence: 99%

Extreme learning machine model for water network management

Sattar

Ertuğrul

Gharabaghi

et al. 2017

Neural Comput & Applic

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105

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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.

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Section: Introductionmentioning

confidence: 99%

Extreme learning machine model for water network management

Sattar

Ertuğrul

Gharabaghi

et al. 2017

Neural Comput & Applic

Self Cite

105

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“…Nonetheless, various soft computing methods of modeling complex phenomena have been applied in recent decades. Numerous researchers have used such methods, for instance fuzzy logic, artificial neural networks (ANN), gene expression programming (GEP), neuro‐fuzzy systems, genetic programming and support vector machines (SVM) to solve a variety of hydraulic problems, including open channel bend simulations (Gholami et al, , ), side weir discharge prediction (Dursun et al, ), sediment transport (Thompson et al, ), predicting the longitudinal dispersion coefficient in streams (Najafzadeh and Sattar, ; Sattar and Gharabaghi, ), dam breach parameters (Sattar, ), organic micropollutant removal in soil aquifer treatment systems (Sattar, ), predicting the timing of water main failure (Sattar et al, ), stormwater runoff temperature (Sabouri et al, ) and erosion resistance of cohesive soils (Sattar, ). Intense studies have been done on the application of soft computing methods to simulate engineering problems, with the number of such studies increasing every day.…”

Section: Introductionmentioning

confidence: 99%

Developing an expert group method of data handling system for predicting the geometry of a stable channel with a gravel bed

Gholami

Bonakdari

Ebtehaj

et al. 2017

Earth Surf Processes Landf

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Predicting the geometry of channels and alluvial rivers is of primary importance in river engineering science. Appropriately designing channels and predicting stable river cross‐sections can decrease costs and prevent the destruction of installations and agricultural land by rivers. Consequently, researchers have applied different empirical and regression methods to achieve relations for predicting stable channel and river geometry. In this study, Group Method of Data Handling ]GMDH) models are used to predict three geometric variables of stable channels, namely width (w), depth (h) and slope (s). The effect of different input parameters, such discharge (Q), median grain size (d50) and the Shields parameter (τ*) on the GMDH models is assessed with regard to predicting stable channel geometry. The results indicate that the GMDH model with mean absolute percentage error (MAPE) of 5.53%, 4.05% and 4.89% for channel width, depth and slope prediction respectively, exhibits good accuracy. Moreover, a comparison of the GMDH models with previous theoretical equations (based on regression analysis) indicates the superiority of GMDH model performance, with error reductions of one‐fifth, one‐eighth and one‐sixth compared with the regression equations for channel width, depth and slope prediction, respectively. Copyright © 2016 John Wiley & Sons, Ltd.

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“…By considering the mean error and standard deviation, confidence bound was defined by the predicted values using the Wilson score method without continuity correction. The 95% confidence bound is expressed as ±1.64 S e . The uncertainty analysis results for COD%, H 2 %, and HY are presented in Table .…”

Section: Resultsmentioning

confidence: 99%

“…The 95% confidence bound is expressed as AE1.64 S e . [43,44] The uncertainty analysis results for COD%, H 2 %, and HY are www.advancedsciencenews.com www.clean-journal.com presented in Table 9. The results show that the COD% was overestimated and the other two models underestimated.…”

Section: Uncertainty Analysis For Gep Predictionsmentioning

confidence: 99%

Evolutionary Prediction of Biohydrogen Production by Dark Fermentation

Akhbari

Ibrahim

Zinatizadeh

et al. 2018

CLEAN Soil Air Water

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The present work is a study of the performance and effect of operational parameters on biohydrogen production from palm oil mill effluent by dark fermentation in batch mode. The process parameters examined are pH (5, 5.5, and 6), temperature (30, 35, and 40 °C), substrate concentration (5000, 12 500, and 20 000 mg L−1) and inoculum volume (20, 25, and 30 mL). The inoculum concentration prepared was 10 000 mg L−1 volatile suspended solids. The experiments were designed by response surface methodology (RSM). The highest chemical oxygen demand (COD) removal, hydrogen percentage (H2%) and hydrogen yield (HY) obtained were 58.3%, 80%, and 4.83 mol H2/mole of COD consumed, respectively. Based on the experimental data obtained with the RSM design, gene expression programming (GEP) was developed to predict the COD removal, hydrogen production, and hydrogen yield as process responses. The projected models were assessed based on the correlation coefficient (R2), root mean square error, mean absolute relative error, scatter index, and BIAS. The results demonstrate that the GEP model outperformed the RSM model and was superior in predicting the response variables. Partial derivative sensitivity analysis was also employed to assess the effect of each variable on COD%, H2%, and HY prediction. The prediction uncertainty for COD%, H2%, and HY was quantified, and the results were ±0.11, ±0.17, and 0.015, respectively. According to the results, the GEP model is more efficient than the RSM model in predicting the experimental data for biological hydrogen production in the dark fermentation process.

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Event-based stormwater management pond runoff temperature model

Cited by 28 publications

References 38 publications

Extreme learning machine model for water network management

Extreme learning machine model for water network management

Developing an expert group method of data handling system for predicting the geometry of a stable channel with a gravel bed

Evolutionary Prediction of Biohydrogen Production by Dark Fermentation

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