Toward urban sustainability and clean potable water: Prediction of water quality via artificial neural networks

[1]  Prabhata K. Swamee,et al.  DESCRIBING WATER QUALITY WITH AGGREGATE INDEX , 2000 .

[2]  Andrew J. Day,et al.  Sensor-fusion of hydraulic data for burst detection and location in a treated water distribution system , 2003, Inf. Fusion.

[3]  Semiha Kiziltas,et al.  Prediction of organizational effectiveness in construction companies , 2005 .

[4]  Aminuddin Ab. Ghani,et al.  Storm water treatment using Bio‐Ecological Drainage System , 2005 .

[5]  Sepehr Ghazinoory,et al.  Cleaner production in Iran: necessities and priorities , 2005 .

[6]  Daniel W. Halpin,et al.  Deterministic models for assessing productivity and cost of bored piles , 2005 .

[7]  Brent Doberstein,et al.  Pipeline risk assessment and risk acceptance criteria in the State of Sao Paulo, Brazil , 2006 .

[8]  R. Sadiq,et al.  Water Quality Failures in Distribution Networks—Risk Analysis Using Fuzzy Logic and Evidential Reasoning , 2007, Risk analysis : an official publication of the Society for Risk Analysis.

[9]  Kerry J. McManus,et al.  Prediction of Water Pipe Asset Life Using Neural Networks , 2007 .

[10]  Rehan Sadiq,et al.  Predicting risk of water quality failures in distribution networks under uncertainties using fault-tree analysis , 2008 .

[11]  Tarek Zayed,et al.  Infrastructure Management : Integrated AHP/ANN Model to Evaluate Municipal Water Mains' Performance , 2008 .

[12]  Osama Moselhi,et al.  Forecasting the Remaining Useful Life of Cast Iron Water Mains , 2009 .

[13]  Dragan Savic,et al.  Assessing pipe failure rate and mechanical reliability of water distribution networks using data-driven modeling , 2009 .

[14]  Isam Shahrour,et al.  Application of Artificial Neural Networks (ANN) to model the failure of urban water mains , 2010, Math. Comput. Model..

[15]  W. Bauwens,et al.  Modeling the structural deterioration of urban drainage pipes: the state-of-the-art in statistical methods , 2010 .

[16]  Symeon E. Christodoulou,et al.  Proactive Risk-Based Integrity Assessment of Water Distribution Networks , 2010 .

[17]  Mohammad Karamouz,et al.  Pressure Management Model for Urban Water Distribution Networks , 2010 .

[18]  Ahmed El-Shafie,et al.  Application of artificial neural networks for water quality prediction , 2012, Neural Computing and Applications.

[19]  NishiyamaMichael,et al.  Review of statistical water main break prediction models , 2013 .

[20]  Ahmad Asnaashari,et al.  Forecasting watermain failure using artificial neural network modelling , 2013 .

[21]  Bahram Gharabaghi,et al.  Predicting the Timing of Water Main Failure Using Artificial Neural Networks , 2014 .

[22]  Chun Kiat Chang,et al.  Spatial pattern analysis for water quality in free-surface constructed wetland. , 2014, Water science and technology : a journal of the International Association on Water Pollution Research.

[23]  Massoud Tabesh,et al.  A comparison between performance of support vector regression and artificial neural network in prediction of pipe burst rate in water distribution networks , 2014, KSCE Journal of Civil Engineering.

[24]  Chun Kiat Chang,et al.  Prediction of water quality index in constructed wetlands using support vector machine , 2015, Environmental Science and Pollution Research.

[25]  Małgorzata Kutyłowska,et al.  Neural network approach for failure rate prediction , 2015 .

[26]  Danial Jahed Armaghani,et al.  Application of artificial neural network for predicting shaft and tip resistances of concrete piles , 2015 .

[27]  Il Won Seo,et al.  Artificial Neural Network ensemble modeling with conjunctive data clustering for water quality prediction in rivers , 2015 .

[28]  Emad Elwakil Integrating AHP-Fuzzy Model for Assessing Construction Organizations’ Performance , 2016 .

[29]  Osama Moselhi,et al.  Assessment of Remaining Useful Life of Pipelines Using Different Artificial Neural Networks Models , 2016 .

[30]  Nor Azazi Zakaria,et al.  Prediction of water quality index in free surface constructed wetlands , 2016, Environmental Earth Sciences.

[31]  S. Shamshirband,et al.  Modeling energy consumption and greenhouse gas emissions for kiwifruit production using artificial neural networks , 2016 .

[32]  Reza Farzipoor Saen,et al.  Forecasting efficiency of green suppliers by dynamic data envelopment analysis and artificial neural networks , 2017 .

[33]  F. Qaderi,et al.  Prediction of the groundwater remediation costs for drinking use based on quality of water resource, using artificial neural network , 2017 .

[34]  Nilanjan Dey,et al.  Water quality prediction: Multi objective genetic algorithm coupled artificial neural network based approach , 2017, 2017 IEEE 15th International Conference on Industrial Informatics (INDIN).

[35]  Bahram Gharabaghi,et al.  Extreme learning machine model for water network management , 2017, Neural Computing and Applications.

[36]  Dongwoo Jang,et al.  Estimation of Leakage Ratio Using Principal Component Analysis and Artificial Neural Network in Water Distribution Systems , 2018 .

[37]  M. Omid,et al.  Sensitivity analysis of energy inputs in crop production using artificial neural networks , 2018, Journal of Cleaner Production.

[38]  Suzhen Li,et al.  Leak detection of water distribution pipeline subject to failure of socket joint based on acoustic emission and pattern recognition , 2018 .

[39]  Tarek Zayed,et al.  Computer Vision-Based Model for Moisture Marks Detection and Recognition in Subway Networks , 2018, J. Comput. Civ. Eng..

[40]  E. Elahi,et al.  Use of artificial neural networks to rescue agrochemical-based health hazards: A resource optimisation method for cleaner crop production , 2019, Journal of Cleaner Production.

[41]  Bhavana N. Umrikar,et al.  Prediction of water quality index using artificial neural network and multiple linear regression modelling approach in Shivganga River basin, India , 2019, Modeling Earth Systems and Environment.

[42]  Emad Elwakil,et al.  Artificial intelligence for the modeling of water pipes deterioration mechanisms , 2020 .

[43]  Edward A. McBean,et al.  Improving Urban Water Security through Pipe-Break Prediction Models: Machine Learning or Survival Analysis , 2020 .

[44]  Emad Elwakil,et al.  Water pipe failure prediction and risk models: state-of-the-art review , 2020 .

[45]  Tarek Zayed,et al.  Sustainability-informed multi-criteria decision support framework for ranking and prioritization of pavement sections , 2020 .

[46]  A. Bokhari,et al.  Optimization on cleaner intensification of ozone production using Artificial Neural Network and Response Surface Methodology: Parametric and comparative study , 2020 .