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