Performance Analysis of the Water Quality Index Model for Predicting Water State Using Machine Learning Techniques

[1]  S. Nash,et al.  Assessing optimization techniques for improving water quality model , 2022, Journal of Cleaner Production.

[2]  S. Nash,et al.  A novel approach for estimating and predicting uncertainty in water quality index model using machine learning approaches. , 2022, Water research.

[3]  Meng Chun Lam,et al.  An Optimized Approach for Predicting Water Quality Features Based on Machine Learning , 2022, Wireless Communications and Mobile Computing.

[4]  S. Nash,et al.  Robust machine learning algorithms for predicting coastal water quality index. , 2022, Journal of environmental management.

[5]  Heba M. Ismail,et al.  Water Quality Classification Using Machine Learning Algorithms , 2022, 2022 IEEE/ACS 19th International Conference on Computer Systems and Applications (AICCSA).

[6]  S. Nash,et al.  A comprehensive method for improvement of water quality index (WQI) models for coastal water quality assessment. , 2022, Water research.

[7]  T. Kavzoglu,et al.  Advanced hyperparameter optimization for improved spatial prediction of shallow landslides using extreme gradient boosting (XGBoost) , 2022, Bulletin of Engineering Geology and the Environment.

[8]  M. Sciandrone,et al.  Nonlinear optimization and support vector machines , 2022, Annals of Operations Research.

[9]  N. Shaadan,et al.  Prediction of Water Quality Classification of the Kelantan River Basin, Malaysia, Using Machine Learning Techniques , 2022, Water.

[10]  A. Ahmed,et al.  Application of Soft Computing in Predicting Groundwater Quality Parameters , 2022, Frontiers in Environmental Science.

[11]  Jia Uddin,et al.  Water quality prediction and classification based on principal component regression and gradient boosting classifier approach , 2021, J. King Saud Univ. Comput. Inf. Sci..

[12]  A. I. Olbert,et al.  Assessment of Urban River Water Quality Using Modified NSF Water Quality Index Model at Siliguri City, West Bengal, India , 2022, SSRN Electronic Journal.

[13]  Yuk Feng Huang,et al.  Integration of advanced optimization algorithms into least-square support vector machine (LSSVM) for water quality index prediction , 2021, Water Supply.

[14]  Tran Minh Tung,et al.  Deep Learning for Prediction of Water Quality Index Classification: Tropical Catchment Environmental Assessment , 2021, Natural Resources Research.

[15]  P. Senthil Kumar,et al.  Automating water quality analysis using ML and auto ML techniques. , 2021, Environmental research.

[16]  M. Eftekhari,et al.  Decoding clinical biomarker space of COVID-19: Exploring matrix factorization-based feature selection methods , 2021, Computers in Biology and Medicine.

[17]  Suyog Gupta,et al.  A critical review on water quality index tool: Genesis, evolution and future directions , 2021, Ecol. Informatics.

[18]  M. Spiliotis,et al.  “One Out–All Out” Principle in the Water Framework Directive 2000—A New Approach with Fuzzy Method on an Example of Greek Lakes , 2021, Water.

[19]  M. Najafzadeh,et al.  A Novel Multiple-Kernel Support Vector Regression Algorithm for Estimation of Water Quality Parameters , 2021, Natural Resources Research.

[20]  Mohammad Najafzadeh,et al.  Reliability assessment of water quality index based on guidelines of national sanitation foundation in natural streams: integration of remote sensing and data-driven models , 2021, Artificial Intelligence Review.

[21]  S. Nash,et al.  A review of water quality index models and their use for assessing surface water quality , 2021 .

[22]  A. Helen Victoria,et al.  Automatic tuning of hyperparameters using Bayesian optimization , 2020, Evolving Systems.

[23]  Wengang Zhang,et al.  Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization , 2021 .

[24]  Mashael S. Maashi,et al.  Water Quality Prediction Using Artificial Intelligence Algorithms , 2020, Applied bionics and biomechanics.

[25]  Sakshi Khullar,et al.  Machine learning techniques in river water quality modelling: a research travelogue , 2020 .

[26]  Z. Yaseen,et al.  River water quality index prediction and uncertainty analysis: A comparative study of machine learning models , 2020 .

[27]  Jia Wu,et al.  Efficient hyperparameter optimization through model-based reinforcement learning , 2020, Neurocomputing.

[28]  Jafar Tanha,et al.  Boosting methods for multi-class imbalanced data classification: an experimental review , 2020, J. Big Data.

[29]  Bela Genge,et al.  Hybrid Hyper-parameter Optimization for Collaborative Filtering , 2020, 2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC).

[30]  Rung-Ching Chen,et al.  Selecting critical features for data classification based on machine learning methods , 2020, Journal of Big Data.

[31]  V. Tsihrintzis,et al.  Comparative evaluation of river chemical status based on WFD methodology and CCME water quality index. , 2020, The Science of the total environment.

[32]  Anju S Pillai,et al.  Comparison of Water Quality Classification Models using Machine Learning , 2020, 2020 5th International Conference on Communication and Electronics Systems (ICCES).

[33]  N. Srinivasu,et al.  Integrating Cuckoo search-Grey wolf optimization and Correlative Naive Bayes classifier with Map Reduce model for big data classification , 2020, Data Knowl. Eng..

[34]  Xin Yao,et al.  A Survey of Automatic Parameter Tuning Methods for Metaheuristics , 2020, IEEE Transactions on Evolutionary Computation.

[35]  Noël Crespi,et al.  A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction , 2020, Sensors.

[36]  Sohrab Hossain,et al.  Machine Learning-Based Phishing Attack Detection , 2020 .

[37]  Min Zuo,et al.  Comparative analysis of surface water quality prediction performance and identification of key water parameters using different machine learning models based on big data. , 2019, Water research.

[38]  Asad Ali Shah,et al.  Efficient Water Quality Prediction Using Supervised Machine Learning , 2019, Water.

[39]  Alireza Bahadori,et al.  Prediction of water quality index (WQI) using support vector machine (SVM) and least square-support vector machine (LS-SVM) , 2019, International Journal of River Basin Management.

[40]  Consolata Gakii,et al.  A Classification Model for Water Quality analysis Using Decision Tree , 2019 .

[41]  Mohammad Najafzadeh,et al.  Prediction of the five-day biochemical oxygen demand and chemical oxygen demand in natural streams using machine learning methods , 2019, Environmental Monitoring and Assessment.

[42]  I. Banerjee,et al.  A multi-class classification system for continuous water quality monitoring , 2019, Heliyon.

[43]  C. Beulah Christalin Latha,et al.  Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques , 2019, Informatics in Medicine Unlocked.

[44]  Mohammad Najafzadeh,et al.  Prediction of water quality parameters using evolutionary computing-based formulations , 2018, International Journal of Environmental Science and Technology.

[45]  Hardi TALABANI,et al.  Impact of Various Kernels on Support Vector Machine Classification Performance for Treating Wart Disease , 2018, 2018 International Conference on Artificial Intelligence and Data Processing (IDAP).

[46]  H. Mohammed,et al.  Predictive analysis of microbial water quality using machine-learning algorithms , 2018, Environmental Research, Engineering and Management.

[47]  R. Prakash,et al.  A Comparative Study of Various Classification Techniques to Determine Water Quality , 2018, 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT).

[48]  Abbas Parsaie,et al.  Water quality prediction using machine learning methods , 2018 .

[49]  Nitin Muttil,et al.  Development of a water quality index for rivers in West Java Province, Indonesia , 2018 .

[50]  A. Segura,et al.  Multiclass classification methods in ecology , 2018 .

[51]  Martin Kappas,et al.  Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery , 2017, Sensors.

[52]  Seyed-Mohammad Hosseini-Moghari,et al.  Classification of water quality status based on minimum quality parameters: application of machine learning techniques , 2018, Modeling Earth Systems and Environment.

[53]  Yanhui Guo,et al.  NS-k-NN: Neutrosophic Set-Based k-Nearest Neighbors Classifier , 2017, Symmetry.

[54]  Matthew P. Miller,et al.  Predicting redox‐sensitive contaminant concentrations in groundwater using random forest classification , 2017 .

[55]  Sakshi Babbar,et al.  Predicting river water quality index using data mining techniques , 2017, Environmental Earth Sciences.

[56]  M. Moniruzzaman,et al.  Evaluation of Groundwater Quality Using CCME Water Quality Index in the Rooppur Nuclear Power Plant Area, Ishwardi, Pabna, Bangladesh , 2017 .

[57]  Ilker Ünal,et al.  Defining an Optimal Cut-Point Value in ROC Analysis: An Alternative Approach , 2017, Comput. Math. Methods Medicine.

[58]  Jingjing Yin,et al.  Using the ROC Curve to Measure Association and Evaluate Prediction Accuracy for a Binary Outcome , 2017 .

[59]  Q. Zou,et al.  Finding the Best Classification Threshold in Imbalanced Classification , 2016, Big Data Res..

[60]  Nitin Muttil,et al.  Uncertainty and sensitivity analysis of West Java Water Sustainability Index – A case study on Citarum catchment in Indonesia , 2016 .

[61]  Azrul Amri Jamal,et al.  Classification Model for Water Quality using Machine Learning Techniques , 2015 .

[62]  W. Nelson,et al.  A method to identify estuarine water quality exceedances associated with ocean conditions , 2015, Environmental Monitoring and Assessment.

[63]  Weifeng Li,et al.  Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery , 2014, Remote. Sens..

[64]  Michael Hartnett,et al.  An integrated measurement and modeling methodology for estuarine water quality management , 2015 .

[65]  Shahab Araghinejad,et al.  A Comparative Assessment of Support Vector Machines, Probabilistic Neural Networks, and K-Nearest Neighbor Algorithms for Water Quality Classification , 2014, Water Resources Management.

[66]  G. Izzo,et al.  The "one-out, all-out" principle entails the risk of imposing unnecessary restoration costs: a study case in two Mediterranean coastal lakes. , 2014, Marine pollution bulletin.

[67]  Luzia Gonçalves,et al.  ROC curve estimation: An overview , 2014 .

[68]  Yoshua Bengio,et al.  Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..

[69]  Eric Monfroy,et al.  Autonomous Search , 2012, Springer Berlin Heidelberg.

[70]  Cha Zhang,et al.  Ensemble Machine Learning , 2012 .

[71]  K. P. Singh,et al.  Support vector machines in water quality management. , 2011, Analytica chimica acta.

[72]  Tania Pencheva,et al.  Tuning Genetic Algorithm Parameters to Improve Convergence Time , 2011 .

[73]  J. Mandrekar Receiver operating characteristic curve in diagnostic test assessment. , 2010, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[74]  Taskin Kavzoglu,et al.  A kernel functions analysis for support vector machines for land cover classification , 2009, Int. J. Appl. Earth Obs. Geoinformation.

[75]  Chong Sun Hong,et al.  Optimal Threshold from ROC and CAP Curves , 2009, Commun. Stat. Simul. Comput..

[76]  Lutz Hamel,et al.  Model Assessment with ROC Curves , 2009, Encyclopedia of Data Warehousing and Mining.

[77]  Robert O Strobl,et al.  Network design for water quality monitoring of surface freshwaters: a review. , 2008, Journal of environmental management.

[78]  Mevlut Ture,et al.  Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease , 2008, Expert Syst. Appl..

[79]  Jan Palczewski,et al.  Monte Carlo Simulation , 2008, Encyclopedia of GIS.

[80]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[81]  Foster J. Provost,et al.  ROC confidence bands: an empirical evaluation , 2005, ICML.

[82]  S D Walter,et al.  The partial area under the summary ROC curve , 2005, Statistics in medicine.

[83]  Kelly A. Coughlin,et al.  Receiver Operating Characteristic Curve Analysis of Beach Water Quality Indicator Variables , 2003, Applied and Environmental Microbiology.

[84]  Yoram Singer,et al.  Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers , 2000, J. Mach. Learn. Res..

[85]  Sašo Džeroski,et al.  Biological Monitoring: a Comparison between Bayesian, Neural and Machine Learning Methods of Water Quality Classification. , 1996 .