Water quality classification using machine learning algorithms

Journal of Water Process Engineering - Tập 48 - Trang 102920 - 2022
Nida Nasir1, Afreen Kansal2, Omar Alshaltone1, Feras Barneih1, Mustafa Sameer3, Abdallah Shanableh1, Ahmed Al-Shamma'a1
1Research Institute of Science and Engineering, University of Sharjah, United Arab Emirates
2Department of Statistics, London School of Economics, United Kingdom
3National Institute of Technology, Patna, India

Tài liệu tham khảo

Brar O’Flynn, 2010, Experiences and recommendations in deploying a real-time, water quality monitoring system, Meas. Sci. Technol., 21 Kedia, 2015, Water quality monitoring for rural areas- a Sensor Cloud based economical project, 50 Alshaltone, 2021, Multi sensing platform for real time water monitoring using electromagnetic sensor, 174 Sun, 2019, How can big data and machine learning benefit environment and water management: a survey of methods, applications, and future directions, Environ. Res. Lett., 14, 10.1088/1748-9326/ab1b7d Bagheri, 2019, Advanced control of membrane fouling in filtration systems using artificial intelligence and machine learning techniques: a critical review, Process Saf. Environ. Prot., 123, 229, 10.1016/j.psep.2019.01.013 Hassanpour, 2019, Development of the FCM-SVR hybrid model for estimating the suspended sediment load, KSCE J. Civ. Eng., 23, 2514, 10.1007/s12205-019-1693-7 Ehteram, 2019, Investigation on the potential to integrate different artificial intelligence models with metaheuristic algorithms for improving river suspended sediment predictions, Appl. Sci., 9, 10.3390/app9194149 Nasir, 2018, Optical detection of dissolved solids in water samples, 1 Nasir, 2019, Capacitive detection and quantification of water suspended solids, 1 Huang, 2010, Application WASP model on validation of reservoir-drinking water source protection areas delineation, 7, 3031 Lai, 2011, Evaluation of non-point source pollution and river water quality using a multimedia two-model system, J. Hydrol., 409, 583, 10.1016/j.jhydrol.2011.08.040 Warren, 1992, MIKE 21: a modelling system for estuaries, coastal waters and seas, Environ. Softw., 7, 229, 10.1016/0266-9838(92)90006-P Tang, 2015, Two-dimensional water environment numerical simulation research based on EFDC in Mudan River, Northeast China, 238 Batur, 2019, Assessment of surface water quality by using satellite images fusion based on PCA method in the Lake gala, Turkey, IEEE Trans. Geosci. Remote Sens., 57, 2983, 10.1109/TGRS.2018.2879024 Liao, 2010, Forecasting and evaluating water quality of chao Lake based on an improved decision tree method, Procedia Environ. Sci., 2, 970, 10.1016/j.proenv.2010.10.109 Solanki, 2015, Predictive analysis of water quality parameters using deep learning, Int. J. Comput. Appl., 125, 29 Shafi, 2018, Surface water pollution detection using internet of things, 92 Gazzaz, 2012, Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors, Mar. Pollut. Bull., 64, 2409, 10.1016/j.marpolbul.2012.08.005 Sakizadeh, 2016, Artificial intelligence for the prediction of water quality index in groundwater systems, Model. Earth Syst. Environ., 2, 8, 10.1007/s40808-015-0063-9 Abyaneh, 2014, Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters, J. Environ. Health Sci. Eng., 12, 40, 10.1186/2052-336X-12-40 Liu, 2020, Accurate prediction scheme of water quality in smart mariculture with deep Bi-S-SRU learning network, IEEE Access, 8, 24784, 10.1109/ACCESS.2020.2971253 Jaloree, 2014, Decision tree approach to build a model for water quality, Binary J. Data Min. Netw., 4, 25 Singh, 2014, Comparison of artificial neural network algorithm for water quality prediction of river Ganga, Environ. Res. J., 8, 55 Vasudevan Kangabam, 2017, Development of a water quality index (WQI) for the Loktak Lake in India, Appl Water Sci, 7, 2907, 10.1007/s13201-017-0579-4 Mensah, 2020, Application of adaptive neuro-fuzzy inference system in flammability parameter prediction, Polymers (Basel), 12 Kumar, 2012, A review of permissible limits of drinking water, Indian J. Occup. Environ. Med., 16, 40, 10.4103/0019-5278.99696 Chen, 2021, Two novelty learning models developed based on deep cascade forest to address the environmental imbalanced issues: a case study of drinking water quality prediction, Environ. Pollut., 291, 118153, 10.1016/j.envpol.2021.118153 Aldhyani, 2020, Water quality prediction using artificial intelligence algorithms, Appl. Bionics Biomech., 2020, 10.1155/2020/6659314 Xiang, 2009, Water quality prediction using LS-SVM and particle swarm optimization, 900 Slapničar, 2019, Blood pressure estimation from photoplethysmogram using a spectro-temporal deep neural network, Sensors, 19, 3420, 10.3390/s19153420 Bashar, 2019, A machine learning approach for heart rate estimation from PPG signal using random forest regression algorithm, 1 Najah, 2011, An application of different artificial intelligences techniques for water quality prediction, IJPS, 6, 5298 Krhoda, 2019, Groundwater quality prediction using logistic regression model for Garissa County, Afr. J. Phys. Sci., 3, 13 Lu, 2020, Hybrid decision tree-based machine learning models for short-term water quality prediction, Chemosphere, 249, 10.1016/j.chemosphere.2020.126169 Gakii Zhou, 2021, Fire prediction based on CatBoost algorithm, Math. Probl. Eng., 2021, 10.1155/2021/1929137 Abu Salem, 2021, Feature selection approaches for predictive modelling of cadmium sources and pollution levels in water springs, Environ. Sci. Pollut. Res. Sathyanarayana, 2016, Sleep quality prediction from wearable data using deep learning, JMIR Mhealth Uhealth, 4 Brooks, 2016, Predicting recreational water quality advisories: a comparison of statistical methods, Environ. Model Softw., 76, 81, 10.1016/j.envsoft.2015.10.012 Chatterjee, 2017, Water quality prediction: multi objective genetic algorithm coupled artificial neural network based approach, 963 Chen, 2020, Comparative analysis of surface water quality prediction performance and identification of key water parameters using different machine learning models based on big data, Water Res., 171, 115454, 10.1016/j.watres.2019.115454 Grbcic Wang, 2015, Cyber-physical systems for water sustainability: challenges and opportunities, IEEE Commun. Mag., 53, 216, 10.1109/MCOM.2015.7105668 Xu, 2020, A predictive model of recreational water quality based on adaptive synthetic sampling algorithms and machine learning, Water Res., 177, 10.1016/j.watres.2020.115788 Yahya, 2019, Water quality prediction model based support vector machine model for ungauged river catchment under dual scenarios, Water, 11 Deepnarain, 2019, Decision tree for identification and prediction of filamentous bulking at full-scale activated sludge wastewater treatment plant, Process. Saf. Environ. Prot., 126, 25, 10.1016/j.psep.2019.02.023 Wise, 2007, Effects of resource availability on tolerance of herbivory: a review and assessment of three opposing models, Am. Nat., 169, 443, 10.1086/512044 Christodoulou, 2019, A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models, J. Clin. Epidemiol., 110, 12, 10.1016/j.jclinepi.2019.02.004 Uddameri, 2020, Tree-based modeling methods to predict nitrate exceedances in the Ogallala Aquifer in Texas, Water, 12, 10.3390/w12041023 Chen, 2018, A novel fuzzy deep-learning approach to traffic flow prediction with uncertain spatial–temporal data features, Futur. Gener. Comput. Syst., 89, 78, 10.1016/j.future.2018.06.021 Yu, 2004, Comparison of the support vector machine and relevant vector machine in regression and classification problems, vol. 2, 1309 Auria, 2008 2017 Tu, 1996, Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes, J. Clin. Epidemiol., 49, 1225, 10.1016/S0895-4356(96)00002-9 Kumar, 2019 Hancock, 2020, CatBoost for big data: an interdisciplinary review, J. Big Data, 7, 94, 10.1186/s40537-020-00369-8 Ahmad, 2017, Improving water quality index prediction in Perak River basin Malaysia through a combination of multiple neural networks, Int. J. River Basin Manag., 15, 79, 10.1080/15715124.2016.1256297 Ranković, 2010, Neural network modeling of dissolved oxygen in the Gruža reservoir, Serbia, Ecol. Model., 221, 1239, 10.1016/j.ecolmodel.2009.12.023 Ahmed, 2019, Efficient water quality prediction using supervised machine learning, Water, 11, 10.3390/w11112210 Hafeez, 2019, Comparison of machine learning algorithms for retrieval of water quality indicators in case-II waters: a case study of Hong Kong, Remote Sens., 11, 10.3390/rs11060617 Di, 2019, Water quality evaluation of the Yangtze River in China using machine learning techniques and data monitoring on different time scales, Water, 11, 10.3390/w11020339 Gupta, 2018, Ground water quality monitoring using wireless sensors and machine learning, 121 da Silva, 2021, A machine learning approach for monitoring Brazilian optical water types using Sentinel-2 MSI, Remote Sens. Appl. Soc. Environ., 23, 100577 Barzegar, 2020, Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model, Stoch. Env. Res. Risk A., 34, 415, 10.1007/s00477-020-01776-2 Al-Adhaileh, 2021, Modelling and prediction of water quality by using artificial intelligence, Sustainability, 13 Shah, 2021, Modeling surface water quality using the adaptive neuro-fuzzy inference system aided by input optimization, Sustainability, 13, 10.3390/su13084576 Ahmed, 2019, Machine learning methods for better water quality prediction, J. Hydrol., 578, 124084, 10.1016/j.jhydrol.2019.124084 Vergina, 2020, A real time water quality monitoring using machine learning algorithm, Clin. Med., 07, 7 Mohammed, 2018, Predictive analysis of microbial water quality using machine-learning algorithms, Environ. Res. Eng. Manag., 74, 10.5755/j01.erem.74.1.20083 Kaur, 2021, Classification and analysis of water quality using machine learning algorithms, 389 Northep, 2020, Water quality classification using data mining techniques: a case study on Wang River in Thailand, 1 Chang, 2017, Integrating multisensor satellite data merging and image reconstruction in support of machine learning for better water quality management, J. Environ. Manag., 201, 227, 10.1016/j.jenvman.2017.06.045 Nourani, 2018, Wastewater treatment plant performance analysis using artificial intelligence – an ensemble approach, Water Sci. Technol., 78, 2064, 10.2166/wst.2018.477 Alizadeh, 2018, Effect of river flow on the quality of estuarine and coastal waters using machine learning models, Eng. Appl. Comput. Fluid Mech., 12, 810 Bui, 2020, Improving prediction of water quality indices using novel hybrid machine-learning algorithms, Sci. Total Environ., 721, 10.1016/j.scitotenv.2020.137612 Folorunso, 2019, Water quality index estimation model for aquaculture system using artificial neural network, J. Adv. Comput. Eng. Technol., 5, 179 Chang, 2015, Integrated satellite data fusion and mining for monitoring lake water quality status of the Albufera de Valencia in Spain, J. Environ. Manag., 151, 416, 10.1016/j.jenvman.2014.12.003 Barzegar, 2016, Application of wavelet-artificial intelligence hybrid models for water quality prediction: a case study in aji-Chay River, Iran, Stoch. Env. Res. Risk A., 30, 1797, 10.1007/s00477-016-1213-y