| Sonstiges: |
- Nachgewiesen in: MEDLINE
- Sprachen: English
- Publication Type: Journal Article; Review
- Language: English
- [Environ Sci Pollut Res Int] 2024 May; Vol. 31 (21), pp. 30370-30398. <i>Date of Electronic Publication: </i>2024 Apr 20.
- MeSH Terms: Neural Networks, Computer* ; Water Resources* ; Algorithms* ; Machine Learning* ; Water Pollutants, Chemical* / analysis ; Environmental Monitoring / methods ; Fuzzy Logic
- References: Abba SI, Benaafi M, Usman AG, Aljundi IH (2023) Sandstone groundwater salinization modelling using physicochemical variables in Southern Saudi Arabia: application of novel data intelligent algorithms. Ain Shams Eng J 14(3):101894. https://doi.org/10.1016/j.asej.2022.101894. (PMID: 10.1016/j.asej.2022.101894) ; Abba SI, Usman AG, Danmaraya YA, Usman AG, Abdullahi HU (2020) Modeling of water treatment plant performance using artificial neural network: case study Tamburawa Kano-Nigeria. Dutse J Pure Appl Sci (DUJOPAS) 6(3):135–114. ; Abba SI, Yassin MA, Shah SMH, Egbueri JC, Elzain HE, Agbasi JC, Saini G, Usaman J, Khan NA, Aljundi IH (2024) Trace element pollution tracking in the complex multi-aquifer groundwater system of Al-Hassa oasis (Saudi Arabia) using spatial, chemometric and index-based techniques. Environ Res:118320. https://doi.org/10.1016/j.envres.2024.118320. ; Abbas H (2021) Removal of potentially toxic elements (PTEs) from contaminated water using microcrystalline cellulose extracted from rice Straw. Environ Biodiver Soil Security 5(2021):31–40. ; Abd El-Mageed AM, Enany TA, Goher ME, Hassouna ME (2022) Forecasting water quality parameters in Wadi El Rayan Upper Lake, Fayoum, Egypt using adaptive neuro-fuzzy inference system. The Egypt J Aqua Res 48(1):13–19. https://doi.org/10.1016/j.ejar.2021.10.001. (PMID: 10.1016/j.ejar.2021.10.001) ; Abu M, Akurugu BA, Egbueri JC (2024) Understanding groundwater mineralization controls and the implications on its quality (Southwestern Ghana): insights from hydrochemistry, multivariate statistics, and multi-linear regression models. Acta Geophys. https://doi.org/10.1007/s11600-023-01271-6. ; Abugu HO, Egbueri JC, Agbasi JC, Ezugwu AL, Omeka ME, Ucheana IA, Aralu CC (2024) Hydrochemical characterization of ground and surface water for irrigation application in nigeria: a review of progress. Chem Afr. https://doi.org/10.1007/s42250-024-00917-2. ; Adeoti OA, Osanaiye PA (2013) Effect of training algorithms on the performance of ANN for pattern recognition of bivariate process. Int J Comput Appl 69(2). ; Agah A, Soleimanpourmoghadam N (2020) Design and implementation of heavy metal prediction in acid mine drainage using multi-output adaptive neuro-fuzzy inference systems (ANFIS)-a case study. Int J Mining Geo-Eng 54(1):59–64. ; Agbasi JC, Egbueri JC (2022) Assessment of PTEs in water resources by integrating HHRISK code, water quality indices, multivariate statistics, and ANNs. Geocarto Int. https://doi.org/10.1080/10106049.2022.2034990. ; Agbasi JC, Ezugwu AL, Omeka ME, Ucheana IA, Aralu CC, Abugu HO, Egbueri JC (2024) More about making profits or providing safe drinking water? A state-of-the-art review on sachet water contamination in Nigeria. J Environ Sci Health C:1–43. https://doi.org/10.1080/26896583.2024.2319009. ; Aggarwal R, Ranganathan P (2016) Common pitfalls in statistical analysis: the use of correlation techniques. Perspect Clin Res 7(4):187. https://doi.org/10.4103/2229-3485.192046. (PMID: 10.4103/2229-3485.192046) ; Alayat H, El Badaoui H, Abdallaoui A, Abrid D, El Hmaidi A (2018) Development of mathematical models for predicting the iron concentrations of lake Oubeira waters (ne Algerian). J Fundam Appl Sci 10(1):83–96. https://doi.org/10.4314/jfas.v10i1.6. (PMID: 10.4314/jfas.v10i1.6) ; Alipour M, Sarafraz M, Chavoshi H, Bay A, Nematollahi A, Sadani M, Fakhri Y, Vasseghian Y, Khaneghah AM (2021) The concentration and probabilistic risk assessment of potentially toxic elements in fillets of silver pomfret (Pampus argenteus): a global systematic review and meta-analysis. J Environ Sci 100:167–180. (PMID: 10.1016/j.jes.2020.07.014) ; Alizamir M, Sobhanardakani S (2016) Forecasting of heavy metals concentration in groundwater resources of Asadabad plain using artificial neural network approach. J Adv Environ Health Res 4(2):68–77. ; Alizamir M, Sobhanardakani S (2017) A comparison of performance of artificial neural networks for prediction of heavy metals concentration in groundwater resources of toyserkan plain. Avicenna J Environ Health Eng 4(1):11792–11792. https://doi.org/10.5812/ajehe.11792. (PMID: 10.5812/ajehe.11792) ; Alizamir M, Sobhanardakani S, Shahrabadi AH (2019) Prediction of heavy metals concentration in the groundwater resources in Razan Plain: extreme learning machine vs. artificial neural network and multivariate adaptive regression spline. Ann Military Health Sci Res 17(4). https://doi.org/10.5812/amh.98554. ; Alizamir M, Sobhanardakani S, Taghavi L (2017) Modeling of groundwater resources heavy metals concentration using soft computing methods: application of different types of artificial neural networks. J Chem Health Risks 7(3). ; Allwright, S. (2022). What is a good MSE value? (Simply explained). https://stephenallwright.com/good-mse-value/# . Retrieved September 7, 2022. ; Aryadoust V, Goh CC (2014) Predicting listening item difficulty with language complexity measures: a comparative data mining study. CaMLA Working Papers, 2014-01. ; Asgharnia A, Jamali A, Shahnazi R, Maheri A (2019) Load mitigation of a class of 5-MW wind türbine with RBF neural network based fractional-order PID controller. ISA Trans 96:272–286. https://doi.org/10.1016/j.isatra.2019.07.006. (PMID: 10.1016/j.isatra.2019.07.006) ; Ayejoto DA, Agbasi JC, Egbueri JC, Echefu K (2022) Assessment of oral and dermal health risk exposures associated with contaminated water resources: an update in Ojoto area, southeast Nigeria. Int J Environ Anal Chem. https://doi.org/10.1080/03067319.2021.2023515. ; Azrour M, Mabrouki J, Fattah G, Guezzaz A, Aziz F (2022) Machine learning algorithms for efficient water quality prediction. Model Earth Syst Environ 8(2):2793–2801. (PMID: 10.1007/s40808-021-01266-6) ; Bayatzadeh Fard Z, Ghadimi F, Fattahi H (2017) Use of artificial intelligence techniques to predict distribution of heavy metals in groundwater of Lakan lead-zinc mine in Iran. J Mining Environ 8(1):35–48. https://doi.org/10.22044/jme.2016.592. (PMID: 10.22044/jme.2016.592) ; Bellatreche L, Ordonez C, Méry D, Golfarelli M (2022) The central role of data repositories and data models in data science and advanced analytics. Futur Gener Comput Syst 129:13–17. (PMID: 10.1016/j.future.2021.11.027) ; Bhatt S, Mishra AP, Chandra N, Sahu H, Chaurasia SK, Pande CB, Agbasi JC, Khan MYA, Abba SI, Egbueri JC, Hunt J (2024) Characterizing seasonal, environmental and human-induced factors influencing the dynamics of Rispana River’s water quality: implications for sustainable river management. Res Eng:102007. https://doi.org/10.1016/j.rineng.2024.102007. ; Bini C, Wahsha M (2014) Potentially harmful elements and human health. In: PHEs, environment and human health. Springer, Dordrecht, pp 401–463. (PMID: 10.1007/978-94-017-8965-3_11) ; Boudaghpour S, Malekmohammadi S (2020) Modeling prediction of dispersal of heavy metals in plain using neural network. J Appl Water Eng Res 8(1):28–43. https://doi.org/10.1080/23249676.2020.1719219. (PMID: 10.1080/23249676.2020.1719219) ; Broomhead DS, Lowe D (1988) Multivariable functional interpolation and adaptive networks. Complex Syst 2:321–355. ; Brunton SL, Kutz JN (2022) Data-driven science and engineering: machine learning, dynamical systems, and control. Cambridge University Press. (PMID: 10.1017/9781009089517) ; Burse K, Yadav RN, Shrivastava SC (2010) Channel equalization using neural networks: a review. IEEE Trans Syst Man Cybern Part C Appl Rev 40(3):352–357. (PMID: 10.1109/TSMCC.2009.2038279) ; Chaal RE, Aboutafail MO (2021) Development of stochastic mathematical models for the prediction of heavy metal content in surface waters using artificial neural network and multiple linear regression. In: E3S Web of Conferences, vol 314. EDP Sciences, p 02001. https://doi.org/10.1051/e3sconf/202131402001. (PMID: 10.1051/e3sconf/202131402001) ; Chang FJ, Chung CH, Chen PA, Liu CW, Coynel A, Vachaud G (2014) Assessment of arsenic concentration in stream water using neuro fuzzy networks with factor analysis. Sci Total Environ 494:202–210. https://doi.org/10.1016/j.scitotenv.2014.06.133. (PMID: 10.1016/j.scitotenv.2014.06.133) ; Chebrolu S, Abraham A, Thomas JP (2005) Feature deduction and ensemble design of intrusion detection systems. Comput Secur 24(4):295–307. (PMID: 10.1016/j.cose.2004.09.008) ; Cho KH, Sthiannopkao S, Pachepsky YA, Kim KW, Kim JH (2011) Prediction of contamination potential of groundwater arsenic in Cambodia, Laos, and Thailand using artificial neural network. Water Res 45(17):5535–5544. https://doi.org/10.1016/j.watres.2011.08.010. (PMID: 10.1016/j.watres.2011.08.010) ; Chojnacki A, Dai C, Farahi A, Shi G, Webb J, Zhang DT, Abernethy J, Schwartz E (2017) A data science approach to understanding residential water contamination in flint. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 1407–1416. (PMID: 10.1145/3097983.3098078) ; Diamantopoulou MJ, Papamichail DM, Antonopoulos VZ (2005) The use of a Neural Network technique for the prediction of water quality parameters. Oper Res 5(1):115–125. https://doi.org/10.1007/BF02944165. (PMID: 10.1007/BF02944165) ; Dogo EM, Afolabi OJ, Nwulu NI, Twala B, Aigbavboa CO (2018) A comparative analysis of gradient descent-based optimization algorithms on convolutional neural networks. In: In 2018 international conference on computational techniques, electronics and mechanical systems (CTEMS). IEEE, pp 92–99. (PMID: 10.1109/CTEMS.2018.8769211) ; Doshi S (2019) Various optimization algorithms for training neural network. Towards data science, p 13. ; Draper C, Reichle R, de Jeu R, Naeimi V, Parinussa R, Wagner W (2013) Estimating root mean square errors in remotely sensed soil moisture over continental scale domains. Remote Sens Environ 137:288–298. https://doi.org/10.1016/j.rse.2013.06.013. (PMID: 10.1016/j.rse.2013.06.013) ; Egbueri JC (2019) Groundwater quality assessment using pollution index of groundwater (PIG), ecological risk index (ERI) and hierarchical cluster analysis (HCA): a case study. Groundw Sustain Dev. https://doi.org/10.1016/j.gsd.2019.100292. ; Egbueri JC (2020) Heavy metals pollution source identification and probabilistic health risk assessment of shallow groundwater in Onitsha, Nigeria. Anal Lett 53(10):1620–1638. https://doi.org/10.1080/00032719.2020.1712606. (PMID: 10.1080/00032719.2020.1712606) ; Egbueri JC (2021) Prediction modeling of potentially toxic elements’ hydrogeopollution using an integrated Q–mode HCs and ANNs machine learning approach in SE Nigeria. Environ Sci Pollut Res 28(30):40938–40956. https://doi.org/10.1007/s11356-021-13678-z. (PMID: 10.1007/s11356-021-13678-z) ; Egbueri JC (2022) Predicting and analysing the quality of water resources for industrial purposes using integrated data-intelligent algorithms. Groundw Sustain Dev 18:100794. https://doi.org/10.1016/j.gsd.2022.100794. (PMID: 10.1016/j.gsd.2022.100794) ; Egbueri JC, Agbasi JC (2022a) Combining data-intelligent algorithms for the assessment and predictive modeling of groundwater resources quality in parts of southeastern Nigeria. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-022-19818-3. ; Egbueri JC, Agbasi JC (2022b) Data-driven soft computing modeling of groundwater quality parameters in southeast Nigeria: comparing the performances of different algorithms. Environ Sci Pollut Res 29(25):38346–38373. https://doi.org/10.1007/s11356-022-18520-8. (PMID: 10.1007/s11356-022-18520-8) ; Egbueri JC, Agbasi JC (2022c) Performances of MLR, RBF-NN, and MLP-NN in the evaluation and prediction of water resources quality for irrigation purposes under two modeling scenarios. Geocarto Int. https://doi.org/10.1080/10106049.2022.2087758. ; Egbueri JC, Ayejoto DA, Agbasi JC (2022b) Pollution assessment and estimation of the percentages of toxic elements to be removed to make polluted drinking water safe: a case from Nigeria. Toxin Rev. https://doi.org/10.1080/15569543.2021.2025401. ; Egbueri JC, Unigwe CO, Agbasi JC, Nwazelibe VE (2022a) Indexical and artificial neural network modeling of the quality, corrosiveness, and encrustation potential of groundwater resources in industrialized metropolises, Southeast Nigeria. Environ Dev Sustain. https://doi.org/10.1007/s10668-022-02687-8. ; Egger R (2022) Machine learning in tourism: a brief overview. In: Applied data science in tourism: Interdisciplinary approaches, methodologies, and applications. Springer, pp 85–107. (PMID: 10.1007/978-3-030-88389-8_6) ; Ehteshami M, Farahani ND, Tavassoli S (2016) Simulation of nitrate contamination in groundwater using artificial neural networks. Mod Earth Sys Environ 2:28. https://doi.org/10.1007/s40808-016-0080-3. ; Eid EM, Dakhil MA, Hassan LM, Salama SG, Galal TM (2021) Uptake prediction of eight potentially toxic elements by Pistia stratiotes L. grown in the Al-Sero Drain (South Nile Delta, Egypt): A biomonitoring approach. Sustainability 13(9):5276. (PMID: 10.3390/su13095276) ; El Mrabet MA, El Makkaoui K, Faize A (2021) Supervised machine learning: a survey. In: In 2021 4th International Conference on Advanced Communication Technologies and Networking (CommNet). IEEE, pp 1–10. ; El-Safa MMA, Elsayed S, Elsherbiny O, Elmetwalli AH, Gad M, Moghanm FS, Eid EM, Taher MA, El-Morsy MH, Osman HE, Saleh AH (2022) Environmental assessment of potentially toxic elements using pollution indices and data-driven modeling in surface sediment of the Littoral Shelf of the Mediterranean Sea Coast and Gamasa Estuary, Egypt. J Marine Sci Eng 10(6):816. (PMID: 10.3390/jmse10060816) ; El-Shafie A, Abdin AE, Noureldin A, Taha MR (2009) Enhancing inflow forecasting model at Aswan high dam utilizing radial basis neural network and upstream monitoring stations measurements. Water Resour Manag 23(11):2289–2315. (PMID: 10.1007/s11269-008-9382-1) ; Elzain HEEO (2020) Improvement of groundwater contamination vulnerability assessment using the adaptive neuro-fuzzy inference system with metaheuristic optimization algorithms. Ph.D. thesis, Pukyong Nat’l Univ., Korea, p 120. ; Emenike PC, Nnaji CC, Tenebe IT, Agunwamba JC (2019) Hydrogeochemical imprints and spatio-temporal health risk assessment of lead in drinking water sources of Abeokuta, south-western Nigeria. Int J Environ Sci Technol. https://doi.org/10.1007/s13762-019-02506-0. ; Ewuzie U, Bolade OP, Egbedina AO (2022) Application of deep learning and machine learning methods in water quality modeling and prediction: a review. In: Current Trends and Advances in Computer-Aided Intelligent Environmental Data Engineering. Elsevier, pp 185–218. (PMID: 10.1016/B978-0-323-85597-6.00020-3) ; Farmaki EG, Thomaidis NS, Efstathiou CE (2010) Artificial neural networks in water analysis: theory and applications. Int J Environ Anal Chem 90(2):85–105. https://doi.org/10.1080/03067310903094511. (PMID: 10.1080/03067310903094511) ; Franzen M, Kloetzer L, Ponti M, Trojan J, Vicens J (2021) Machine learning in citizen science: promises and implications. The Sci Citizen Sci 183. ; Fural Ş, Kükrer S, Cürebal İ, Aykır D (2022) Ecological degradation and non-carcinogenic health risks of potential toxic elements: a GIS-based spatial analysis for Doğancı Dam (Turkey). Environ Monit Assess 194(4):1–18. (PMID: 10.1007/s10661-022-09870-4) ; Geetha R, Balasubramanian M, Devi KR (2022) COVIDetection: deep convolutional neural networks-based automatic detection of COVID-19 with chest x-ray images. Res Biomed Eng 38(3):955–964. (PMID: 10.1007/s42600-022-00230-2) ; Genchi G, Carocci A, Lauria G, Sinicropi MS, Catalano A (2020) Nickel: human health and environmental toxicology. Int J Environ Res Public Health 17:679. (PMID: 10.3390/ijerph17030679) ; Ghadimi F (2015) Prediction of heavy metals contamination in the groundwater of Arak region using artificial neural network and multiple linear regression. J Tethys 3(3):203–215. ; Gholami R, Kamkar-Rouhani A, Doulati Ardejani F, Maleki S (2011) Prediction of toxic metals concentration using artificial intelligence techniques. Appl Water Sci 1(3):125–134. (PMID: 10.1007/s13201-011-0016-z) ; Gunjal BL (2022) Real-world applications of data science. Data Science: Techniques and Intelligent Applications, p 33. ; Haji SH, Abdulazeez AM (2021) Comparison of optimization techniques based on gradient descent algorithm: a review. PalArch’s J Archaeol Egypt/Egyptol 18(4):2715–2743. ; Han S, Wang H, Tian Y, Christov N (2019) Time-delay estimation based computed torque control with robust adaptive RBF neural network compensator for a rehabilitation exoskeleton. ISA Trans 97:171–181. https://doi.org/10.1016/j.isatra.2019.07.030. (PMID: 10.1016/j.isatra.2019.07.030) ; Han H, Trimi S (2022) Towards a data science platform for improving SME collaboration through Industry 4.0 technologies. Technol Forecast Soc Chang 174:121242. (PMID: 10.1016/j.techfore.2021.121242) ; Hasan N, Alam M (2021) Applications of big data analytics in supply-chain management. In: Big Data Analytics. Auerbach Publications, pp 173–199. (PMID: 10.1201/9781003175711-10) ; Ighalo JO, Adeniyi AG, Adeniran JA, Ogunniyi S (2021) A systematic literature analysis of the nature and regional distribution of water pollution sources in Nigeria. J Clean Prod 283:124566. (PMID: 10.1016/j.jclepro.2020.124566) ; Illinois Department of Public Health (IDPH). 2010. Iron in drinking water. http://www.idph.state.il.us/envhealth/factsheets/ironFS.htm . Accessed 11th July 2022. ; Jebastina N, Prince Arulraj G (2018) Spatial prediction of nitrate concentration using GIS and ANFIS modelling in groundwater. Bull Environ Contam Toxicol 101(3):403–409. https://doi.org/10.1007/s00128-018-2406-5. (PMID: 10.1007/s00128-018-2406-5) ; Jianfei C, Chunfang L, Lixia Z, Quanyuan W, Jianshu L (2020) Source apportionment of potentially toxic elements in soils using APCS/MLR, PMF and geostatistics in a typical industrial and mining city in Eastern China. PLoS One 15(9). https://doi.org/10.1371/journal.pone.0238513. ; Jiang Y, Li C, Sun L, Guo D, Zhang Y, Wang W (2021) A deep learning algorithm for multi-source data fusion to predict water quality of urban sewer networks. J Clean Prod 318:128533. (PMID: 10.1016/j.jclepro.2021.128533) ; Jorjani E, Chehreh Chelgani S, Mesroghli SH (2008) Application of artificial neural networks to predict chemical desulfurization of Tabas coal. Fuel 87:2727–2734. (PMID: 10.1016/j.fuel.2008.01.029) ; Kanj F, Sawaya R, Halwani J, Nehmeh N (2022) Mercury prediction in groundwater of Naameh landfill using an artificial neural network (ANN) model. Green Technol Resil Sustain 2(1):1–14. ; Karaca Y, Baleanu D (2022) Artificial neural network modeling of systems biology datasets fit based on Mittag-Leffler functions with heavy-tailed distributions for diagnostic and predictive precision medicine. In: Multi-Chaos, Fractal and Multi-fractional Artificial Intelligence of Different Complex Systems. Academic Press, pp 133–148. (PMID: 10.1016/B978-0-323-90032-4.00020-1) ; Karmakar B, Singh MK, Choudhary BK, Singh SK, Egbueri JC, Gautam S, Rawat SK (2021) Investigation of the hydrogeochemistry, groundwater quality and associated health risks in industrialized regions of Tripura, northeast In-dia. Environ Forensic. https://doi.org/10.1080/15275922.2021.2006363. ; Khalil A, Almasri MN, McKee M, Kaluarachchi JJ (2005) Applicability of statistical learning algorithms in groundwater quality modeling. Water Resour Res 41(5). ; Khan U, Cook FJ, Laugesen R, Hasan MM, Plastow K, Amirthanathan GE, Bari MA, Tuteja NK (2020) Development of catchment water quality models within a realtime status and forecast system for the Great Barrier Reef. Environ Model Softw 132:104790. https://doi.org/10.1016/j.envsoft.2020.104790. (PMID: 10.1016/j.envsoft.2020.104790) ; Kouadri S, Pande CB, Panneerselvam B, Moharir KN, Elbeltagi A (2022) Prediction of irrigation groundwater quality parameters using ANN, LSTM, and MLR models. Environ Sci Pollut Res 29(14):21067–21091. (PMID: 10.1007/s11356-021-17084-3) ; Kumar B (2015) An encyclopedic overview of ‘big data’analytics. Int J Appl Eng Res 10(3):5681–5705. ; Landín M, Rowe RC, York P (2009) Advantages of neurofuzzy logic against conventional experimental design and statistical analysis in studying and developing direct compression formulations. Eur J Pharm Sci 38(4):325–331. (PMID: 10.1016/j.ejps.2009.08.004) ; Lee C, Jung DE, Lee D, Kim KH, Do SL (2021) Prediction performance analysis of artificial neural network model by input variable combination for residential heating loads. Energies 14(3):756. (PMID: 10.3390/en14030756) ; Leonard KC, Hasan F, Sneddon HF, You F (2021) Can artificial intelligence and machine learning be used to accelerate sustainable chemistry and engineering? ACS Sustain Chem Eng 9(18):6126–6129. (PMID: 10.1021/acssuschemeng.1c02741) ; Li P, Hua P, Gui D, Niu J, Pei P, Zhang J, Krebs P (2020) A comparative analysis of artificial neural networks and wavelet hybrid approaches to long-term toxic heavy metal prediction. Sci Rep 10(1):1–15. ; Lu H, Li H, Liu T, Fan Y, Yuan Y, Xie M, Qian X (2019) Simulating heavy metal concentrations in an aquatic environment using artificial intelligence models and physicochemical indexes. Sci Total Environ 694:133591. https://doi.org/10.1016/j.scitotenv.2019.133591. (PMID: 10.1016/j.scitotenv.2019.133591) ; Maier HR, Dandy GC (2000) Neural Networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications. Environ Model Softw 15:101–124. (PMID: 10.1016/S1364-8152(99)00007-9) ; May RJ, Dandy GC, Maier HR, Nixon JB (2008) Application of partial mutual information variable selection to ANN forecasting of water quality in water distribution systems. Environ Model Softw 23(10-11):1289–1299. (PMID: 10.1016/j.envsoft.2008.03.008) ; McCarthy J, Minsky ML, Rochester N (1955) A proposal for the Dartmouth Summer Research Project on artificial intelligence. IBM Corporation, C.E, Shannon, Bell telephone Lab. ; McCulloch W, Pitts W (1943) A logical calculus of ideas immanent in nervous activity. Bull Math Biophys 5:115–133. (PMID: 10.1007/BF02478259) ; Mgbenu CN, Egbueri JC (2019) The hydrogeochemical signatures, quality indices and health risk assessment of water resources in Umunya district, southeast Nigeria. Appl Water Sci 9(1):22. https://doi.org/10.1007/s13201-019-0900-5. (PMID: 10.1007/s13201-019-0900-5) ; Mhlongo S, Mativenga PT, Marnewick A (2018) Water quality in a mining and water-stressed region. J Clean Prod 171:446–456. https://doi.org/10.1016/j.jclepro.2017.10.030. (PMID: 10.1016/j.jclepro.2017.10.030) ; Moghanm FS, El-Banna A, El-Esawi MA, Abdel-Daim MM, Mosa A, Abdelaal KA (2020) Genotoxic and anatomical deteriorations associated with potentially toxic elements accumulation in water hyacinth grown in drainage water resources. Sustainability 12(5):2147. (PMID: 10.3390/su12052147) ; Mousavi SF, Amiri MJ (2012) Modelling nitrate concentration of groundwater using adaptive neural-based fuzzy inference system. Soil Water Res 7(2):73–83. (PMID: 10.17221/46/2010-SWR) ; Nazar S, Yang J, Amin MN, Khan K, Ashraf M, Aslam F, Javed MF, Eldin SM (2023) Machine learning interpretable-prediction models to evaluate the slump and strength of fly ash-based geopolymer. J Mater Res Technol 24:100–124. https://doi.org/10.1016/j.jmrt.2023.02.180. (PMID: 10.1016/j.jmrt.2023.02.180) ; Nicklow J, Reed P, Savic D, Dessalegne T, Harrell L, Chan-Hilton A, Karamouz M, Minsker B, Ostfeld A, Singh A, Zechman E (2010) State of the art for genetic algorithms and beyond in water resources planning and management. J Water Resour Plan Manag 136(4):412–432. (PMID: 10.1061/(ASCE)WR.1943-5452.0000053) ; Nourani V, Ejlali RG, Alami MT (2011) Spatiotemporal groundwater level forecasting in coastal aquifers by hybrid artificial neural network-geostatistics model: a case study. Environ Eng Sci 28(3):217–228. (PMID: 10.1089/ees.2010.0174) ; Olazaran M (1996) A sociological study of the official history of the perceptron’s controversy. Soc Stud Sci 26(3):611–659. (PMID: 10.1177/030631296026003005) ; Omeka ME, Egbueri JC (2022) Hydrogeochemical assessment and health related risks due to toxic element ingestion and dermal contact within the Nnewi-Awka urban areas, Nigeria. Environ Geochem Health. https://doi.org/10.1007/s10653-022-01332-7. ; Pande CB, Egbueri JC, Costache R, Sidek LM, Wang Q, Alshehri F, Din NM, Gautam VK, Pal SC (2024) Predictive modeling of land surface temperature (LST) based on Landsat-8 satellite data and machine learning models for sustainable development. J Clean Prod 444:141035. https://doi.org/10.1016/j.jclepro.2024.141035. (PMID: 10.1016/j.jclepro.2024.141035) ; Powell MJD (1987) Radial basis functions for multivariable interpolation: a review. In: Mason JC, Cox MG (eds) Proc., IMA Conf. on Algorithms for Approximation. Oxford University Press, New York, pp 143–167. ; Praveena N, Vivekanandan K (2021) A review on deep neural network design and their applications. In: In 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), vol 1. IEEE, pp 1495–1501. ; Purkait B, Kadam SS, Das SK (2008) Application of artificial neural network model to study arsenic contamination in groundwater of Malda District, Eastern India. J Environ Inf 12(2). ; Rajaee T, Khani S, Ravansalar M (2020) Artificial intelligence-based single and hybrid models for prediction of water quality in rivers: a review. Chemom Intell Lab Syst 200:103978. (PMID: 10.1016/j.chemolab.2020.103978) ; Rashid A, Ayub M, Khan S, Ullah Z, Ali L, Gao X, Li C, El-Serehy H, Kaushik P, Rasool A (2022) Hydrogeochemical assessment of carcinogenic and non-carcinogenic health risks of potentially toxic elements in aquifers of the Hindukush ranges, Pakistan: insights from groundwater pollution indexing, GIS-based, and multivariate statistical approaches. Environ Sci Pollut Res 29(50):75744–75768. (PMID: 10.1007/s11356-022-21172-3) ; Ricolfi L, Barbieri M, Muteto PV, Nigro A, Sappa G, Vitale S (2020) Potential toxic elements in groundwater and their health risk assessment in drinking water of Limpopo National Park, Gaza Province, Southern Mozambique. Environ Geochem Health 42(9):2733–2745. (PMID: 10.1007/s10653-019-00507-z) ; Rooki R, Doulati Ardejani F, Aryafar A, Bani Asadi A (2011) Prediction of heavy metals in acid mine drainage using artificial neural network from the Shur River of the Sarcheshmeh porphyry copper mine, Southeast Iran. Environ Earth Sci 64(5):1303–1316. (PMID: 10.1007/s12665-011-0948-5) ; Ruder, S. (2016). An overview of gradient descent optimization algorithms. https://arxiv.org/abs/1609.04747 . ; Sabharwal R, Miah SJ (2021) A new theoretical understanding of big data analytics capabilities in organizations: a thematic analysis. J Big Data 8(1):1–17. (PMID: 10.1186/s40537-021-00543-6) ; Saleh AH, Elsayed S, Gad M, Elmetwalli AH, Elsherbiny O, Hussein H, Moghanm FS, Qazaq AS, Eid EM, El-Kholy AS, Taher MA (2022) Utilization of pollution indices, hyperspectral reflectance indices, and data-driven multivariate modelling to assess the bottom sediment quality of Lake Qaroun, Egypt. Water 14(6):890. (PMID: 10.3390/w14060890) ; Sara U, Akter M, Uddin MS (2019) Image quality assessment through FSIM, SSIM, MSE and PSNR—a comparative study. J Comput Commun 7(3):8–18. https://doi.org/10.4236/jcc.2019.73002. (PMID: 10.4236/jcc.2019.73002) ; Sarker IH (2021) Data science and analytics: an overview from data-driven smart computing, decision-making and applications perspective. SN Comput Sci 2(5):1–22. (PMID: 10.1007/s42979-021-00765-8) ; Shah SIH, Peristeras V, Magnisalis I (2021) Government big data ecosystem: definitions, types of data, actors, and roles and the impact in public administrations. ACM J Data Inform Qual 13(2):1–25. (PMID: 10.1145/3425709) ; Shahin MA, Jaksa MB, Maier HR (2001) Artificial neural network applications in geotechnical engineering. Aust Geomech 36(1):49–62. ; Shakeri AA, Gholamalizadeh AA, Soltani J (2013) Artificial neural network (ANN) approach for predicting Cu concentration in drinking water of Chahnimeh1 reservoir in Sistan-Balochistan. Iran Health Scope 2(1):31–38. (PMID: 10.17795/jhealthscope-9828) ; Shi Y (2022) Advances in big data analytics: theory, algorithms and practices. Springer Nature. (PMID: 10.1007/978-981-16-3607-3) ; Singh KP, Basant A, Malik A, Jain G (2009) Artificial neural network modeling of the river water quality—a case study. Ecol Model 220(6):888–895. (PMID: 10.1016/j.ecolmodel.2009.01.004) ; Singha S, Pasupuleti S, Singha SS, Singh R, Kumar S (2021) Prediction of groundwater quality using efficient machine learning technique. Chemosphere 276:130265. (PMID: 10.1016/j.chemosphere.2021.130265) ; Sit M, Demiray BZ, Xiang Z, Ewing GJ, Sermet Y, Demir I (2020) A comprehensive review of deep learning applications in hydrology and water resources. Water Sci Technol 82(12):2635–2670. (PMID: 10.2166/wst.2020.369) ; Sonmez AY, Kale S, Ozdemir RC, Kadak AE (2018) An adaptive neuro-fuzzy inference system (ANFIS) to predict of cadmium (Cd) concentrations in the Filyos River, Turkey. Turk J Fish Aquat Sci 18(12):1333–1343. https://doi.org/10.4194/1303-2712-v18_12_01. (PMID: 10.4194/1303-2712-v18_12_01) ; Suen JP, Eheart JW (2003) Evaluation of neural networks for modeling nitrate concentrations in rivers. J Water Resour Plan Manag 129(6):505–510. (PMID: 10.1061/(ASCE)0733-9496(2003)129:6(505)) ; Team, G. L. (2020). Types of neural networks and definition of neural network. https://www.mygreatlearning.com/blog/types-of-neural-networks/ . Accessed 11 July 2022. ; Than NH, Ly CD, Van Tat P (2021) The performance of classification and forecasting Dong Nai River water quality for sustainable water resources management using neural network techniques. J Hydrol 596:126099. (PMID: 10.1016/j.jhydrol.2021.126099) ; Ucun Ozel H, Gemici BT, Gemici E, Ozel HB, Cetin M, Sevik H (2020) Application of artificial neural networks to predict the heavy metal contamination in the Bartin River. Environ Sci Pollut Res 27(34):42495–42512. https://doi.org/10.1007/s11356-020-10156-w. (PMID: 10.1007/s11356-020-10156-w) ; Ukah BU, Egbueri JC, Unigwe CO, Ubido OE (2019) Extent of heavy metals pollution and health risk assessment of groundwater in a densely populated industrial area, Lagos, Nigeria. Int J Energy Water Resour 3(4):291–303. https://doi.org/10.1007/s42108-019-00039-3. (PMID: 10.1007/s42108-019-00039-3) ; Unigwe CO, Egbueri JC, Omeka ME (2022) Geospatial and statistical approaches to nitrate health risk and groundwater quality assessment of an alluvial aquifer in SE Nigeria for drinking and irrigation purposes. J Indian Chem Soc 99:100479. https://doi.org/10.1016/j.jics.2022.100479. (PMID: 10.1016/j.jics.2022.100479) ; Usman J, Salami BA, Gbadamosi A, Adamu H, Usman AG, Benaafi M, Abba SI, Othman MHD, I. H., & Aljundi, I. H. (2023) Intelligent optimization for modelling superhydrophobic ceramic membrane oil flux and oil-water separation efficiency: evidence from wastewater treatment and experimental laboratory. Chemosphere 331:138726. https://doi.org/10.1016/j.chemosphere.2023.138726. (PMID: 10.1016/j.chemosphere.2023.138726) ; Valente T, Ferreira MJ, Grande JA, de la Torre ML, Borrego J (2013) pH, electric conductivity and sulfate as base parameters to estimate the concentration of metals in AMD using a fuzzy inference system. J Geochem Explor 124:22–28. https://doi.org/10.1016/j.gexplo.2012.07.013. (PMID: 10.1016/j.gexplo.2012.07.013) ; Venkatramanan S, Chung SY, Selvam S, Son JH, Kim YJ (2017) Interrelationship between geochemical elements of sediment and groundwater at Samrak Park Delta of Nakdong River Basin in Korea: multivariate statistical analyses and artificial neural network approaches. Environ Earth Sci 76(13):1–12. https://doi.org/10.1007/S12665-017-6795-2. (PMID: 10.1007/S12665-017-6795-2) ; Wagh V, Panaskar D, Muley A, Mukate S, Gaikwad S (2018) Neural network modelling for nitrate concentration in groundwater of Kadava River basin, Nashik, Maharashtra, India. Groundw Sustain Dev 7:436–445. https://doi.org/10.1016/j.gsd.2017.12.012. (PMID: 10.1016/j.gsd.2017.12.012) ; Wagh VM, Panaskar DB, Muley AA (2017) Estimation of nitrate concentration in groundwater of Kadava river basin-Nashik district, Maharashtra, India by using artificial neural network model. Model Earth Syst Environ 3:36. https://doi.org/10.1007/s40808-017-0290-3. (PMID: 10.1007/s40808-017-0290-3) ; Wan D, Zhan C, Yang G, Liu X, Yang J (2016) Preliminary assessment of health risks of potentially toxic elements in settled dust over Beijing urban area. Int J Environ Res Public Health 13(5):491. (PMID: 10.3390/ijerph13050491) ; Yasmin M (2019) Big data analytics capabilities and firm performance: an MCDM approach. (Master's thesis, İbn Haldun Üniversitesi, Lisansüstü Eğitim Enstitüsü). ; Yassin MA, Tawabini B, Al-Shaibani A, Adetoro JA, Benaafi M, Al-Areeq AM, Usman AG, Abba SI (2022) Geochemical and spatial distribution of topsoil HMs coupled with modeling of Cr using chemometrics intelligent techniques: case study from Dammam Area, Saudi Arabia. Molecules 27(13):4220. https://doi.org/10.3390/molecules27134220. (PMID: 10.3390/molecules27134220) ; Yassin MA, Usman AG, Abba SI, Ozsahin DU, Aljundi IH (2023) Intelligent learning algorithms integrated with feature engineering for sustainable groundwater salinization modelling: Eastern Province of Saudi Arabia. Res Eng 20:101434. https://doi.org/10.1016/j.rineng.2023.101434. (PMID: 10.1016/j.rineng.2023.101434) ; Yesilnacar MI, Sahinkaya E, Naz M, Ozkaya B (2007) Neural network prediction of nitrate in groundwater of Harran Plain, Turkey. Environ Geol 56(1):19–25. https://doi.org/10.1007/S00254-007-1136-5. (PMID: 10.1007/S00254-007-1136-5) ; Zaqoot HA, Hamada M, Miqdad S (2018) A comparative study of Ann for predicting nitrate concentration in groundwater wells in the southern area of Gaza Strip. Appl Artif Intell 32(7-8):727–744. https://doi.org/10.1080/08839514.2018.1506970. (PMID: 10.1080/08839514.2018.1506970) ; Zaqoot H, Aish A, Abdeljawad S (2017) Application of artificial neural networks for predicting water quality. LAMBERT Academic Publishing. ; Zare A, Bayat V, Daneshkare A (2011) Forecasting nitrate concentration in groundwater using artificial neural network and linear regression models. Int Agrophys 25(2). ; Zhang L, Yang Z, Peng M, Cheng X (2022) Contamination levels and the ecological and human health risks of potentially toxic elements (PTEs) in soil of Baoshan Area, Southwest China. Appl Sci 12(3):1693. (PMID: 10.3390/app12031693) ; Zhang W, Wu C, Li Y, Wang L, Samui P (2021) Assessment of pile drivability using random forest regression and multivariate adaptive regression splines. Georisk 15(1):27–40. ; Zhang W, Zhang R, Wu C, Goh ATC, Lacasse S, Liu Z, Liu H (2020) State-of-the-art review of soft computing applications in underground excavations. Geosci Front 11(4):1095–1106. (PMID: 10.1016/j.gsf.2019.12.003) ; Zounemat-Kermani M, Batelaan O, Fadaee M, Hinkelmann R (2021) Ensemble machine learning paradigms in hydrology: a review. J Hydrol 598:126266. (PMID: 10.1016/j.jhydrol.2021.126266) ; Zur RM, Jiang Y, Pesce LL, Drukker K (2009) Noise injection for training artificial neural networks: a comparison with weight decay and early stopping. Med Phys 36(10):4810–4818. (PMID: 10.1118/1.3213517)
- Contributed Indexing: Keywords: Artificial neural networks; Input variable selection; Input variables; PTE prediction modeling; Soft computing models
- Entry Date(s): Date Created: 20240419 Date Completed: 20240516 Latest Revision: 20240516
- Update Code: 20240516
|
