State-of-art implementation of computing intelligent models for water demand modelling : A decade review and future direction
DOI:
https://doi.org/10.24425/jwld.2025.155316Abstract
The main insight from this research is that there has been significant progress in the development of computer-aided models for water demand modelling over the past two decades. These models are used to balance water demand and supply, which is critical for effective water supply management systems. The equilibrium is achieved through various measures, many of which involve the use of forecasting tools. Recent research on urban water demand forecasting using artificial intelligence (AI) models is discussed in this article, to present the ‘state of the art’ on the issue and provide some insights and suggestions for future research on methodologies and models. The review examines models developed using traditional statistical methods, including artificial neural networks, linear regression, and time-series analysis, as well as soft computing techniques. This paper demonstrates that the study is focused on a decade-long evaluation of operating system management, indicating an opportunity for long-term projections. It goes without saying that no single model outperforms all the others; however, it is vital to assess the strengths of each model or combination of models for each country or region to determine which model works best in that location. Although the usage of AI and machine learning (ML) has increased significantly in recent decades, there is still potential for development in the field of water demand forecasting.References
Abba, S.I. et al. (2020a) “Emerging evolutionary algorithm integrated with kernel principal component analysis for modeling the performance of a water treatment plant,” Journal of Water Process Engineering, 33, 101081. Available at: https://doi.org/10.1016/j.jwpe.2019.101081.
Abba, S.I. et al. (2020b) “Hybrid machine learning ensemble techniques for modeling dissolved oxygen concentration,” IEEE Access, 8, pp. 157218–157237. Available at: https://doi.org/10.1109/ACCESS.2020.3017743.
Abba, S.I. et al. (2020c) “Implementation of data intelligence models coupled with ensemble machine learning for prediction of water quality index,” Environmental Science and Pollution Research, 27. Available at: https://doi.org/10.1007/s11356-020-09689-x.
Abba, S.I. et al. (2021) “Effluents quality prediction by using nonlinear dynamic block-oriented models: A system identification approach,” Desalination and Water Treatment, 218, pp. 52–62. Available at: https://doi.org/10.5004/dwt.2021.26983.
Abba, S.I. et al. (2022) “Inverse groundwater salinization modeling in a sandstone’s aquifer using stand-alone models with an improved non-linear ensemble machine learning technique,” Journal of King Saud University – Computer and Information Sciences, 34(10, A). Available at: https://doi.org/10.1016/j.jksuci.2022.08.002.
Abba, S.I., Benaafi, M. and Aljundi, I.H. (2023) “Intelligent process optimisation based on cutting-edge emotional learning for performance evaluation of NF/RO of seawater desalination plant,” Desalination, 550, 116376. Available at: https://doi.org/10.1016/j.desal.2023.116376.
Abba, S.I., Elkiran, G. and Nourani, V. (2020) “Non-linear ensemble modeling for multi-step ahead prediction of treated COD in wastewater treatment plant,” Advances in Intelligent Systems and Computing, 1095, pp. 683–689. Available at: https://doi.org/10.1007/978-3-030-35249-3_88.
Abba, S.I., Nourani, V. and Elkiran, G. (2019) “Multi-parametric modeling of water treatment plant using AI-based non-linear ensemble,” Journal of Water Supply: Research and Technology – Aqua, 68(7), pp. 547–561. Available at: https://doi.org/10.2166/aqua.2019.078.
Abba, S.I., Usman, A.G. and Işik, S. (2020) “Simulation for response surface in the HPLC optimization method development using artificial intelligence models: A data-driven approach,” Chemometrics and Intelligent Laboratory Systems, 201, 104007. Available at: https://doi.org/10.1016/j.chemolab.2020.104007.
Adamowski, J. et al. (2012) “Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada,” Water Resources Research, 48(1), pp. 1–14. Available at: https://doi.org/10.1029/2010WR009945.
Adamowski, J. and Karapataki, C. (2010) “Comparison of multivariate regression and artificial neural networks for peak urban water-demand forecasting: Evaluation of different ANN learning algorithms,” Journal of Hydrologic Engineering, 15(10), pp. 729– 743. Available at: https://doi.org/10.1061/(asce)he.1943-5584.0000245.
Abdullahi, H.U., Usman, A.G. and Abba, S.I. (2020) “Modelling the absorbance of a bioactive compound in HPLC method using artificial neural network and multilinear regression methods,” Dutse Journal of Pure and Applied Sciences, 6(2), pp. 362–371.
Ajbar, A.H. and Ali, E. (2012) “Water demand prediction for touristic Mecca City in Saudi Arabia using neural networks,” World Academy of Science, Engineering and Technology, 65(5), pp. 36–40.
Aliyu, D.S. et al. (2021) “Prediction of compressive strength of lightweight concrete made with partially replaced cement by animal bone ash using artificial neural network,” 2021 1st International Conference on Multidisciplinary Engineering and Applied Science, ICMEAS 2021. Available at: https://doi.org/10.1109/ICMEAS52683.2021.9692317.
Altunkaynak, A., Özger, M. and Çakmakci, M. (2005) “Water consumption prediction of Istanbul City by using fuzzy logic approach,” Water Resources Management, 19(5), pp. 641–654. Available at: https://doi.org/10.1007/s11269-005-7371-1.
Anele, A.O. et al. (2017) “Overview, comparative assessment and recommendations of forecasting models for short-term water demand prediction,” Water, 9(11), 887. Available at: https://doi.org/10.3390/w9110887.
Arandia, E. et al. (2016) “Tailoring seasonal time series models to forecast short-term water demand,” Journal of Water Resources Planning and Management, 142(3). Available at: https://doi.org/10.1061/(asce)wr.1943-5452.0000591.
Baghban, A. and Ebadi, T. (2018) “ANFIS modeling of higher heating value of wastes: Application to fuel upgrading,” Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, pp. 1–7. Available at: https://doi.org/10.1080/15567036.2017.1344746.
Bakker, M. et al. (2014) “Improving the performance of water demand forecasting models by using weather input,” Procedia Engineering, 70, pp. 93–102. Available at: https://doi.org/10.1016/j.proeng.014.02.012.
Benaafi, M. et al. (2022) “Neurocomputing modelling of hydrochemical and physical properties of groundwater coupled with spatial clustering, GIS, and statistical techniques,” Sustainability, 14(4), 2250. Available at: https://doi.org/10.3390/su14042250.
Bennett, C., Stewart, R.A. and Beal, C.D. (2013) “ANN-based residential water end-use demand forecasting model,” Expert Systems with Applications, 40(4), pp. 1014–1023. Available at: https://doi.org/10.1016/j.eswa.2012.08.012.
Boubaker, S. (2017) “Identification of monthly municipal water demand system based on autoregressive integrated moving average model tuned by particle swarm optimization,” Journal of Hydroinformatics, 19(2), pp. 261–281. Available at: https://doi.org/10.2166/hydro.2017.035.
Breiman, L. (1996) “Bagging predictors,” Machine Learning, 24, pp. 123–140. Available at: https://doi.org/10.1023/A:1018054314350.
Breiman, L. (2001) “Random forests,” Machine Learning, 45, pp. 5–32. Available at: https://doi.org/10.1023/A:1010933404324.
Brentan, B.M. et al. (2017) “Correlation analysis of water demand and predictive variables for short-term forecasting models,” Mathematical Problems in Engineering, 2017(1). Available at: https://doi.org/10.1155/2017/6343625.
Campisi-Pinto, S., Adamowski, J. and Oron, G. (2013) “Erratum to: Forecasting urban water demand via wavelet-denoising and neural network models. Case study: City of Syracuse, Italy,” Water Resources Management, 27(1), pp. 319–321. Available at: https://doi.org/10.1007/s11269-012-0122-1.
Dashti, A. et al. (2019) “Estimation of biomass higher heating value (HHV) based on the proximate analysis: Smart modeling and correlation,” Fuel, 257, pp. 1–11. Available at: https://doi.org/10.1016/j.fuel.2019.115931.
Di, C. and Yang, X. (2014) “Modeling and dynamical analysis of the water resources supply-demand system: A case study in Haihe river basin,” Journal of Applied Mathematics, 171793. Available at: https://doi.org/10.1155/2014/171793.
Dietterich, T.G. (2000) “Ensemble methods in machine learning,” in J. Kittler and F. Roli (eds.) Multiple Classifier Systems, 1857. Berlin, Heidelberg: Springer. Available at: https://doi.org/10.1007/3-540-45014-9_1.
Donyaii, A., Sarraf, A. and Ahmadi, H. (2020) “A novel approach to supply the water reservoir demand based on a hybrid whale optimization algorithm,” Shock and Vibration, 8833866. Available at: https://doi.org/10.1155/2020/8833866.
Duarte Alonso, A. and Kok, S. (2018) “Adapting through learning and knowledge acquisition: the cases of four global family firms,” Journal of Family Business Management, 8(3), pp. 274–292.
Elkiran, G., Nourani, V. and Abba, S.I. (2019) “Multi-step ahead modelling of river water quality parameters using ensemble artificial intelligence-based approach,” Journal of Hydrology, 577, 123962. Available at: https://doi.org/10.1016/j.jhydrol.2019.123962.
Emamgholizadeh, S. et al. (2014) “Prediction of water quality parameters of Karoon River (Iran) by artificial intelligence-based models,” International Journal of Environmental Science and Technology, 11(3), pp. 645–656. Available at: https://doi.org/10.1007/s13762-013-0378-x.
Felfelani, F. and Kerachian, R. (2016) “Municipal water demand forecasting under peculiar fluctuations in population: A case study of Mashhad, a tourist city,” Hydrological Sciences Journal, 61(8), pp. 1524–1534. Available at: https://doi.org/10.1080/02626667.2015.1027208.
Feng, Q. et al. (2015) “Proximate analysis based prediction of gross calorific value of coals: A comparison of support vector machine, alternating conditional expectation and artificial neural network,” Fuel Processing Technology, 129, pp. 120–129. Available at: https://doi.org/10.1016/j.fuproc.2014.09.001.
Freund, Y. and Schapire, R.E. (1996) “Experiments with a new boosting algorithm,” Machine Learning: Proceedings of the Thirteenth International Conference, pp. 148–156. Available at: https://dl.acm.org/doi/10.5555/3091696.3091715 (Accessed: February 10, 2022).
Fullerton, T.M., Ceballos, A. and Walke, A.G. (2016) “Short-term forecasting analysis for municipal water demand,” Journal – American Water Works Association, 108(1), pp. E27–E38. Available at: https://doi.org/10.5942/jawwa.2016.108.0003.
Gagliardi, F. et al. (2017a) “A comparison between pattern-based and neural network short-term water demand forecasting models,” Water Science and Technology: Water Supply, 17(5), pp. 1426–1435. Available at: https://doi.org/10.2166/ws.2017.045.
Gagliardi, F. et al. (2017b) “A probabilistic short-termwater demand forecasting model based on the Markov chain,” Water, 9(7), pp. 7–14. Available at: https://doi.org/10.3390/w9070507.
Ghalehkhondabi, I. et al. (2017) “Water demand forecasting: Review of soft computing methods,” Environmental Monitoring and Assessment, 189, 313. Available at: https://doi.org/10.1007/s10661-017-6030-3.
Ghali, U.M. et al. (2020) “Advanced chromatographic technique for performance simulation of anti-Alzheimer agent: An ensemble machine learning approach,” SN Applied Sciences, 2(11). Available at: https://doi.org/10.1007/s42452-020-03690-2.
Ghiassi, M., Zimbra, D.K. and Saidane, H. (2008) “Urban water demand forecasting with a dynamic artificial neural network model,” Journal of Water Resources Planning and Management, 134(2), pp. 138–146. Available at: https://doi.org/10.1061/(ASCE)0733-9496(2008)134:2(138).
Guyon, I. and Elisseeff, A. (2003) “An introduction to variable and feature selection,” Journal of Machine Learning Research, 3, pp. 1157–1182.
Hadi, S.J. et al. (2019) “Non-linear input variable selection approach integrated with non-tuned data intelligence model for streamflow pattern simulation,” IEEE Access, 7, pp. 141533–141548. Available at: https://doi.org/10.1109/ACCESS.2019.2943515.
Hsu, H.H., Hsieh, C.W. and Lu, M.D. (2011) “Hybrid feature selection by combining filters and wrappers,” Expert Systems with Applications, 38(7), pp. 8144–8150. Available at: https://doi.org/10.1016/j.eswa.2010.12.156.
Ighalo, J.O., Adeniyi, A.G. and Marques, G. (2020) “Application of linear regression algorithm and stochastic gradient descent in a machine-learning environment for predicting biomass higher heating value,” Biofuels, Bioproducts and Biorefining, 14(6), pp. 1286–1295. Available at: https://doi.org/10.1002/bbb.2140.
Jia, J. and Hao, S. (2013) “Water demand forecasting based on adaptive extreme learning machine,” in T. Hu (ed.) Proceedings of the 2013 The International Conference on Artificial Intelligence and Software Engineering (ICAISE 2013), 37, pp. 42–45. Available at: https://doi.org/10.2991/icaise.2013.10.
Kofinas, D. et al. (2014) “Urban water demand forecasting for the island of Skiathos,” Procedia Engineering, 89, pp. 1023–1030. Available at: https://doi.org/10.1016/j.proeng.2014.11.220.
Li, W. and Huicheng, Z. (2010) “Urban water demand forecasting based on HP filter and fuzzy neural network,” Journal of Hydroinformatics, 12(2), pp. 172–184. Available at: https://doi.org/10.2166/hydro.2009.082.
Mahmoud, K. et al. (2021) “Prediction of the effects of environmental factors towards COVID-19 outbreak using AI-based models,” IAES International Journal of Artificial Intelligence, 10(1), pp. 35–42. Available at: https://doi.org/10.11591/ijai.v10.i1.pp35-42.
Malik, A. et al. (2021) “Prediction of meteorological drought by using hybrid support vector regression optimized with HHO versus PSO algorithms,” Environmental Science and Pollution Research, 28(29), pp. 39139–39158. Available at: https://doi.org/10.1007/s11356-021-13445-0.
Maroufpoor, S. et al. (2022) “A novel hybridized neuro-fuzzy model with an optimal input combination for dissolved oxygen estimation,” Frontiers in Environmental Science, 10, pp. 1–17. Available at: https://doi.org/10.3389/fenvs.2022.929707.
Menapace, A., Zanfei, A. and Righetti, M. (2021) “Tuning ann hyperparameters for forecasting drinking water demand,” Applied Sciences (Switzerland), 11(9). Available at: https://doi.org/10.3390/app11094290.
Muhammad, A.U. and Abba, S.I. (2023) “Transfer learning for streamflow forecasting using unguaged MOPEX basins data set,” 16, pp. 1241–1264. Available at: https://doi.org/10.1007/s12145-023-00952-6.
Mohammadi, B. et al. (2020) “Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series,” Hydrological Sciences Journal, 65(10), pp. 1738–1751. Available at: https://doi.org/10.1080/02626667.2020.1758703.
Mohammed, J.R. and Ibrahim, H.M. (2012) “Hybrid wavelet artificial neural network model for municipal water demand forecasting,” ARPN Journal of Engineering and Applied Sciences, 7(8), pp. 1047–1065.
Mohammed, J.R. and Ibrahim, H.M. (2013) “Multilayer perceptron-multiactivation function artificial neural network model for municipal water demand forecasting,” ARPN Journal of Engineering and Applied Sciences, 8(12), pp. 1019–1038.
Nasseri, M., Moeini, A. and Tabesh, M. (2011) “Forecasting monthly urban water demand using Extended Kalman Filter and genetic programming,” Expert Systems with Applications, 38(6), pp. 7387–7395. Available at: https://doi.org/10.1016/j.eswa.2010.12.087.
Navarrete-Lòpez, C. et al. (2016) “Epidemiological approach to forecast water demand consumption through SAX,” in L. Jódar et al. (eds.) Modelling for Engineering & Human Behaviour. Valencia: Universitat Politècnica de València.
Nourani, V., Elkiran, G. and Abba, S.I. (2018) “Wastewater treatment plant performance analysis using artificial intelligence – An ensemble approach,” Water Science and Technology, 78(10), pp. 2064–2076. Available at: https://doi.org/10.2166/wst.2018.477.
Nourani, V., Elkiran, G. and Abdullahi, J. (2019) “Multi-station artificial intelligence based ensemble modeling of reference evapotranspiration using pan evaporation measurements,” Journal of Hydrology, 577, 123958. Available at: https://doi.org/10.1016/j.jhydrol.2019.123958.
Okeke, O.P. et al. (2022) “Performance analysis and control of wastewater treatment plant using adaptive neuro-fuzzy inference system (ANFIS) and multi-linear regression (MLR) techniques,” 04(02), pp. 1–16. Available at: https://doi.org/10.30574/gscaet.2022.4.2.0033.
Onifade, M. et al. (2022) “Prediction of gross calorific value of solid fuels from their proximate analysis using soft computing and regression analysis,” International Journal of Coal Preparation and Utilization, 42(4), pp. 1–15. Available at: https://doi.org/10.1080/19392699.2019.1695605.
Oyebode, O. (2019) “Evolutionary modelling of municipal water demand with multiple feature selection techniques,” Journal of Water Supply: Research and Technology – AQUA, 68(4), pp. 264–281. Available at: https://doi.org/10.2166/aqua.2019.145.
Oyebode, O. and Ighravwe, D.E. (2019) “Urban water demand forecasting: A comparative evaluation of conventional and soft computing techniques,” Resources, 8(3). Available at: https://doi.org/10.3390/resources8030156.
Oyesanmi, O. (2017) “An assessment of domestic water consumption pattern in Lokoja Metropolis, Kogi State,” International Journal of Social Sciences, 11(3), pp. 47–56.
Ozveren, U. (2016) “An artificial intelligence approach to predict a lower heating value of municipal solid waste,” Energy Sources, Part A: Recovery, Utilization and Environmental Effects, 38(19), pp. 2906–2913. Available at: https://doi.org/10.1080/15567036.2015.1107864.
Peña-Guzmán, C., Melgarejo, J. and Prats, D. (2016) “Forecasting water demand in residential, commercial, and industrial zones in Bogotá, Colombia, using least-squares support vector machines,” Mathematical Problems in Engineering, 2016. Available at: https://doi.org/10.1155/2016/5712347.
Peng, H., Wu, H. and Wang, J. (2020) “Research on the prediction of the water demand of construction engineering based on the BP neural network,” Advances in Civil Engineering, 2020, 8868817. Available at: https://doi.org/10.1155/2020/8868817.
Pham, Q.B. et al. (2019) “Potential of hybrid data-intelligence algorithms for multi-station modelling of rainfall,” Water Resources Management, 33, pp. 5067–5087. Available at: https://doi.org/10.1007/s11269-019-02408-3.
Pinto, F. et al. (2014) “Simulation of the ensemble generation process: The divergence between data and model similarity,” Modelling and Simulation 2014 – European Simulation and Modelling Conference, ESM 2014, pp. 103–107.
Piramuthu, S. (2004) “Evaluating feature selection methods for learning in data mining applications,” European Journal of Operational Research, 156(2), pp. 483–494. Available at: https://doi.org/10.1016/S0377-2217(02)00911-6.
Ponte, B. et al. (2015) “Real-time water demand forecasting system through an agent-based architecture,” International Journal of Bio-Inspired Computation, 7(3), pp. 147–156. Available at: https://doi.org/10.1504/IJBIC.2015.069559.
Ponte, B. et al. (2016) “Intelligent decision support system for real-time water demand management,” International Journal of Computational Intelligence Systems, 9(1), pp. 168–183. Available at: https://doi.org/10.1080/18756891.2016.1146533.
Rooney, N. et al. (2004) “Dynamic integration of regression models,” in F. Roli, J. Kittler and T. Windeatt (eds.) Lecture Notes in Computer Science, 3077, pp. 164–173. Berlin, Heidelberg: Springer. Available at: https://doi.org/10.1007/978-3-540-25966-4_16.
Seo, Y., Kwon, S. and Choi, Y. (2018) “Short-term water demand forecasting model combining variational mode decomposition and extreme learning machine,” Hydrology, 5(4). Available at: https://doi.org/10.3390/hydrology5040054.
Shirkoohi, M.G., Doghri, M. and Duchesne, S. (2021) “Short-term water demand predictions coupling an artificial neural network model and a genetic algorithm,” Water Supply, 21(5), pp. 2374–2386. Available at: https://doi.org/10.2166/ws.2021.049.
Souza Groppo de, G., Costa, M.A. and Libânio, M. (2019) “Predicting water demand: A review of the methods employed and future possibilities,” Water Science and Technology: Water Supply, 19(8), pp. 2179–2198. Available at: https://doi.org/10.2166/ws.2019.122.
Sunusi, M.A., Abba, S.I. and Iesha, R. (2016) “Smart cities in India: A theoretical approach,” 5(12), pp. 643–651.
Tawabini, B. et al. (2022) “Spatiotemporal variability assessment of trace metals based on subsurface water quality impact integrated with artificial intelligence-based modeling,” Sustainability, 14(4). Available at: https://doi.org/10.3390/su14042192.
Tiwari, M.K. and Adamowski, J. (2013) “Urban water demand forecasting and uncertainty assessment using ensemble wavelet-bootstrap-neural network models,” Water Resources Research, 49(10), pp. 6486–6507. Available at: https://doi.org/10.1002/wrcr.20517.
Tiwari, M.K. and Adamowski, J.F. (2015) “Medium-term urban water demand forecasting with limited data using an ensemble wavelet–bootstrap machine-learning approach,” Journal of Water Resources Planning and Management, 141(2), 04014053. Available at: https://doi.org/10.1061/(asce)wr.1943-5452.0000454.
Uzun, H. et al. (2017) “Improved prediction of higher heating value of biomass using an artificial neural network model based on proximate analysis,” Bioresource Technology, 234, pp. 122–130. Available at: https://doi.org/10.1016/j.biortech.2017.03.015.
Varahrami, V. (2013) “Short-term water demand forecasting by neural network,” International Journal of Management and Innovation, 5(2), pp. 1–10.
Vijai, P. and Bagavathi Sivakumar, P. (2018) “Performance comparison of techniques for water demand forecasting,” Procedia Computer Science, 143, pp. 258–266. Available at: https://doi.org/10.1016/j.procs.2018.10.394.
Webb, G.I. and Zheng, Z. (2004) “Multistrategy ensemble learning: Reducing error by combining ensemble learning techniques,” IEEE Transactions on Knowledge and Data Engineering, 16(8), pp. 980–991. Available at: https://doi.org/10.1109/TKDE.2004.29.
Wichard, J., Merkwirth, C. and Ogorzalek, M. (2003) “Building ensembles with heterogeneous models,” Lecture Notes in Computer Science. 7th Course of the International School on Neural Nets IIASS, pp. 22–28.
Xing, J. et al. (2019) “A comprehensive study on estimating higher heating value of biomass from proximate and ultimate analysis with machine learning approaches,” Energy, 188, pp. 1–11. Available at: https://doi.org/10.1016/j.energy.2019.116077.
Xu, Y. et al. (2019) “Hourly urban water demand forecasting using the continuous deep belief echo state network,” Water, 11(2). Available at: https://doi.org/10.3390/w11020351.
Yasar, A., Bilgili, M. and Simsek, E. (2012) “Water demand forecasting based on stepwise multiple nonlinear regression analysis,” Arabian Journal for Science and Engineering, 37(8), pp. 2333–2341. Available at: https://doi.org/10.1007/s13369-012-0309-z.
Yassin, M.A. et al. (2022) “Geochemical and spatial distribution of topsoil HMs coupled with modeling of Cr using chemometrics intelligent techniques: Case study from Dammam Area, Saudi Arabia,” 27(13), 4220. Availble at: https://doi.org/10.3390/molecules27134220.
Zubaidi, S. et al. (2018) “Short-term urban water demand prediction considering weather factors,” Water Resources Management, 14(32). pp. 4527–4542. Available at: https://doi.org/10.1007/s11269-018-2061-y.
Zubaidi, S.L. et al. (2020a) “Hybridised artificial neural network model with slime mould algorithm: A novel methodology for prediction of urban stochastic water demand,” Water, 12(10). Available at: https://doi.org/10.3390/w12102692.
Zubaidi, S.L. et al. (2020b) “Urban water demand prediction for a city that suffers from climate change and population growth: Gauteng province case study,” Water, 12(7), pp. 1–17. Available at: https://doi.org/10.3390/W12071885.
Zubaidi, S.L. et al. (2021) “Simulating a stochastic signal of urban water demand by a novel combination of data analytic and machine learning techniques,” IOP Conference Series: Materials Science and Engineering, 1058(1), 012066. Available at: https://doi.org/10.1088/1757-899x/1058/1/012066.
