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Artificial neural networks for sustainable development: a critical review

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Abstract

Computational and statistical tools help manage the prevailing challenges of the 17 Sustainable Development Goals (SDGs) by providing meticulous understanding of contemporary issues. However, complex challenges are difficult to handle with conventional techniques, resulting to the need for more advanced methods. Artificial neural networks (ANNs) are often used as an advanced approach in modelling complex behaviour of systems. Evaluating the current utilization of ANNs helps researchers gauge their applicability to SDG-related issues. The gaps among the studied SDGs need to be addressed through a comprehensive survey of the state-of-the-art literature. Hence, this work reviews published journal articles on the application of ANNs in resolving issues of the SDGs. This review identifies the current trends and limitations of ANN for SDG, and discusses its prominent applications and field of utilization. Descriptive and content analysis of journal articles is performed for this review. Journal articles from the Scopus database reveal Clean Water and Sanitation, Affordable and Clean Energy, Sustainable Cities and Communities, and Responsible Consumption and Production are the most popular subject matter for modelling and forecasting. New innovative functions include feature selection, kriging, and simulation. The main contribution of this work is a comprehensive mapping of the current state of this area of research. This work aims to aid future researchers to recognize further possible uses of ANNs with respect to the SDGs.

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References

  • Adamowski J, Chan HF (2011) A wavelet neural network conjunction model for groundwater level forecasting. J Hydrol 407:28–40

    Google Scholar 

  • Adamowski J, Fung Chan H, Prasher SO 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 Resour Res 48:1–14

    Google Scholar 

  • Adgaba N, Alghamdi A, Sammoud R, Shenkute A, Tadesse Y, Ansari MJ, Sharma D, Hepburn C (2017) Determining spatio-temporal distribution of bee forage species of Al-Baha region based on ground inventorying supported with GIS applications and Remote Sensed Satellite Image analysis. Saudi J Biol Sci 24:1038–1044

    Google Scholar 

  • Aiken VCF, Dórea JRR, Acedo JS, de Sousa FG, Dias FG, Rosa GJDM (2019) Record linkage for farm-level data analytics: comparison of deterministic, stochastic and machine learning methods. Comput Electron Agric 163:104857

    Google Scholar 

  • Akyüz İ, Özşahin Ş, Tiryaki S, Aydın A (2017) An application of artificial neural networks for modeling formaldehyde emission based on process parameters in particleboard manufacturing process. Clean Technol Environ Policy 19:1449–1458

    Google Scholar 

  • Ali M, Deo RC, Downs NJ, Maraseni T (2018) Multi-stage committee based extreme learning machine model incorporating the influence of climate parameters and seasonality on drought forecasting. Comput Electron Agric 152:149–165

    Google Scholar 

  • Androjić I, Marović I, Kaluđer J, Kaluđer G (2019) Achieving sustainability through the temperature prediction of aggregate stockpiles. J Clean Prod 219:451–460

    Google Scholar 

  • Araújo JPC, Palha CAO, Martins FF, Silva HMRD, Oliviera JRM (2019) Estimation of energy consumption on the tire-pavement interaction for asphalt mixtures with different surface properties using data mining techniques. Transp Res Part D Transp Environ 67:421–432

    Google Scholar 

  • Aronica S, Fontana I, Giacalone G, Lo Bosco G, Rizzo R, Mazzola S, Basilone G, Ferreri R, Genovese S, Barra M, Bonanno A (2019) Identifying small pelagic Mediterranean fish schools from acoustic and environmental data using optimized artificial neural networks. Ecol Inform 50:149–161

    Google Scholar 

  • Asadi E, Da Silva MG, Antunes CH, Dias L, Glicksman L (2014) Multi-objective optimization for building retrofit: a model using genetic algorithm and artificial neural network and an application. Energy Build 81:444–456

    Google Scholar 

  • Atoyebi OD, Awolusi TF, Davies IEE (2018) Artificial neural network evaluation of cement-bonded particle board produced from red iron wood (Lophira alata) sawdust and palm kernel shell residues. Case Stud Constr Mater 9:e00185

    Google Scholar 

  • Avinash A, Murugesan A (2018) Prediction capabilities of mathematical models in producing a renewable fuel from waste cooking oil for sustainable energy and clean environment. Fuel 216:322–329

    CAS  Google Scholar 

  • Bafitlhile TM, Li Z, Li Q (2018) Comparison of Levenberg Marquardt and conjugate gradient descent optimization methods for simulation of streamflow using artificial neural network. Adv Ecol and Environ Res 3(11):217–237

    Google Scholar 

  • Bandyopadhyay S (2017) Renewable targets for India. Clean Technol Environ Policy 19:293–294

    Google Scholar 

  • Bradford E, Schweidtmann AM, Zhang D, Jing K, del Rio-Chanona EA (2018) Dynamic modeling and optimization of sustainable algal production with uncertainty using multivariate Gaussian processes. Comput Chem Eng 118:143–158

    CAS  Google Scholar 

  • Brandenburg M, Govindan K, Sarkis J, Seuring S (2014) Quantitative models for sustainable supply chain management: developments and directions. Eur J Oper Res 233:299–312

    Google Scholar 

  • Broomhead DS, Lowe D (1988) Radial basis functions, multi-variable functional interpolation and adaptive networks (No. RSRE-MEMO-4148). R Signals and Radar Establishment

  • Brundtland G (1987) Report of the world commission on environment and development: our common future. Oxford University Press, Oxford

    Google Scholar 

  • Catalano M, Galatioto F, Bell M, Namdeo A, Bergantino AS (2016) Improving the prediction of air pollution peak episodes generated by urban transport networks. Environ Sci Policy 60:69–83

    CAS  Google Scholar 

  • Chapman R, Cook S, Donough C, Lim YL, Vun Vui Ho P, Lo KW, Oberthür T (2018) Using Bayesian networks to predict future yield functions with data from commercial oil palm plantations: a proof of concept analysis. Comput Electron Agric 151:338–348

    Google Scholar 

  • Chattopadhyay PB, Rangarajan R (2014) Application of ANN in sketching spatial nonlinearity of unconfined aquifer in agricultural basin. Agric Water Manag 133:81–91

    Google Scholar 

  • Diego-Mas J-A, Poveda-Bautista R, Alcaide-Marzal J (2016) Designing the appearance of environmentally sustainable products. J Clean Prod 135:784–793

    Google Scholar 

  • Dkhichi F, Oukarfi B (2014) Levenberg–Marquardt and conjugate gradient training algorithms of neural network for parameter determination of solar cell. Int J Innov Appl Stud 9(4):1869

    Google Scholar 

  • Dutta S, Lanvin B, Wunsch-Vincent S (2019) The Global Innovation Index, 2019. https://www.globalinnovationindex.org/gii-2019-report. Accessed 21 June 2020

  • Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, pp 39–43

  • Elahi E, Weijun C, Jha SK, Zhang H (2019) Estimation of realistic renewable and non-renewable energy use targets for livestock production systems utilising an artificial neural network method: a step towards livestock sustainability. Energy 183:191–204

    Google Scholar 

  • Elman JL (1990) Finding structure in time. Cogn Sci 14(2):179–211

    Google Scholar 

  • Estahbanati MRK, Feilizadeh M, Iliuta MC (2017) Photocatalytic valorization of glycerol to hydrogen: optimization of operating parameters by artificial neural network. Appl Catal B 209:483–492

    CAS  Google Scholar 

  • Garg A, Lam JSL (2015) Measurement of environmental aspect of 3-D printing process using soft computing methods. Measur J Int Measur Confed 75:210–217

    Google Scholar 

  • Geng Z, Shang D, Han Y, Zhong Y (2019) Early warning modeling and analysis based on a deep radial basis function neural network integrating an analytic hierarchy process: a case study for food safety. Food Control 96:329–342

    Google Scholar 

  • George J, Arun P, Muraleedharan C (2018) Assessment of producer gas composition in air gasification of biomass using artificial neural network model. Int J Hydrogen Energy 43:9558–9568

    CAS  Google Scholar 

  • González M, Alonso-Almeida MM, Avila C, Dominguez D (2015) Modeling sustainability report scoring sequences using an attractor network. Neurocomputing 168:1181–1187

    Google Scholar 

  • Graves A, Wayne G, Danihelka I (2014) Neural turing machines, pp 1–26. arXiv preprint arXiv:1410.5401

  • Ha TV, Asada T, Arimura M (2019) Determination of the influence factors on household vehicle ownership patterns in Phnom Penh using statistical and machine learning methods. J Transp Geogr 78:70–86

    Google Scholar 

  • Holland JH (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor

    Google Scholar 

  • Hu L, He S, Han Z, Xiao H, Su S, Weng M, Cai Z (2019) Monitoring housing rental prices based on social media: an integrated approach of machine-learning algorithms and hedonic modeling to inform equitable housing policies. Land Use Policy 82:657–673

    Google Scholar 

  • Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: Theory and applications. Neurocomputing 70:489–501

    Google Scholar 

  • Jalilian M, Kargarsharifabad H, Abbasi Godarzi A, Ghofrani A, Sahfii MB (2016) Simulation and optimization of pulsating heat pipe flat-plate solar collectors using neural networks and genetic algorithm: a semi-experimental investigation. Clean Technol Environ Policy 18:2251–2264

    Google Scholar 

  • Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685

    Google Scholar 

  • Jurado S, Nebot À, Mugica F, Avellana N (2015) Hybrid methodologies for electricity load forecasting: entropy-based feature selection with machine learning and soft computing techniques. Energy 86:276–291

    Google Scholar 

  • Kasmuri NH, Kamarudin SK, Abdullah SRS, Hasan HA, Md Som A (2019) Integrated advanced nonlinear neural network-simulink control system for production of bio-methanol from sugar cane bagasse via pyrolysis. Energy 168:261–272

    CAS  Google Scholar 

  • Kennedy M, Dinh VN, Basu B (2016) Analysis of consumer choice for low-carbon technologies by using neural networks. J Clean Prod 112:3402–3412

    Google Scholar 

  • Kialashaki A, Reisel JR (2014) Development and validation of artificial neural network models of the energy demand in the industrial sector of the United States. Energy 76:749–760

    Google Scholar 

  • Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biol Cybern 43(1):59–69

    Google Scholar 

  • Kosov S, Shirahama K, Li C, Grzegorzek M (2018) Environmental microorganism classification using conditional random fields and deep convolutional neural networks. Pattern Recognit 77:248–261

    Google Scholar 

  • Koyee RD, Heisel U, Eisseler R, Schmauder S (2015) Modelling and optimization of turning duplex stainless steels. J Manuf Processes 16(4):451–467

    Google Scholar 

  • Kuo RJ, Wang YC, Tien FC (2010) Integration of artificial neural network and MADA methods for green supplier selection. J Clean Prod 18:1161–1170

    Google Scholar 

  • Larivière V, Haustein S, Mongeon P (2015) The oligopoly of academic publishers in the digital era. PLoS ONE 10(6):e0127502

    Google Scholar 

  • LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Google Scholar 

  • Li HZ, Guo S, Li CJ, Sun JQ (2013) A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm. Knowl-Based Syst 37:378–387

    Google Scholar 

  • Li J, Zhang Y, Du D, Liu Z (2017) Improvements in the decision making for Cleaner Production by data mining: case study of vanadium extraction industry using weak acid leaching process. J Clean Prod 143:582–597

    CAS  Google Scholar 

  • Li LL, Zhang XB, Tseng ML, Zhou YT (2019) Optimal scale Gaussian process regression model in Insulated Gate Bipolar Transistor remaining life prediction. Appl Soft Comput J 78:261–273

    Google Scholar 

  • Lindkvist E, Norberg J (2014) Modeling experiential learning: the challenges posed by threshold dynamics for sustainable renewable resource management. Ecol Econ 104:107–118. https://doi.org/10.1016/j.ecolecon.2014.04.018

    Article  Google Scholar 

  • Liu L, Lei Y (2018) An accurate ecological footprint analysis and prediction for Beijing based on SVM model. Ecol Inform 44:33–42

    Google Scholar 

  • López Santos A, Torres González JA, Meraz Jiménez ADJ et al (2019) Assessing the culture of fruit farmers from Calvillo, Aguascalientes, Mexico with an artificial neural network: an approximation of sustainable land management. Environ Sci Policy 92:311–322

    Google Scholar 

  • Maher I, Sarhan AAD, Barzani MM, Hamdi M (2015) Increasing the productivity of the wire-cut electrical discharge machine associated with sustainable production. J Clean Prod 108:247–255

    Google Scholar 

  • Maleki H, Sorooshian A, Goudarzi G, Baboli Z, Tahmasebi Birgani Y, Rahmati M (2019) Air pollution prediction by using an artificial neural network model. Clean Technol Environ Policy 21:1341–1352

    CAS  Google Scholar 

  • Marquardt DW (1963) An algorithm for least-squares estimation of nonlinear parameters. J Soc Ind Appl Math 11(2):431–441

    Google Scholar 

  • Masud MH, Ananno AA, Arefin AME, Ahamed R, Das P, Joardder MUH (2019) Perspective of biomass energy conversion in Bangladesh. Clean Technol Environ Policy 21:719–731

    Google Scholar 

  • McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115–133

    Google Scholar 

  • Melendez-Pastor I, Hernández EI, Navarro-Pedreño J, Gómez I (2014) Socioeconomic factors influencing land cover changes in rural areas: the case of the Sierra de Albarracín (Spain). Appl Geogr 52:34–45

    Google Scholar 

  • Miotto R, Wang F, Wang S, Jiang X, Dudley JT (2018) Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform 19:1236–1246

    Google Scholar 

  • Mohandes M, Rehman S, Rahman SM (2011) Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS). Appl Energy 88:4024–4032

    Google Scholar 

  • Møller MF (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6(4):525–533

    Google Scholar 

  • Mosavi A, Salimi M, Faizollahzadeh Ardabili S, Rabczuk T, Shamshirband S, Varkonyi-Koczy A (2019) State of the art of machine learning models in energy systems, a systematic review. Energies 12:1301

    Google Scholar 

  • Mozumder C, Tripathi NK (2014) Geospatial scenario based modelling of urban and agricultural intrusions in Ramsar wetland Deepor Beel in Northeast India using a multi-layer perceptron neural network. Int J Appl Earth Obs Geoinf 32:92–104

    Google Scholar 

  • Mukherjee A, Ramachandran P (2018) Prediction of GWL with the help of GRACE TWS for unevenly spaced time series data in India: analysis of comparative performances of SVR, ANN and LRM. J Hydrol 558:647–658

    Google Scholar 

  • Nair DJ, Rashidi TH, Dixit VV (2017) Estimating surplus food supply for food rescue and delivery operations. Socioecon Plan Sci 57:73–83

    Google Scholar 

  • Naji S, Keivani A, Shamshirband S, Alengaram UJ, Jumaat MZ, Mansor Z, Lee M (2016) Estimating building energy consumption using extreme learning machine method. Energy 97:506–516

    Google Scholar 

  • Nguyen TT, Kawamura A, Tong TN, Amaguchi H, Nakagawa N, Gilbuena R, Bui DD (2015a) Identification of spatio-seasonal hydrogeochemical characteristics of the unconfined groundwater in the Red River Delta, Vietnam. Appl Geochem 63:10–21

    CAS  Google Scholar 

  • Nguyen KA, Stewart RA, Zhang H, Jones C (2015b) Intelligent autonomous system for residential water end use classification: autoflow. Appl Soft Comput J 31:118–131

    CAS  Google Scholar 

  • Niestroy I (2016) How are we getting ready? The 2030 agenda for sustainable development in the EU and its member states: analysis and action so far. https://www.die-gdi.de/uploads/media/DP_9.2016.pdf. Accessed 25 May 2020

  • Ouammi A, Sacile R, Zejli D et al (2010) Sustainability of a wind power plant: application to different Moroccan sites. Energy 35:4226–4236

    CAS  Google Scholar 

  • Palani S, Liong S-Y, Tkalich P (2008) An ANN application for water quality forecasting. Mar Pollut Bull 56:1586–1597

    CAS  Google Scholar 

  • Paliwal M, Kumar UA (2009) Neural networks and statistical techniques: a review of applications. Expert Syst Appl 36:2–17

    Google Scholar 

  • Park J, Sandberg IW (1991) Universal approximation using radial-basis-function networks. Neural Comput 3(2):246–257

    CAS  Google Scholar 

  • Puyana Romero V, Maffei L, Brambilla G, Ciaburro G (2016) Modelling the soundscape quality of urban waterfronts by artificial neural networks. Appl Acoust 111:121–128

    Google Scholar 

  • Raut RD, Mangla SK, Narwane VS et al (2019) Linking big data analytics and operational sustainability practices for sustainable business management. J Clean Prod 224:10–24

    Google Scholar 

  • Reis LP, dos Reis PCM, Mazzei L, Soares CPB, Miquelino Eleto Torres CM, da Silva LF, Ruschel, AR, Rêgo LJS, Leite HG (2018) Estimation of mortality and survival of individual trees after harvesting wood using artificial neural networks in the amazon rain forest. Ecol Eng 112:140–147

    Google Scholar 

  • Riedmiller M, Braun H (1992) RPROP-A fast adaptive learning algorithm. In: Proceedings of ISCIS VII, Universitat

  • Rodrigues E, Gomes Á, Gaspar AR, Henggeler Antunes C (2018) Estimation of renewable energy and built environment-related variables using neural networks—a review. Renew Sustain Energy Rev 94:959–988

    Google Scholar 

  • Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 65(6):386

    CAS  Google Scholar 

  • Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533–536

    Google Scholar 

  • Sabzali Yameqani A, Alesheikh AA (2019) Predicting subjective measures of walkability index from objective measures using artificial neural networks. Sustain Cities Soc 48:101560

    Google Scholar 

  • Safavi HR, Golmohammadi MH, Sandoval-Solis S (2015) Expert knowledge based modeling for integrated water resources planning and management in the Zayandehrud River Basin. J Hydrol 528:773–789

    Google Scholar 

  • Sanikhani H, Deo RC, Yaseen ZM et al (2018) Non-tuned data intelligent model for soil temperature estimation: a new approach. Geoderma 330:52–64

    Google Scholar 

  • Savic DA, Walters GA, Davidson JW (1999) A genetic programming approach to rainfall-runoff modelling. Water Resour Manag 13:219–231

    Google Scholar 

  • Saviozzi M, Massucco S, Silvestro F (2019) Implementation of advanced functionalities for distribution management systems: load forecasting and modeling through artificial neural networks ensembles. Electr Power Syst Res 167:230–239

    Google Scholar 

  • Sewsynker-Sukai Y, Faloye F, Kana EBG (2017) Artificial neural networks: an efficient tool for modelling and optimization of biofuel production (a mini review). Biotechnol Biotechnol Equip 31:221–235

    CAS  Google Scholar 

  • Shaharum NSN, Shafri HZM, Gambo J, Abidin FAZ (2018) Mapping of Krau Wildlife Reserve (KWR) protected area using Landsat 8 and supervised classification algorithms. Remote Sens Appl Soc Environ 10:24–35

    Google Scholar 

  • Sharif SA, Hammad A (2019) Developing surrogate ANN for selecting near-optimal building energy renovation methods considering energy consumption, LCC and LCA. J Build Eng 25:100790

    Google Scholar 

  • Simić VM, Simić SB, Stojković Piperac M et al (2014) Commercial fish species of inland waters: a model for sustainability assessment and management. Sci Total Environ 497–498:642–650

    Google Scholar 

  • Sirsat MS, Cernadas E, Fernández-Delgado M, Barro S (2018) Automatic prediction of village-wise soil fertility for several nutrients in India using a wide range of regression methods. Comput Electron Agric 154:120–133

    Google Scholar 

  • Song YQ, Zhu AX, Sen Cui X et al (2019) Spatial variability of selected metals using auxiliary variables in agricultural soils. CATENA 174:499–513

    CAS  Google Scholar 

  • Suganthi L, Samuel AA (2012) Energy models for demand forecasting—a review. Renew Sustain Energy Rev 16:1223–1240

    Google Scholar 

  • Taslimi Renani E, Elias MFM, Rahim NA (2016) Using data-driven approach for wind power prediction: a comparative study. Energy Convers Manag 118:193–203

    Google Scholar 

  • Tien Bui D, Pradhan B, Nampak H, Bui QT, Tran QA, Nguyen QP (2016) Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS. J Hydrol 540:317–330

    Google Scholar 

  • Tufaner F, Demirci Y (2020) Prediction of biogas production rate from anaerobic hybrid reactor by artificial neural network and nonlinear regressions models. Clean Technol Environ Policy 22:713–724

    CAS  Google Scholar 

  • Tufaner F, Avşar Y, Gönüllü MT (2017) Modeling of biogas production from cattle manure with co-digestion of different organic wastes using an artificial neural network. Clean Technol Environ Policy 19:2255–2264

    CAS  Google Scholar 

  • Tufaner F, Dabanli İ, Özbeyaz A (2019) Kuraklığın Yapay Sinir Ağları ile Analizi: Adıyaman Örneği [Analysis of Drought with Artificial Neural Networks: Adıyaman Example]. İklim Değişikliği ve Çevre 4:25–32

    Google Scholar 

  • Tunckaya Y, Koklukaya E (2015) Comparative analysis and prediction study for effluent gas emissions in a coal-fired thermal power plant using artificial intelligence and statistical tools. J Energy Inst 88:118–125

    CAS  Google Scholar 

  • UNCED (1992) Agenda 21. https://sustainabledevelopment.un.org/content/documents/Agenda21.pdf. Accessed 3 March 2020

  • United Nations (2000) United Nations millennium declaration. United Nations. https://undocs.org/A/RES/55/2. Accessed 3 March 2020

  • United Nations (2015) Transforming our world: the 2030 agenda for sustainable development. https://sustainabledevelopment.un.org/content/documents/21252030%20Agenda%20for%20Sustainable%20Development%20web.pdf. Accessed 3 March 2020

  • United Nations (2019) The sustainable development goals report 2019. United Nations Statistics Division. https://unstats.un.org/sdgs/report/2019/The-Sustainable-Development-Goals-Report-2019.pdf. Accessed 3 March 2020

  • Ushada M, Okayama T, Murase H (2015) Development of kansei engineering-based watchdog model to assess worker capacity in Indonesian small-medium food industry. Eng Agric Environ Food 8(4):241–250

    Google Scholar 

  • Wang J, Chen Y, Shao X, Zhang Y, Cao Y (2012) Land-use changes and policy dimension driving forces in China: present, trend and future. Land Use Policy 29:737–749

    CAS  Google Scholar 

  • Wang L, Pijanowski B, Yang W et al (2018) Predicting multiple land use transitions under rapid urbanization and implications for land management and urban planning: the case of Zhanggong District in central China. Habitat Int 82:48–61

    Google Scholar 

  • Wang C, Ghadimi P, Lim MK, Tseng M-L (2019) A literature review of sustainable consumption and production: a comparative analysis in developed and developing economies. J Clean Prod 206:741–754

    Google Scholar 

  • Wei Y, Zhang X, Shi Y et al (2018a) A review of data-driven approaches for prediction and classification of building energy consumption. Renew Sustain Energy Rev 82:1027–1047

    Google Scholar 

  • Wei Y, Wang Z, Wang H et al (2018b) Promoting inclusive water governance and forecasting the structure of water consumption based on compositional data: a case study of Beijing. Sci Total Environ 634:407–416

    CAS  Google Scholar 

  • Wilamowski B (2009) Neural network architectures and learning algorithms. IEEE Ind Electron Mag 3:56–63

    Google Scholar 

  • World Population Review (2019) Total population by country 2019. http://worldpopulationreview.com/countries/. Accessed 3 March 2020

  • Wurm M, Stark T, Zhu XX et al (2019) Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks. ISPRS J Photogramm Remote Sens 150:59–69

    Google Scholar 

  • Xu S, Yu Z, Ji X, Sudicky EA (2017) Comparing three models to estimate transpiration of desert shrubs. J Hydrol 550:603–615

    Google Scholar 

  • Yaseen ZM, Jaafar O, Deo RC, Kisi O, Adamowski J, Quilty J, El-Shafie A (2016) Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq. J Hydrol 542:603–614

    Google Scholar 

  • Yazdi J, Salehi Neyshabouri SAA (2014) Identifying low impact development strategies for flood mitigation using a fuzzy-probabilistic approach. Environ Model Softw 60:31–44

    Google Scholar 

  • Younes MK, Nopiah ZM, Basri NEA, Abushammala MFM, Younes MY (2016) Landfill area estimation based on integrated waste disposal options and solid waste forecasting using modified ANFIS model. Waste Manag 55:3–11

    Google Scholar 

  • Zeiler MD, Krishnan D, Taylor GW, Fergus R (2010) Deconvolutional networks. In: 2010 IEEE computer society conference on computer vision and pattern recognition. IEEE, pp 2528–2535

  • Zhang J, Ji M, Zhang Y (2015) Tourism sustainability in Tibet—forward planning using a systems approach. Ecol Indic 56:218–228

    Google Scholar 

  • Zheng Y-J, Chen S-Y, Lin Y, Wang W-L (2013) Bio-inspired optimization of sustainable energy systems: a review. Math Probl Eng 2013:1–12

    Google Scholar 

  • Zheng J, Wang W, Cao X et al (2018) Responses of phosphorus use efficiency to human interference and climate change in the middle and lower reaches of the Yangtze River: historical simulation and future projections. J Clean Prod 201:403–415

    Google Scholar 

  • Zhou C-C, Yin G-F, Hu X-B (2009) Multi-objective optimization of material selection for sustainable products: Artificial neural networks and genetic algorithm approach. Mater Des 30:1209–1215

    CAS  Google Scholar 

  • Zhou Q, Wang C, Zhang G (2019) Hybrid forecasting system based on an optimal model selection strategy for different wind speed forecasting problems. Appl Energy 250:1559–1580

    Google Scholar 

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Acknowledgements

The first author acknowledges the support of the Office of the Chancellor of De La Salle University via the Competitiveness Fund for the travel grant to Asia University.

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Authors and Affiliations

  1. Mechanical Engineering Department, De La Salle University, 2401 Taft Avenue, 0922, Manila, Philippines

    Ivan Henderson V. Gue & Aristotle T. Ubando

  2. Mechanical Engineering Department, FEU Institute of Technology, 839 P. Paredes Street, 1015, Manila, Philippines

    Ivan Henderson V. Gue

  3. Center for Engineering and Sustainable Development Research, De La Salle University, 2401 Taft Avenue, 0922, Manila, Philippines

    Aristotle T. Ubando & Raymond R. Tan

  4. Thermomechanical Laboratory, De La Salle University, Laguna Campus, LTI Spine Road, Laguna Blvd, Biñan, Laguna, 4024, Philippines

    Aristotle T. Ubando

  5. Institute of Innovation and Circular Economy, Asia University, Taichung City, Taiwan

    Ming-Lang Tseng

  6. Department of Medical Research, China Medical University Hospital, China Medical University, Taichung City, Taiwan

    Ming-Lang Tseng

  7. Faculty of Economics and Management, Universiti Kebangsaan Malaysia, Bandar Baru Bangi, Selangor, Malaysia

    Ming-Lang Tseng

  8. Chemical Engineering Department, De La Salle University, 2401 Taft Avenue, 0922, Manila, Philippines

    Raymond R. Tan

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Gue, I.H.V., Ubando, A.T., Tseng, ML. et al. Artificial neural networks for sustainable development: a critical review. Clean Techn Environ Policy 22, 1449–1465 (2020). https://doi.org/10.1007/s10098-020-01883-2

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  • DOI: https://doi.org/10.1007/s10098-020-01883-2

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