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A Review on the Role of Computational Intelligence on Sustainability Development

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Computational Intelligence Methodologies Applied to Sustainable Development Goals

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1036))

Abstract

This paper presents a review of the existing publications using computational intelligence techniques in applications to sustainability development. Computational intelligence is the area that deals with the design and development of intelligent systems for diverse scopes of application. Sustainable development can be viewed as a way to achieve human development goals while simultaneously maintaining the ability of natural systems to offer natural resources and ecosystem services for the benefit of the economy and society. In this regard, it is natural to think that the computational intelligence area, which includes models like neural networks, fuzzy systems, and metaheuristics, will significantly impact achieving the goals of sustainable development. This review paper reveals that there has been some work in this area. We will provide up to date relevant statistics and analysis of the current work. In addition, we will outline possible future trends for research on applying intelligent systems to problems in sustainability development.

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References

  1. Adao, T., Soares, A., Padua, L., Guimardes, N., Pinho, T., Sousa, J.J., Morais, R., Peres, E.: Mysense-Webgis: a graphical map layering-based decision support tool for agriculture. Int Geosci Remote Sens Symp (IGARSS) 4195 (2020)

    Google Scholar 

  2. Ahmed, Q., Anifowose, F.A., Khan, F.: System availability enhancement using computational intelligence-based decision tree predictive model. Proc. Inst. Mechan. Eng. Part O: J. Risk. Reliab. 229(6), 612–626 (2015)

    Google Scholar 

  3. Alptekin, S.E., Alptekin, G.I.: A fuzzy quality function deployment approach for differentiating cloud products. Int. J. Comput. Intel. Syst. 11(1), 1041–1055 (2018)

    Article  Google Scholar 

  4. An, B.: Game theoretic analysis of security and sustainability. In: IJCAI International Joint Conference on Artificial Intelligence, p. 5111 (2017)

    Google Scholar 

  5. Arabameri, A., Saha, S., Roy, J., Tiefenbacher, J.P., Cerda, A., Biggs, T., Pradhan, B., Thi Ngo, P.T., Collins, A.L.: A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility. Sci. Total Environ. 726 (2020)

    Google Scholar 

  6. Barrientos, F., Moral, A., Rodríguez, J., Martínez, C., Campo, F., Carnerero, R., Parra, M., Benítez, J.M., Sainz, G.: Knowledge-based minimization of railway infrastructures environmental impact. Transport. Res. Procedia 840 (2016)

    Google Scholar 

  7. Basak, A., Mengshoel, O., Hosein, S., Martin, R., Jayakumaran, J., Morga, M.G., Aghav, I.: Identifying contributing factors of occupant thermal discomfort in a smart building. In: AAAI Workshop—Technical Report, pp. 219 (2016)

    Google Scholar 

  8. Beyer, B., Geldermann, J., Lauven, L.: Agent-based model of the German heating market: simulations concerning the use of wood pellets and the sustainability of the market. In: International Conference on the European Energy Market, EEM (2017)

    Google Scholar 

  9. Bibri, S.E.: Novel intelligence functions for data–driven smart sustainable urbanism. In: Utilizing Complexity Sciences in Fashioning Powerful Forms of Simulations Models (2019)

    Google Scholar 

  10. Bibri, S.E.: Smart Sustainable Urbanism: Paradigmatic, Scientific, Scholarly, Epistemic, and Discursive Shifts in Light of Big Data Science and Analytics (2019)

    Google Scholar 

  11. Bibri, S.E.: The Leading Smart Sustainable Paradigm of Urbanism and Big Data Computing: A Topical Literature Review (2019)

    Google Scholar 

  12. Bondi, E.: Bridging the gap between high-level reasoning in strategic agent coordination and low-level agent development: Doctoral consortium. In: Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, p. 2402

    Google Scholar 

  13. Buitrago, P.A., Nystrom, N.A.: Neocortex and Bridges-2: A High Performance AI+HPC Ecosystem for Science, Discovery, and Societal Good (2021)

    Google Scholar 

  14. Cascales, M.S.G., Lozano, J.M.S., Arredondo, A.D.M., Corona, C.C.: Soft computing applications for renewable energy and energy efficiency. In: Soft Computing Applications for Renewable Energy and Energy Efficiency, pp. 1–408 (2014)

    Google Scholar 

  15. Chang, N., Kumar, R., Yen, G., Wang, C.: Guest Editorial: Special Issue on ‘Cyber-Innovated Environmental Sensing, Monitoring, and Modeling for Sustainability’. IEEE Syst. J. 10(3), 1236–1238 (2016)

    Google Scholar 

  16. Chen, G., He, Y., Yang, T.: An ISMP approach for promoting design innovation capability and its interaction with personal characters. IEEE Access 8, 161304–161316 (2020)

    Article  Google Scholar 

  17. Chen, Y., Hu, M.: A swarm intelligence based distributed decision approach for transactive operation of networked building clusters. Energy Build. 169, 172–184 (2018)

    Article  Google Scholar 

  18. Chui, K.T., Alhalabi, W., Pang, S.S.H., de Pablos, P.O., Liu, R.W., Zhao, M.: Disease diagnosis in smart healthcare: innovation, technologies and applications. Sustainability (Switzerland) 9(12) (2017)

    Google Scholar 

  19. Chvátalová, Z., Hřebíček, J., Bartulec, T.: Sustainability performance indicators construction with using neural networks in Maple. In: Proceedings of the 26th International Business Information Management Association Conference—Innovation Management and Sustainable Economic Competitive Advantage: From Regional Development to Global Growth, IBIMA 2015, p. 2035 (2015)

    Google Scholar 

  20. Davis, J., Edgar, T., Porter, J., Bernaden, J., Sarli, M.: Smart manufacturing, manufacturing intelligence and demand-dynamic performance. Comput. Chem. Eng. 47, 145–156 (2012)

    Article  Google Scholar 

  21. De Gracia, A., Fernández, C., Castell, A., Mateu, C., Cabeza, L.F.: Control of a PCM ventilated facade using reinforcement learning techniques. Energy Build. 106, 234–242 (2015)

    Article  Google Scholar 

  22. De Melo, J.G., De Souza Farias, F., Kato, O.R.: Assessment of the sustainability of agroecosystems in the Amazon region using neural artificial networks. IEEE Lat. Am. Trans. 14(8), 3804–3810 (2016)

    Article  Google Scholar 

  23. Du, R., Lu, Z., Pandit, A., Kuang, D., Crittenden, J., Park, H.: Toward social media opinion mining for sustainability research. AAAI Workshop—Technical Report, p. 21 (2015)

    Google Scholar 

  24. Dujardin, Y., Dietterich, T., Chadès, I.: Three new algorithms to Solve N-POMDPs. In: 31st AAAI Conference on Artificial Intelligence, AAAI 2017, p. 4495 (2017)

    Google Scholar 

  25. Dujardin, Y., Dietterich, T., Chadès, I.: α-min: A compact approximate solver for finite-horizon POMDPs. In: IJCAI International Joint Conference on Artificial Intelligence, p. 2582 (2015)

    Google Scholar 

  26. Dursun, P., Kaya, T.: Fuzzy multiple criteria sustainability assessment in forest management based on an integrated AHP-TOPSIS methodology. In: Computational Intelligence Foundations and Applications—Proceedings of the 9th International FLINS Conference, FLINS 2010, p. 438 (2010)

    Google Scholar 

  27. Eaton, E., Gomes, C., Williams, B.: Computational sustainability. AI Mag. 35(2), 3–7 (2014)

    Google Scholar 

  28. Echeverry, A.X.H., Montoya-Torres, J.R., Richards, D., Neira, N.O.: Computational Intelligence to Support Cooperative Seaport Decision-Making in Environmental and Ecological Sustainability (2015)

    Google Scholar 

  29. Ekici, B., Cubukcuoglu, C., Turrin, M., Sariyildiz, I.S.: Performative computational architecture using swarm and evolutionary optimisation: a review. Build. Environ. 147, 356–371 (2019)

    Article  Google Scholar 

  30. Emerson, A., Henderson, N., Rowe, J., Min, W., Lee, S., Minogue, J., Lester, J.: Investigating Visitor Engagement in Interactive Science Museum Exhibits with Multimodal Bayesian Hierarchical Models (2020)

    Google Scholar 

  31. Fernandes, A.S., Bacciu, D., Jarman, I.H., Etchells, T.A., Fonseca, J.M., Lisboa, P.J.G.: p-Health in breast oncology: a framework for predictive and participatory e-systems. Proc. Int. Conf. Dev. eSyst. Eng. DeSE 2009, 123 (2009)

    Google Scholar 

  32. Fernández, C., Manyà, F., Mateu, C., Sole-Mauri, F.: Modeling energy consumption in automated vacuum waste collection systems. Environ. Model. Softw. 56, 63–73 (2014)

    Article  Google Scholar 

  33. Fisher, D.H.: A selected summary of AI for computational sustainability. In: 31st AAAI Conference on Artificial Intelligence, AAAI 2017, pp. 4852 (2017)

    Google Scholar 

  34. Fisher, D.H.: Recent advances in AI for computational sustainability. IEEE Intell. Syst. 31(4), 56–61 (2016)

    Article  Google Scholar 

  35. Fisher, D.H., Dilkina, B., Eaton, E., Gomes, C.: Incorporating computational sustainability into AI education through a freely-available, collectively-composed supplementary lab text. In: Proceedings of the National Conference on Artificial Intelligence, p. 2369 (2012)

    Google Scholar 

  36. Fuchino, T., Batres, R., Shimada, Y.: A Knowledge-Based Approach for the Analysis of Abnormal Situations (2007)

    Google Scholar 

  37. Ganapathi Subramanian, S., Crowley, M.: Combining MCTS and A3C for Prediction of Spatially Spreading Processes in Forest Wildfire Settings (2018)

    Google Scholar 

  38. Garg, A., Lam, J.S.L., Gao, L.: Energy conservation in manufacturing operations: modelling the milling process by a new complexity-based evolutionary approach. J. Clean. Prod. 108, 34–45 (2015)

    Article  Google Scholar 

  39. Geem, Z.W., Chung, S.Y., Kim, J.: Improved optimization for wastewater treatment and reuse system using computational intelligence. Complexity (2018)

    Google Scholar 

  40. Naserifar, S., Chen, Y., Kwon, S., Xiao, H., Goddard, W.A.: Artificial intelligence and QM/MM with a polarizable reactive force field for next-generation electrocatalysts. Matter 4(1), 195–216 (2021)

    Article  Google Scholar 

  41. Pinter, G., Mosavi, A., Felde, I.: Artificial intelligence for modeling real estate price using call detail records and hybrid machine learning approach. Entropy 22(12), 1–14 (2020)

    Article  Google Scholar 

  42. Olivas, F., Valdez, F., Castillo, O., Melin, P.: Dynamic parameter adaptation in particle swarm optimization using interval type-2 fuzzy logic. Soft Comput. 20(3), 1057–1070 (2016)

    Article  Google Scholar 

  43. Olivas, F., Valdez, F., Castillo, O., Gonzalez, C.I., Martinez, G., Melin, P.: Ant colony optimization with dynamic parameter adaptation based on interval type-2 fuzzy logic systems. Appl. Soft Comput. 53, 74–87 (2017)

    Google Scholar 

  44. Sanchez, D., Melin, P., Castillo, O.: Optimization of modular granular neural networks using a firefly algorithm for human recognition. Eng. Appl. of AI 64, 172–186 (2017)

    Article  Google Scholar 

  45. González, B., Valdez, F., Melin, P., Prado-Arechiga, G.: Fuzzy logic in the gravitational search algorithm for the optimization of modular neural networks in pattern recognition. Expert Syst. Appl. 42(14), 5839–5847 (2015)

    Article  Google Scholar 

  46. González, B., Valdez, F., Melin, P., Prado-Arechiga, G.: Fuzzy logic in the gravitational search algorithm enhanced using fuzzy logic with dynamic alpha parameter value adaptation for the optimization of modular neural networks in echocardiogram recognition. Appl. Soft Comput. 37, 245–254 (2015)

    Article  Google Scholar 

  47. Miramontes, I., Guzman, J., Melin, P., Prado-Arechiga, G.: Optimal design of interval type-2 fuzzy heart rate level classification systems using the bird swarm algorithm. Algorithms 11(12), 206 (2018)

    Article  Google Scholar 

  48. Gonzalez, C.I., Melin, P., Castro, J.R., Castillo, O., Mendoza, O.: Optimization of interval type-2 fuzzy systems for image edge detection. Appl. Soft Comput. 47, 631–643 (2016)

    Article  Google Scholar 

  49. Castillo, O., Castro, J.R., Melin, P., Rodriguez-Diaz, A.: Application of interval type-2 fuzzy neural networks in non-linear identification and time series prediction. Soft. Comput. 18(6), 1213–1224 (2014)

    Article  Google Scholar 

  50. Melin, P., Gonzalez, C.I., Castro, J.R., et al.: Edge-detection method for image processing based on generalized type-2 fuzzy logic. IEEE Trans. Fuzzy Syst. 22(6), 1515–1525 (2014)

    Article  Google Scholar 

  51. Castillo, O., Melin, P.: A review on interval type-2 fuzzy logic applications in intelligent control. Inf. Sci. 279, 615–631 (2014)

    Article  MathSciNet  Google Scholar 

  52. Gaxiola, F., Melin, P., Valdez, F., Castro, J.R., Castillo, O.: Optimization of type-2 fuzzy weights in backpropagation learning for neural networks using GAs and PSO. Appl. Soft Comput. 38, 860–871 (2016)

    Article  Google Scholar 

  53. Melin, P., Castillo, O.: Intelligent control of complex electrochemical systems with a neuro-fuzzy-genetic approach. IEEE Trans. Industr. Electron. 48(5), 951–955 (2001)

    Article  Google Scholar 

  54. Sanchez, M.A., Castillo, O., Castro, J.R., Melin, P.: Fuzzy granular gravitational clustering algorithm for multivariate data. Inf. Sci. 279, 498–511 (2014)

    Article  MathSciNet  Google Scholar 

  55. Sánchez, D., Melin, P.: Optimization of modular granular neural networks using hierarchical genetic algorithms for human recognition using the ear biometric measure. Eng. Appl. Artif. Intell. 27, 41–56 (2014)

    Article  Google Scholar 

  56. Sanchez, M.A., Castro, J.R., Castillo, O., Mendoza, O., Rodriguez-Diaz, A., Melin, P.: Fuzzy higher type information granules from an uncertainty measurement. Granul. Comput. 2(2), 95–103 (2017)

    Article  Google Scholar 

  57. Melin, P., Miramontes, I., Prado-Arechiga, G.: A hybrid model based on modular neural networks and fuzzy systems for classification of blood pressure and hypertension risk diagnosis. Expert Syst. Appl. 107, 146–164 (2018)

    Article  Google Scholar 

  58. Guzmán, J.C., Miramontes, I., Melin, P., Prado-Arechiga, G.: Optimal genetic design of type-1 and interval type-2 fuzzy systems for blood pressure level classification. Axioms 8(1), 8 (2019)

    Article  Google Scholar 

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Authors and Affiliations

  1. Tijuana Institute of Technology, 22379, Tijuana, BC, Mexico

    Oscar Castillo & Patricia Melin

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  1. Oscar Castillo
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  2. Patricia Melin
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Correspondence to Oscar Castillo .

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Editors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

    José Luis Verdegay

  2. Department of Engineering of Systems, University of La Laguna, San Cristóbal de La Laguna, Spain

    Julio Brito

  3. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

    Carlos Cruz

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Castillo, O., Melin, P. (2022). A Review on the Role of Computational Intelligence on Sustainability Development. In: Verdegay, J.L., Brito, J., Cruz, C. (eds) Computational Intelligence Methodologies Applied to Sustainable Development Goals. Studies in Computational Intelligence, vol 1036. Springer, Cham. https://doi.org/10.1007/978-3-030-97344-5_1

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