Inteligencia artificial, machine learning y SIG en ingeniería ambiental: tendencias actuales
Contenido principal del artículo
Resumen
El uso de herramientas computacionales, como la Inteligencia Artificial, el Aprendizaje
Automático o los Sistemas de Información Geográfica, ha tenido un impacto significativo en
el conocimiento generado sobre cuestiones ambientales en los últimos años. El número de
publicaciones que se muestran en esta revisión preliminar de la base de datos de IEEE Xplore
Digital Library demuestra su amplia aplicabilidad y relevancia científica. Se utilizaron filtros de
búsqueda y palabras clave como agua, aire, suelo, cambio climático, energía y residuos. Los
datos obtenidos se procesaron para visualizar la proporción de uso en temas clave de interés
ambiental, proporcionando orientación científica hacia las áreas de mayor aplicabilidad, así
como hacia aquellas que merecen ser reforzadas. De esta manera, este trabajo tiene como
objetivo promover el desarrollo de soluciones de trabajo colaborativo en las áreas relacionadas
de la ingeniería ambiental y computacional.
Detalles del artículo
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
Los autores conservan los derechos de autor y ceden a la revista el derecho de la primera publicación y pueda editarlo, reproducirlo, distribuirlo, exhibirlo y comunicarlo en el país y en el extranjero mediante medios impresos y electrónicos. Asimismo, asumen el compromiso sobre cualquier litigio o reclamación relacionada con derechos de propiedad intelectual, exonerando de responsabilidad a la Editorial Tecnológica de Costa Rica. Además, se establece que los autores pueden realizar otros acuerdos contractuales independientes y adicionales para la distribución no exclusiva de la versión del artículo publicado en esta revista (p. ej., incluirlo en un repositorio institucional o publicarlo en un libro) siempre que indiquen claramente que el trabajo se publicó por primera vez en esta revista.
Citas
E. K. Nti et al, “Environmental sustainability technologies in biodiversity, energy, transportation and water
management using artificial intelligence: A systematic review,” Sustainable Futures, vol. 4, pp. 100068,
DOI: 10.1016/j.sftr.2022.100068.
S. Zhong et al, “Machine Learning: New Ideas and Tools in Environmental Science and Engineering,” Environ.
Sci. Technol., vol. 55, (19), pp. 12741-12754, 2021. DOI: 10.1021/acs.est.1c01339.
M. M. Nowak et al, “Mobile GIS applications for environmental field surveys: A state of the art,” Global Ecology
and Conservation, vol. 23, pp. e01089, 2020. DOI: 10.1016/j.gecco.2020.e01089.
P. Tahmasebi et al, “Machine learning in geo- and environmental sciences: From small to large scale,” Adv.
Water Resour., vol. 142, pp. 103619, 2020. DOI: 10.1016/j.advwatres.2020.103619.
S. S. Gill et al, “AI for next generation computing: Emerging trends and future directions,” Internet of Things, vol.
, pp. 100514, 2022. DOI: 10.1016/j.iot.2022.100514.
S. Gupta et al, “Data Analytics for Environmental Science and Engineering Research,” Environ. Sci.
Technol., vol. 55, (16), pp. 10895-10907, 2021. DOI: 10.1021/acs.est.1c01026.
G. Lü et al, “Reflections and speculations on the progress in Geographic Information Systems (GIS): a geographic perspective,” Int. J. Geogr. Inf. Sci., vol. 33, (2), pp. 346-367, 2019. DOI: 10.1080/13658816.2018.1533136.
M. Zhu et al, “A review of the application of machine learning in water quality evaluation,” Eco-Environment &
Health, vol. 1, (2), pp. 107-116, 2022. DOI: 10.1016/j.eehl.2022.06.001.
Z. Liu et al, “Remote sensing and geostatistics in urban water-resource monitoring: a review,” Mar. Freshwater
Res., vol. 74, (10), pp. 747-765, 2023. DOI: 10.1071/MF22167.
T. Ahmad et al, “Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities,” J. Clean. Prod., vol. 289, pp. 125834, 2021. DOI: 10.1016/j.jclepro.2021.125834.
A. Samad et al, “Air pollution prediction using machine learning techniques – An approach to replace existing monitoring stations with virtual monitoring stations,” Atmos. Environ., vol. 310, pp. 119987, 2023. DOI:
1016/j.atmosenv.2023.119987.
S. Liu et al, “Status and environmental management of soil mercury pollution in China: A review,” J. Environ.
Manage., vol. 277, pp. 111442, 2021. DOI: 10.1016/j.jenvman.2020.111442.
M. Abdallah et al, “Artificial intelligence applications in solid waste management: A systematic research
review,” Waste Manage., vol. 109, pp. 231-246, 2020. DOI: 10.1016/j.wasman.2020.04.057.
L. Chen et al, “Artificial intelligence-based solutions for climate change: a review,” Environmental Chemistry
Letters, vol. 21, (5), pp. 2525-2557, 2023. DOI: 10.1007/s10311-023-01617-y.
A. Balogun et al, “A review of the inter-correlation of climate change, air pollution and urban sustainability using
novel machine learning algorithms and spatial information science,” Urban Climate, vol. 40, pp. 100989, 2021.
DOI: 10.1016/j.uclim.2021.100989.
A. Alfaro-Barquero and S. Chinchilla-Brenes. “Preferencias y habilidades vocacionales de las Ingenierías
Ambiental, Forestal y Seguridad Laboral e Higiene Ambiental: Vocational preferences and skills in
Environmental Engineering, Forestry and Occupational Safety and Environmental Hygiene,” Revista Digital:
Matemática, Educación E Internet, vol. 20, (2), 2020. Available: https://tecdigital.tec.ac.cr/servicios/revistamatematica/ArticulosRevistaDigitalV20_n2_2020/RevistaDigital_AlfaroBrenes_V20_n2_2020/RevistaDigital_
AlfaroBrenes_V20_n2_2020.pdf
T. Bhardwaj, S. Mehenge and B. S. Revathi, “Wind Turbine Power Output Forecasting Using Artificial Intelligence,”
International Virtual Conference on Power Engineering Computing and Control: Developments in Electric
Vehicles and Energy Sector for Sustainable Future (PECCON), Chennai, India, 2022, pp. 1-5, doi: 10.1109/
PECCON55017.2022.9851008.
T. C. Brito and M. A. Brito, “Forecasting of Energy Consumption : Artificial Intelligence Methods,” 2022 17th
Iberian Conference on Information Systems and Technologies (CISTI), Madrid, Spain, 2022, pp. 1-4, doi:
23919/CISTI54924.2022.9820078.
Purwanto, Hermawan, Suherman, D. A. Widodo and N. Iksan, “Renewable Energy Generation Forecasting on
Smart Home Micro Grid using Deep Neural Network,” 2021 International Conference on Artificial Intelligence and
Mechatronics Systems (AIMS), Bandung, Indonesia, 2021, pp. 1-4, doi: 10.1109/AIMS52415.2021.9466089.
Q. Sun, D. Wang, D. Ma and B. Huang, “Multi-objective energy management for we-energy in Energy Internet
using reinforcement learning,” 2017 IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu,
HI, USA, 2017, pp. 1-6, doi: 10.1109/SSCI.2017.8285243.
A. A. Allal, K. Mansouri, M. Youssfi and M. Qbadou, “Toward a review of innovative solutions in the ship
design and performance management for energy-saving and environmental protection,” 2018 19th IEEE
Mediterranean Electrotechnical Conference (MELECON), Marrakech, Morocco, 2018, pp. 115-118, doi:
1109/MELCON.2018.8379078.
A. Srivastava, A. Ahmad, S. Kumar and M. A. Ahmad, “Air Pollution Data and Forecasting Data Monitored
through Google Cloud Services by using Artificial Intelligence and Machine Learning,” 2022 6th International
Conference on Electronics, Communication and Aerospace Technology, Coimbatore, India, 2022, pp. 804-
, doi: 10.1109/ICECA55336.2022.10009293.
A. Vishnubhatla, “IoT based Air Pollution Monitoring through Telit Bravo Kit,” 2022 International Conference
on Applied Artificial Intelligence and Computing (ICAAIC), Salem, India, 2022, pp. 1751-1755, doi: 10.1109/
ICAAIC53929.2022.9793252.
J. Lan, P. Zhang and Y. Huang, “Application Research of Computer Artificial Intelligence Monitoring System
in Surface Water Quality Measurement of Water Conservancy Industry,” 2022 International Conference on
Education, Network and Information Technology (ICENIT), Liverpool, United Kingdom, 2022, pp. 311-314, doi:
1109/ICENIT57306.2022.00075.
N. Desai and Dhinesh Babu L.D, “Software sensor for potable water quality through qualitative and quantitative analysis using artificial intelligence,” 2015 IEEE Technological Innovation in ICT for Agriculture and Rural
Development (TIAR), Chennai, India, 2015, pp. 208-213, doi: 10.1109/TIAR.2015.7358559.
T. C. Brito and M. A. Brito, “Forecasting of energy consumption : Artificial intelligence methods,” 2022 17th Iberian
Conference on Information Systems and Technologies (CISTI), Madrid, Spain, 2022, pp. 1-4, doi: 10.23919/
CISTI54924.2022.9820078.
R. G. Rajasekaran, S. Manikandaraj and R. Kamaleshwar, “Implementation of machine le arning algorithm for predicting user behavior and smart energy management,” 2017 International Conference on
Data Management, Analytics and Innovation (ICDMAI), Pune, India, 2017, pp. 24-30, doi: 10.1109/
ICDMAI.2017.8073480.
A. Talwariya et al, “Domestic energy consumption forecasting using machine learning,” 2022 7th
International Conference on Smart and Sustainable Technologies (SpliTech), 2022, . DOI: 10.23919/
SpliTech55088.2022.9854296.
F. Fakour et al, “Machine learning & uncertainty quantification: Application in building energy consumption,” 2022
Annual Reliability and Maintainability Symposium (RAMS), 2022, . DOI: 10.1109/RAMS51457.2022.9893988.
X. Yang et al, “A forecasting method of air conditioning energy consumption based on extreme learning
machine algorithm,” 2017 6th Data Driven Control and Learning Systems (DDCLS), 2017, . DOI: 10.1109/
DDCLS.2017.8068050.
Z. Wu and W. Chu, “Sampling strategy analysis of machine learning models for energy consumption prediction,” 2021 IEEE 9th International Conference on Smart Energy Grid Engineering (SEGE), 2021, . DOI: 10.1109/
SEGE52446.2021.9534987.
A. Prasad et al, “Analyzing land use change and climate data to forecast energy demand for a smart environment,” 2021 9th International Renewable and Sustainable Energy Conference (IRSEC), Morocco, 2021, pp.
-6, doi: 10.1109/IRSEC53969.2021.9741210.
P. Rezaei et al, “A novel energy management scheme for a microgrid with renewable energy sources considering uncertainties and demand response,” 2022 12th Smart Grid Conference (SGC), 2022, . DOI: 10.1109/
SGC58052.2022.9998980.
A. Alavi-Koosha et al, “Trend curve- and machine learning-based renewable energy development forecast,”
12th Smart Grid Conference (SGC), 2022, . DOI: 10.1109/SGC58052.2022.9998917.
A. Shahab and M. P. Singh, “Comparative analysis of different machine learning algorithms in classification
of suitability of renewable energy resource,” 2019 International Conference on Communication and Signal
Processing (ICCSP), 2019, . DOI: 10.1109/ICCSP.2019.8697969.
B. D. Parameshachari et al, “Prediction and analysis of air quality index using machine learning algorithms,”
IEEE International Conference on Data Science and Information System (ICDSIS), 2022, . DOI: 10.1109/
ICDSIS55133.2022.9915802.
K. M. O. V. K. Kekulanadara, B. T. G. S. Kumara and B. Kuhaneswaran, “Comparative analysis of machine
learning algorithms for predicting air quality index,” 2021 from Innovation to Impact (FITI), 2021, . DOI: 10.1109/
FITI54902.2021.9833033.
T. M. Amado and J. C. Dela Cruz, “Development of machine learning-based predictive models for air quality
monitoring and characterization,” TENCON 2018 - 2018 IEEE Region 10 Conference, 2018, . DOI: 10.1109/
TENCON.2018.8650518.
O. Bouakline et al, “Prediction of daily PM10 concentration using machine learning,” 2020 IEEE 2nd International
Conference on Electronics, Control, Optimization and Computer Science (ICECOCS), 2020, . DOI: 10.1109/
ICECOCS50124.2020.9314380.
A. Chauhan and P. R. Vamsi, “Anomalous ozone measurements detection using unsupervised machine learning methods,” 2019 International Conference on Signal Processing and Communication (ICSC), 2019, . DOI:
1109/ICSC45622.2019.8938256.
S. B. Kasetty and S. Nagini, “A survey paper on an IoT-based machine learning model to predict air pollution
levels,” 2022 4th International Conference on Advances in Computing, Communication Control and Networking
(ICAC3N), 2022, . DOI: 10.1109/ICAC3N56670.2022.10074555.
A. Catovic et al, “Air pollution prediction and warning system using IoT and machine learning,” 2022 International
Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 2022, . DOI:
1109/ICECCME55909.2022.9987957.
G. Gomathi et al, “Real time air pollution prediction in urban cities using deep learning algorithms and IoT,”
7th International Conference on Communication and Electronics Systems (ICCES), 2022, . DOI: 10.1109/
ICCES54183.2022.9835991.
M. Fahim et al, “A machine learning based analysis between climate change and human health: A correlational study,” 2022 International Conference on Computer and Applications (ICCA), 2022, . DOI: 10.1109/
ICCA56443.2022.10039484.
Y. -C. Lin et al, “Using machine learning to analyze and predict the relations between cardiovascular disease incidence, extreme temperature and air pollution,” 2021 IEEE 3rd Eurasia Conference on Biomedical
Engineering, Healthcare and Sustainability (ECBIOS), 2021, . DOI: 10.1109/ECBIOS51820.2021.9510479.
J. Collado and C. Pinzon, “Air pollution prediction using machine learning algorithms: A literature review,”
V Congreso Internacional en Inteligencia Ambiental, Ingeniería de Software y Salud Electrónica y Móvil
(AmITIC), San Jose, Costa Rica, 2022, pp. 1-6, doi: 10.1109/AmITIC55733.2022.9941271.
A. Dhumvad et al, “Water pollution monitoring and decision support system,” 2022 3rd International Conference
for Emerging Technology (INCET), Belgaum, India, 2022, pp. 1-6, doi: 10.1109/INCET54531.2022.9824110.
N. Rakesh and U. Kumaran, “Performance analysis of water quality monitoring system in IoT using machine learning techniques,” 2021 International Conference on Forensics, Analytics, Big Data, Security (FABS),
Bengaluru, India, 2021, pp. 1-6, doi: 10.1109/FABS52071.2021.9702592.
S. J. Sugumar et al, “Real time water treatment plant monitoring system using IOT and machine learning
approach,” 2021 International Conference on Design Innovations for 3Cs Compute Communicate Control
(ICDI3C), Bangalore, India, 2021, pp. 286-289, doi: 10.1109/ICDI3C53598.2021.00064.
D. Jalal and T. Ezzedine, “Decision tree and support vector machine for anomaly detection in water distribution
networks,” 2020 International Wireless Communications and Mobile Computing (IWCMC), Limassol, Cyprus,
, pp. 1320-1323, doi: 10.1109/IWCMC48107.2020.9148431.
U. Shafi et al, “Surface water pollution detection using internet of things,” 2018 15th International Conference
on Smart Cities: Improving Quality of Life Using ICT & IoT (HONET-ICT), Islamabad, Pakistan, 2018, pp. 92-96,
doi: 10.1109/HONET.2018.8551341.
S. Cao, S. Wang and Y. Zhang, “Design of river water quality assessment and prediction algorithm,” 2018 17th
IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, USA, 2018, pp.
-906, doi: 10.1109/ICMLA.2018.00146.
C. Lal and S. Kumar, “Ganga river water assessment using deep neural network: A study,” 2022 International
Conference on Fourth Industrial Revolution Based Technology and Practices (ICFIRTP), Uttarakhand, India,
, pp. 184-186, doi: 10.1109/ICFIRTP56122.2022.10063185.
K. Smolak et al, “Urban hourly water demand prediction using human mobility data,” 2018 IEEE/ACM 5th
International Conference on Big Data Computing Applications and Technologies (BDCAT), Zurich, Switzerland,
, pp. 213-214, doi: 10.1109/BDCAT.2018.00036.
A. N. Hasan and K. M. Alhammadi, “Quality monitoring of abu dhabi drinking water using machine learning
classifiers,” 2021 14th International Conference on Developments in eSystems Engineering (DeSE), Sharjah,
United Arab Emirates, 2021, pp. 1-6, doi: 10.1109/DeSE54285.2021.9719373.
E. Kuruvilla and S. Kundapura, “Performance comparison of machine learning algorithms in groundwater potability prediction,” 2022 IEEE 7th International Conference on Recent Advances and Innovations in Engineering
(ICRAIE), MANGALORE, India, 2022, pp. 53-58, doi: 10.1109/ICRAIE56454.2022.10054298.
V. P. Parmar and A. J. Dhruv, “Efficient sea water purification using hybrid nanofiltration system and ML for optimization,” 2021 International Conference on Artificial Intelligence and Machine Vision (AIMV), Gandhinagar,
India, 2021, pp. 1-6, doi: 10.1109/AIMV53313.2021.9670922.
R. Gai and J. Yang, “Summary of water quality prediction models based on machine learning,” 2021 IEEE 23rd
Int Conf on High Performance Computing & Communications; 7th Int Conf on Data Science & Systems; 19th Int
Conf on Smart City; 7th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/
DSS/SmartCity/DependSys), Haikou, Hainan, China, 2021, pp. 2338-2343, doi: 10.1109/HPCC-DSS-SmartCityDependSys53884.2021.00353.
Liming Zhang and Haowen Yan, “Implementation of a GIS-based water quality standards syntaxis and basin
water quality prediction system,” 2012 International Symposium on Geomatics for Integrated Water Resource
Management, Lanzhou, 2012, pp. 1-4, doi: 10.1109/GIWRM.2012.6349656.
F. R. Islam and K. A. Mamun, “GIS based water quality monitoring system in pacific coastal area: A case study
for Fiji,” 2015 2nd Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE), Nadi,
Fiji, 2015, pp. 1-7, doi: 10.1109/APWCCSE.2015.7476226.
M. G. Tyagunov and Z. Y. Lin, “Determining the optimal placements of renewable power generation systems
using regional geographic information system,” 2017 2nd International Conference on the Applications of
Information Technology in Developing Renewable Energy Processes & Systems (IT-DREPS), Amman, Jordan,
, pp. 1-6, doi: 10.1109/IT-DREPS.2017.8277823.
B. Pulido et al., “GIS-based DSS for optimal placement for oceanic power generation: OCEANLIDER project,
Spanish coastline study,” 2013 International Conference on Renewable Energy Research and Applications
(ICRERA), Madrid, Spain, 2013, pp. 137-142, doi: 10.1109/ICRERA.2013.6749740.