Modelos híbridos y configuraciones tecnológicas para pronóstico de variables suelo-meteorológicas: revisión exhaustiva
Barra lateral del artículo
Contenido principal del artículo
Introducción: Los diseños de estaciones caseras de monitoreo del clima y del suelo han mostrado avances significativos en aplicaciones de registro, procesamiento, calibración y pronóstico, especialmente en agricultura sustentable. El desarrollo tecnológico ha impulsado el uso de sensores de aire y suelo de bajo costo, lo que ha permitido conformar bases de datos útiles para mejorar las mediciones y predicciones espacio-temporales.
Objetivo: Analizar de manera exhaustiva las configuraciones tecnológicas, aplicaciones, tendencias, calidad y necesidades de investigación en el monitoreo y pronóstico de variables suelo-meteorológicas.
Materiales y Métodos: Se revisaron artículos científicos publicados y se aplicaron herramientas de análisis estadístico y minería de datos, particularmente clustering jerárquico, para identificar patrones y enfoques relevantes en el campo.
Resultados: Los estudios muestran que la calibración de sensores de aire y suelo mediante métodos matemáticos, estadísticos y de inteligencia artificial ha permitido obtener registros confiables. Estos datos se emplean en la predicción de condiciones meteorológicas, del suelo y de niveles futuros de nutrientes y contaminantes. Asimismo, se observa un crecimiento en la utilización de modelos híbridos que combinan dos o más métodos de pronóstico, junto con estaciones meteorológicas y edáficas equipadas con diferentes sensores.
Conclusiones: La integración de tecnologías de bajo costo y modelos híbridos fortalece la precisión de los registros y pronósticos suelo-meteorológicos. Sin embargo, se identifican desafíos en la mejora de la calidad de los datos y en la consolidación de tendencias que orienten la investigación futura.
- Análisis ambiental
- Agricultura de precisión
- Sensores
- Macrodatos
- Inteligencia Artificial
Balogun AL, Tella A, Baloo L, Adebisi N. 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. 2021 Dec;40:100989. https://doi.org/10.1016/j.uclim.2021.100989
De Vries W, Dobbertin MH, Solberg S, Van Dobben HF, Schaub M. Impacts of acid deposition, ozone exposure and weather conditions on forest ecosystems in Europe: an overview. Plant Soil. 2014 Jul;380(1-2):1-45. https://doi.org/10.1007/s11104-014-2056-2
Zeng Z, Gui K, Wang Z, Luo M, Geng H, Ge E, et al. Estimating hourly surface PM2.5 concentrations across China from high-density meteorological observations by machine learning. Atmospheric Research. 2021 Jun;254:105516. https://doi.org/10.1016/j.atmosres.2021.105516
Espinosa R, Palma J, Jiménez F, Kamińska J, Sciavicco G, Lucena-Sánchez E. A time series forecasting based multi-criteria methodology for air quality prediction. Applied Soft Computing. 2021 Dec;113:107850. https://doi.org/10.1016/j.asoc.2021.107850
Akinosho TD, Oyedele LO, Bilal M, Barrera-Animas AY, Gbadamosi AQ, Olawale OA. A scalable deep learning system for monitoring and forecasting pollutant concentration levels on UK highways. Ecological Informatics. 2022 Jul;69:101609. https://doi.org/10.1016/j.ecoinf.2022.101609
Wang J, Li H, Yang H, Wang Y. Intelligent multivariable air-quality forecasting system based on feature selection and modified evolving interval type-2 quantum fuzzy neural network. Environmental Pollution. 2021 Apr;274:116429. https://doi.org/10.1016/j.envpol.2021.116429
Organization WH. Ambient air pollution: a global assessment of exposure and burden of disease. World Health Organization; 2016. https://www.cleanairjournal.org.za/article/view/7001
Koushal S, Arya D, Anbarasan S, Haloi D, M R, Rahman T, et al. Soil Pollution: Sources, Effects, and Mitigation Strategies. AJSSPN. 2025 Feb 15;11(1):280-90. https://doi.org/10.9734/ajsspn/2025/v11i1480
Kumar A, Attri AK. Correlating respiratory disease incidences with corresponding trends in ambient particulate matter and relative humidity. Atmospheric Pollution Research. 2016 Sep;7(5):858-64. https://doi.org/10.1016/j.apr.2016.05.005
Combes A, Franchineau G. Fine particle environmental pollution and cardiovascular diseases. Metabolism. 2019 Nov;100:153944. https://doi.org/10.1016/j.metabol.2019.07.008
Keikhosravi G, Fadavi SF. Impact of the inversion and air pollution on the number of patients with Covid-19 in the metropolitan city of Tehran. Urban Climate. 2021 May;37:100867. https://doi.org/10.1016/j.uclim.2021.100867
Organization WH. WHO global air quality guidelines: particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide [Internet]. World Health Organization; 2021. 290 p. Available from: https://www.who.int/publications/i/item/9789240034228
Renard JB, Surcin J, Annesi-Maesano I, Delaunay G, Poincelet E, Dixsaut G. Relation between PM2.5 pollution and Covid-19 mortality in Western Europe for the 2020-2022 period. Science of The Total Environment. 2022 Nov;848:157579. https://doi.org/10.1016/j.scitotenv.2022.157579
Różański SŁ, Castejón JMP, McGahan DG. Child risk assessment of selected metal(loid)s from urban soils using in vitro UBM procedure. Ecological Indicators. 2021 Aug;127:107726. https://doi.org/10.1016/j.ecolind.2021.107726
Khan ZI, Ahmad K, Yasmeen S, Akram NA, Ashraf M, Mehmood N. Potential health risk assessment of potato (Solanum tuberosum L.) grown on metal contaminated soils in the central zone of Punjab, Pakistan. Chemosphere. 2017 Jan;166:157-62. https://doi.org/10.1016/j.chemosphere.2016.09.064
da Silva Júnior AH, Mulinari J, de Oliveira PV, de Oliveira CRS, Reichert Júnior FW. Impacts of metallic nanoparticles application on the agricultural soils microbiota. Journal of Hazardous Materials Advances. 2022 Aug;7:100103. https://doi.org/10.1016/j.hazadv.2022.100103
Masood A, Ahmad K. A review on emerging artificial intelligence (AI) techniques for air pollution forecasting: Fundamentals, application and performance. Journal of Cleaner Production. 2021 Nov;322:129072. https://doi.org/10.1016/j.jclepro.2021.129072
Liu H, Yan G, Duan Z, Chen C. Intelligent modeling strategies for forecasting air quality time series: A review. Applied Soft Computing. 2021 Apr;102:106957. https://doi.org/10.1016/j.asoc.2020.106957
Liu H, Yin S, Chen C, Duan Z. Data multi-scale decomposition strategies for air pollution forecasting: A comprehensive review. Journal of Cleaner Production. 2020 Dec;277:124023. https://doi.org/10.1016/j.jclepro.2020.124023
Zhou C, Li S, Wang S. Examining the Impacts of Urban Form on Air Pollution in Developing Countries: A Case Study of China’s Megacities. IJERPH. 2018 Jul 24;15(8):1565. https://doi.org/10.3390/ijerph15081565
Thorat T, Patle BK, Kashyap SK. Intelligent insecticide and fertilizer recommendation system based on TPF-CNN for smart farming. Smart Agricultural Technology. 2023 Feb;3:100114. https://doi.org/10.1016/j.atech.2022.100114
Özdemir U, Taner S. Impacts of Meteorological Factors on PM 10 : Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) Approaches. Environmental Forensics. 2014 Oct 2;15(4):329-36. https://doi.org/10.1080/15275922.2014.950774
He HD, Lu WZ, Xue Y. Prediction of particulate matters at urban intersection by using multilayer perceptron model based on principal components. Stoch Environ Res Risk Assess. 2015 Dec;29(8):2107-14. https://doi.org/10.1007/s00477-014-0989-x
Tian J, Chen D. A semi-empirical model for predicting hourly ground-level fine particulate matter (PM2.5) concentration in southern Ontario from satellite remote sensing and ground-based meteorological measurements. Remote Sensing of Environment. 2010 Feb;114(2):221-9. https://doi.org/10.1016/j.rse.2009.09.011
Awewomom J, Dzeble F, Takyi YD, Ashie WB, Ettey ENYO, Afua PE, et al. Addressing global environmental pollution using environmental control techniques: a focus on environmental policy and preventive environmental management. Discov Environ. 2024 Feb 6;2(1):8. https://doi.org/10.1007/s44274-024-00048-y
Kim Y, Evans RG, Iversen WM. Remote sensing and control of an irrigation system using a distributed wireless sensor network. IEEE Transactions on Instrumentation and Measurement. 2008;57(7):1379-87. https://doi.org/10.1109/TIM.2008.917198
Ben Ishak A, Moslah Z, Trabelsi A. Analysis and prediction of PM10 concentration levels in Tunisia using statistical learning approaches. Environmental and Ecological Statistics. 2016 Sep 1;23(3):469-90. https://doi.org/10.1007/s10651-016-0349-8
Boznar M, Lesjak M, Mlakar P. A neural network-based method for short-term predictions of ambient SO2 concentrations in highly polluted industrial areas of complex terrain. Atmospheric Environment Part B Urban Atmosphere. 1993 Jun;27(2):221-30. https://doi.org/10.1016/0957-1272(93)90007-S
Chaloulakou A, Grivas G, Spyrellis N. Neural Network and Multiple Regression Models for PM 10 Prediction in Athens: A Comparative Assessment. Journal of the Air & Waste Management Association. 2003 Oct;53(10):1183-90. https://doi.org/10.1080/10473289.2003.10466276
Xiong J, Yao R, Wang W, Yu W, Li B. A spatial-and-temporal-based method for rapid particle concentration estimations in an urban environment. Journal of Cleaner Production. 2020 May;256:120331. https://doi.org/10.1016/j.jclepro.2020.120331
Baklanov A, Zhang Y. Advances in air quality modeling and forecasting. Global Transitions. 2020;2:261-70. https://doi.org/10.1016/j.glt.2020.11.001
Wu H, Levinson D. The ensemble approach to forecasting: A review and synthesis. Transportation Research Part C: Emerging Technologies. 2021;132:103357. https://doi.org/10.1016/j.trc.2021.103357
Khaydukova M, Kirsanov D, Sarkar S, Mukherjee S, Ashina J, Bhattacharyya N, et al. One shot evaluation of NPK in soils by “electronic tongue.” Computers and Electronics in Agriculture. 2021 Jul;186:106208. https://doi.org/10.1016/j.compag.2021.106208
Bai L, Wang J, Ma X, Lu H. Air Pollution Forecasts: An Overview. Int J Environ Res Public Health. 2018;44. https://doi.org/10.3390/ijerph15040780
Slater LJ, Arnal L, Boucher MA, Chang AYY, Moulds S, Murphy C, et al. Hybrid forecasting: blending climate predictions with AI models. Hydrol Earth Syst Sci. 2023 May 15;27(9):1865-89. https://doi.org/10.5194/hess-27-1865-2023
Chacón RL, García HM. Sistema de bajo coste para la medida y monitorización en agricultura inteligente. SAAEI 2018: 25 Seminario Anual de Automática, Electrónica Industrial e Instrumentación 2018: Barcelona, España: 4-6 Julio, 2018: proceedings book. 2018;250-6. https://acortar.link/t74tVZ
Fisher DK, Kebede H. A low-cost microcontroller-based system to monitor crop temperature and water status. Computers and Electronics in Agriculture. 2010 Oct;74(1):168-73. https://doi.org/10.1016/j.compag.2010.07.006
Piamonte M, Huerta M, Clotet R, Padilla J, Vargas T, Rivas D. WSN prototype for African oil palm bud rot monitoring. Advances in Intelligent Systems and Computing. 2018;687:170-81. https://doi.org/10.1007/978-3-319-70187-5_13
Abad J, Farez J, Chasi P, Guillermo JC, García-Cedeño A, Clotet R, et al. Coffee Crops Variables Monitoring: A Case of Study in Ecuadorian Andes. Advances in Intelligent Systems and Computing. 2019;893:202-17. https://doi.org/10.1007/978-3-030-04447-3_14
SV G M, Galande SG. Measurement of NPK, Temperature, Moisture, Humidity using WSN [Internet]. 2015 p. 84-9. Available from: https://www.ijera.com/papers/Vol5_issue8/Part%20-%203/M58038489.pdf
Sahitya G, Balaji N, Naidu CD, Abinaya S. Designing a wireless sensor network for precision agriculture using zigbee. Proceedings - 7th IEEE International Advanced Computing Conference, IACC 2017. 2017 Jul;287-91. https://doi.org/10.1109/IACC.2017.0069
Kapse S, Kale S, Bhongade S, Sangamnerkar S, Gotmare Y. IOT Enable Soil Testing & NPK Nutrient Detection. Jac: a Journal of Composition Theory. 2020;XIII:310-8.
Mesas-Carrascosa FJ, Santano DV, Meroño JE, Orden MS de la, García-Ferrer A. Open source hardware to monitor environmental parameters in precision agriculture. Biosystems Engineering. 2015 Sep;137:73-83. https://doi.org/10.1016/j.biosystemseng.2015.07.005
Fahmi N, Huda S, Prayitno E, Rasyid MUHA, Roziqin MC, Pamenang MU. A prototype of monitoring precision agriculture system based on WSN. 2017 International Seminar on Intelligent Technology and Its Application: Strengthening the Link Between University Research and Industry to Support ASEAN Energy Sector, ISITIA 2017 - Proceeding. 2017 Nov;2017-January:323-8. https://doi.org/10.1109/ISITIA.2017.8124103
Juan MNV, Faruk FR, Quezada YML. Design and implementation of WSN for precision agriculture in white cabbage crops. Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017. 2017 Oct; https://ieeexplore.ieee.org/abstract/document/8079671
Karimi N, Arabhosseini A, Karimi M, Kianmehr MH. Web-based monitoring system using Wireless Sensor Networks for traditional vineyards and grape drying buildings. Computers and Electronics in Agriculture. 2018 Jan;144:269-83. https://doi.org/10.1016/j.compag.2017.12.018
Dorji U, Pobkrut T, Kerdcharoen T. Electronic nose based wireless sensor network for soil monitoring in precision farming system. 2017 9th International Conference on Knowledge and Smart Technology: Crunching Information of Everything, KST 2017. 2017 Mar;182-6. https://doi.org/10.1109/KST.2017.7886087
Martínez VDV, García FGF, Cervantes GG, Medina M de JF, Casillas HAM. Desarrollo y validación de una estación meteorológica automatizada de bajo costo dirigida a agricultura. Revista mexicana de ciencias agrícolas. 2015;6(6):1253-64. https://www.scielo.org.mx/scielo.php?pid=S2007-09342015000600009&script=sci_arttext
Cordero JM, Borge R, Narros A. Using statistical methods to carry out in field calibrations of low cost air quality sensors. Sensors and Actuators B: Chemical. 2018 Aug;267:245-54. https://doi.org/10.1016/j.snb.2018.04.021
Agarwal S, Sharma S, R. S, Rahman MH, Vranckx S, Maiheu B, et al. Air quality forecasting using artificial neural networks with real time dynamic error correction in highly polluted regions. Science of The Total Environment. 2020 Sep;735:139454. https://doi.org/10.1016/j.scitotenv.2020.139454
Fernandez L, Huerta M, Sagbay G, Clotet R, Soto A. Sensing climatic variables in a orchid greenhouse. 2017 International Caribbean Conference on Devices, Circuits and Systems, ICCDCS 2017. 2017 Jun;101-4. https://doi.org/10.1109/ICCDCS.2017.7959719
Espinosa R, Jiménez F, Palma J. Multi-objective evolutionary spatio-temporal forecasting of air pollution. Future Generation Computer Systems. 2022 Nov;136:15-33. https://doi.org/10.1016/j.future.2022.05.020
Pruthi D, Liu Y. Low-cost nature-inspired deep learning system for PM2.5 forecast over Delhi, India. Environment International. 2022 Aug;166:107373. https://doi.org/10.1016/j.envint.2022.107373
Rodríguez Á, Figueredo J. Selección e implementación de un prototipo de estación meteorológica. 2016;341-52. https://acortar.link/MawXhB
Strigaro D, Cannata M, Antonovic M. Boosting a Weather Monitoring System in Low Income Economies Using Open and Non-Conventional Systems: Data Quality Analysis. Sensors 2019, Vol 19, Page 1185. 2019 Mar;19(5):1185. https://doi.org/10.3390/s19051185
Saini H, Thakur A, Ahuja S, Sabharwal N, Kumar N. Arduino based automatic wireless weather station with remote graphical application and alerts. 3rd International Conference on Signal Processing and Integrated Networks, SPIN 2016. 2016 Sep;605-9. https://doi.org/10.1109/SPIN.2016.7566768
Botero-Valencia JS, Mejia-Herrera M, Pearce JM. Low cost climate station for smart agriculture applications with photovoltaic energy and wireless communication. HardwareX. 2022 Apr;11:e00296. https://doi.org/10.1016/j.ohx.2022.e00296
Devaraju JT, Suhas KR, Mohana HK, Patil VA. Wireless Portable Microcontroller based Weather Monitoring Station. Measurement. 2015 Dec;76:189-200. https://doi.org/10.1016/j.measurement.2015.08.027
Bernardes GFLR, Ishibashi R, Ivo AAS, Rosset V, Kimura BYL. Prototyping low-cost automatic weather stations for natural disaster monitoring. Digital Communications and Networks. 2022 May; https://doi.org/10.1016/j.dcan.2022.05.002
Yamamoto K, Togami T, Yamaguchi N, Ninomiya S. Machine Learning-Based Calibration of Low-Cost Air Temperature Sensors Using Environmental Data. Sensors 2017, Vol 17, Page 1290. 2017 Jun;17(6):1290. https://doi.org/10.3390/s17061290
Perez P, Reyes J. Prediction of Particulate Air Pollution using Neural Techniques. :7. https://acortar.link/1eaA01
Zimmerman N, Presto AA, Kumar SPN, Gu J, Hauryliuk A, Robinson ES, et al. A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring. Atmospheric Measurement Techniques. 2018;11(1):291-313. https://doi.org/10.5194/amt-11-291-2018
Lin CY, Chang YS, Abimannan S. Ensemble multifeatured deep learning models for air quality forecasting. Atmospheric Pollution Research. 2021 May;12(5):101045. https://doi.org/10.1016/j.apr.2021.03.008
Hofman J, Nikolaou M, Shantharam SP, Stroobants C, Weijs S, Manna VPL. Distant calibration of low-cost PM and NO2 sensors; evidence from multiple sensor testbeds. Atmospheric Pollution Research. 2022 Jan;13(1):101246. https://doi.org/10.1016/j.apr.2021.101246
Gladkova E, Saychenko L. Applying machine learning techniques in air quality prediction. Transportation Research Procedia. 2022;63:1999-2006. https://doi.org/10.1016/j.trpro.2022.06.222
Smith JR. Weather Station and Data Logger. Programming the PIC Microcontroller with MBASIC. 2005 Jan;691-728. https://doi.org/10.1016/B978-075067946-6/50030-4
Benghanem M. Measurement of meteorological data based on wireless data acquisition system monitoring. Applied Energy. 2009 Dec;86(12):2651-60. https://doi.org/10.1016/j.apenergy.2009.03.026
Cao-Hoang T, Duy CN. Environment monitoring system for agricultural application based on wireless sensor network. 7th International Conference on Information Science and Technology, ICIST 2017 - Proceedings. 2017 May;99-102. https://doi.org/10.1109/ICIST.2017.7926499
Ma RH, Wang YH, Lee CY. Wireless Remote Weather Monitoring System Based on MEMS Technologies. Sensors (Basel, Switzerland). 2011 Mar;11(3):2715. https://doi.org/10.3390/s110302715
Jeong JH, Choi J, Jeong JY, Woo SH, Kim SW, Lee D, et al. A novel statistical-dynamical method for a seasonal forecast of particular matter in South Korea. Science of The Total Environment. 2022 Nov;848:157699. https://doi.org/10.1016/j.scitotenv.2022.157699
Mat I, Kassim MRM, Harun AN, Yusoff IM. IoT in Precision Agriculture applications using Wireless Moisture Sensor Network. ICOS 2016 - 2016 IEEE Conference on Open Systems. 2017 Mar;24-9. https://doi.org/10.1109/ICOS.2016.7881983
Pratama H, Yunan A, Candra RA. Design and Build a Soil Nutrient Measurement Tool for Citrus Plants Using NPK Soil Sensors Based on the Internet of Things. Brilliance: Research of Artificial Intelligence. 2021 Dec;1(2):67-74. https://doi.org/10.47709/brilliance.v1i2.1300
Suresh DS, V JKP, J RC. Automated Soil Testing Device. ITSI Transac-tions on Electrical and Electronics Engineering (ITSI-TEEE) ISSN (PRINT) 2013;(1):2320-8945.
Madhumathi R, Arumuganathan T, Shruthi R. Soil NPK and Moisture analysis using Wireless Sensor Networks. 2020 11th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2020. 2020 Jul; https://doi.org/10.1109/ICCCNT49239.2020.9225547
Shylaja SN, Veena MB. Real-time monitoring of soil nutrient analysis using WSN. 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS 2017. 2018 Jun;3059-62. https://doi.org/10.1109/ICECDS.2017.8390018
Flores-Medina M, Flores-García F, Velasco-Martínez V, González-Cervantes G, Jurado-Zamarripa F. Monitoreo de humedad en suelo a través de red inalámbrica de sensores. Tecnología y ciencias del agua. 2015 Oct;6(5):75-88. https://www.scielo.org.mx/scielo.php?pid=S2007-24222015000500006&script=sci_arttext
Ananthi N, Divya J, Divya M, Janani V. IoT based smart soil monitoring system for agricultural production. Proceedings - 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development, TIAR 2017. 2018 Jan;2018-January:209-14. https://doi.org/10.1109/TIAR.2017.8273717
Sindhu P, Indirani G. IoT Enabled Soil Testing. Asian Journal of Computer Science and Technology. 2018;7(S1):54-7. https://doi.org/10.51983/ajcst-2018.7.S1.1805
Ramadan KM, Oates MJ, Molina-Martinez JM, Ruiz-Canales A. Design and implementation of a low cost photovoltaic soil moisture monitoring station for irrigation scheduling with different frequency domain analysis probe structures. Computers and Electronics in Agriculture. 2018 May;148:148-59. https://doi.org/10.1016/j.compag.2017.12.038
Lawrence MG. The Relationship between Relative Humidity and the Dewpoint Temperature in Moist Air: A Simple Conversion and Applications. Bull Amer Meteor Soc. 2005 Feb;86(2):225-34. https://doi.org/10.1175/BAMS-86-2-225
Tehrani NA, Esfahani IC, Sun H. Simultaneous humidity and temperature measurement with micropillar enhanced QCM sensors. Sensors and Actuators A: Physical. 2024 Feb;366:115039. https://doi.org/10.1016/j.sna.2024.115039
Topp GC, Davis JL, Annan AP. Electromagnetic determination of soil water content: Measurements in coaxial transmission lines. Water Resour Res. 1980 Jun;16(3):574-82. https://doi.org/10.1029/WR016i003p00574
Mane S, Das N, Singh G, Cosh M, Dong Y. Advancements in dielectric soil moisture sensor Calibration: A comprehensive review of methods and techniques. Computers and Electronics in Agriculture. 2024;218:108686. https://doi.org/10.1016/j.compag.2024.108686
Amankwah SK, Ireson AM, Brannen R. An improved model and field calibration technique for measuring liquid water content in unfrozen and frozen soils with dielectric probes. Vadose Zone Journal. https://acsess.onlinelibrary.wiley.com/doi/pdf/10.1002/vzj2.20225
The World Air Quality Index project. aqicn.org. 2023 [cited 2023 Jun 27]. Air Quality Open Data Platform. Available from: https://aqicn.org/data-platform/tos/
Rosiek S, Batlles FJ. A microcontroller-based data-acquisition system for meteorological station monitoring. Energy Conversion and Management. 2008 Dec;49(12):3746-54. https://doi.org/10.1016/j.enconman.2008.05.029
Babalola TE, Babalola AD, Olokun MS. Development of an ESP-32 Microcontroller Based Weather Reporting Device. JERR. 2022 Jul 12;27-38. https://doi.org/10.9734/jerr/2022/v22i1117577
Sziroczak D, Rohacs D, Rohacs J. Review of using small UAV based meteorological measurements for road weather management. Progress in Aerospace Sciences. 2022 Oct;134:100859. https://doi.org/10.1016/j.paerosci.2022.100859
Outay F, Galland S, Gaud N, Abbas-Turki A. Simulation of connected driving in hazardous weather conditions: General and extensible multiagent architecture and models. Engineering Applications of Artificial Intelligence. 2021 Sep;104:104412. https://doi.org/10.1016/j.engappai.2021.104412
Han E, Ines AVM, Baethgen WE. Climate-Agriculture-Modeling and Decision Tool (CAMDT): A software framework for climate risk management in agriculture. Environmental Modelling & Software. 2017 Sep;95:102-14. https://doi.org/10.1016/j.envsoft.2017.06.024
Danbatta SJ, Varol A. Comparison of Zigbee, Z-Wave, Wi-Fi, and Bluetooth Wireless Technologies Used in Home Automation. In: 2019 7th International Symposium on Digital Forensics and Security (ISDFS) [Internet]. Barcelos, Portugal: IEEE https://doi.org/10.1109/ISDFS.2019.8757472
Kumar, Bhuvana C, Anushya S. Comparison of ZigBee and Bluetooth wireless technologies-survey. In: 2017 International Conference on Information Communication and Embedded Systems (ICICES) [Internet]. Chennai, India: IEEE; 2017 [cited 2023 May 10]. p. 1-4. Available from: https://doi.org/10.1109/ICICES.2017.8070716
Ye S, Xue P, Fang W, Dai Q, Peng J, Sun Y, et al. Quantitative effects of PM concentrations on spectral distribution of global normal irradiance. Solar Energy. 2021 May;220:1099-108. https://doi.org/10.1016/j.solener.2020.08.070
Zhang C, Stevenson D. Characteristic changes of ozone and its precursors in London during COVID-19 lockdown and the ozone surge reason analysis. Atmospheric Environment. 2022 Mar;273:118980. https://doi.org/10.1016/j.atmosenv.2022.118980
Vazquez Santiago J, Inoue K, Tonokura K. Diagnosis of ozone formation sensitivity in the Mexico City Metropolitan Area using HCHO/NO2 column ratios from the ozone monitoring instrument. Environmental Advances. 2021 Dec;6:100138. https://doi.org/10.1016/j.envadv.2021.100138
Wongrin W, Chaisee K, Suphawan K. Comparison of Statistical and Deep LearningMethods for Forecasting PM2.5 Concentrationin Northern Thailand. Pol J Environ Stud. 2023 Feb 23;32(2):1419-31. https://doi.org/10.15244/pjoes/157072
Descargas
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
Los autores que publican en esta revista están de acuerdo con los siguientes términos:
Los autores ceden los derechos patrimoniales a la revista y a la Universidad del Valle sobre los manuscritos aceptados, pero podrán hacer los reusos que consideren pertinentes por motivos profesionales, educativos, académicos o científicos, de acuerdo con los términos de la licencia que otorga la revista a todos sus artículos.
Los artículos serán publicados bajo la licencia Creative Commons 4.0 BY-NC-SA (de atribución, no comercial, sin obras derivadas).