Research Article
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Year 2025, Volume: 13 Issue: 2, 476 - 488, 01.06.2025

Erman Özer

https://doi.org/10.36306/konjes.1607132

Abstract

References

  • Haykin, Simon Neural networks and learning machines / Simon Haykin.—3rd ed. p. cm. Rev. ed of: Neural networks. 2nd ed., 1999. Includes bibliographical references and index. ISBN-13: 978-0-13-147139-9
  • J. . -S. R. Jang, "ANFIS: adaptive-network-based fuzzy inference system," in IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, no. 3, pp. 665-685, May-June 1993, doi: 10.1109/21.256541
  • Walia, Navneet & Singh, Harsukhpreet & Sharma, Anurag. (2015). ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey. International Journal of Computer Applications. 123. 32-38. 10.5120/ijca2015905635.
  • E. Özer, N. Sevinçkan and E. Demiroğlu, "Comparative Analysis of Computational Intelligence Techniques in Financial Forecasting: A Case Study on ANN and ANFIS Models," 2024 32nd Signal Processing and Communications Applications Conference (SIU), Mersin, Turkey, 2024, pp. 1-4, doi: 10.1109/SIU61531.2024.10600769.
  • E. Özer, “Thyroid Disease Diagnosis: A Study on the Efficacy of Feature Reduction and Biomarker Selection in Artificial Neural Network Models”, IJMSIT, c. 8, sy. 2, ss. 59–62, 2024.
  • M. Elsisi, M. -Q. Tran, K. Mahmoud, M. Lehtonen and M. M. F. Darwish, "Robust Design of ANFIS-Based Blade Pitch Controller for Wind Energy Conversion Systems Against Wind Speed Fluctuations," in IEEE Access, vol. 9, pp. 37894-37904, 2021, doi: 10.1109/ACCESS.2021.3063053.
  • J. . -S. R. Jang, "Input selection for ANFIS learning," Proceedings of IEEE 5th International Fuzzy Systems, New Orleans, LA, USA, 1996, pp. 1493-1499 vol.2, doi: 10.1109/FUZZY.1996.552396
  • Teruko Tamura, Clothing as a Mobile Environment for Human Beings, Journal of the Human-Environment System, 2007, Volume 10, Issue 1, Pages 1-6, Released on J-STAGE December 20, 2007, Online ISSN 1349-7723, Print ISSN 1345-1324, https://doi.org/10.1618/jhes.10.1,
  • Betsill, M. M. (2001). Mitigating Climate Change in US Cities: Opportunities and obstacles. Local Environment, 6(4), 393–406. https://doi.org/10.1080/13549830120091699
  • Valdivieso, P., Andersson, K.P. & Villena-Roldán, B. Institutional drivers of adaptation in local government decision-making: evidence from Chile. Climatic Change 143, 157–171 (2017). https://doi.org/10.1007/s10584-017-1961-9
  • Q. Fu, D. Niu, Z. Zang, J. Huang and L. Diao, "Multi-Stations’ Weather Prediction Based on Hybrid Model Using 1D CNN and Bi-LSTM," 2019 Chinese Control Conference (CCC), Guangzhou, China, 2019, pp. 3771-3775, doi: 10.23919/ChiCC.2019.8866496.
  • Information guide of prediction and warning service of Czech Hydrometeorological Institute for water managers: Flash floods and possibilities of their prediction - Formation of flash floods [online],Available:http://portal.chmi.cz/files/portal/docs/poboc/CB/pruvodce/pruvodce_vodohospodari_ffg. html. [Accessed: 01-Nov-2024].
  • Flash Flood Guidance, Flood Forecast Service of Czech Hydrometeorological Institute. Available : http://hydro.chmi.cz/hpps/main_rain.php?mt=ffg. [Accessed: 10-Nov-2024].
  • Mcgovern, Amy & Elmore, Kimberly & Gagne, David & Haupt, Sue & Karstens, Christopher & Lagerquist, Ryan & Smith, Travis & Williams, John. (2017). Using Artificial Intelligence to Improve Real-Time Decision-Making for High-Impact Weather. Bulletin of the American Meteorological Society. 98. 10.1175/BAMS-D-16-0123.1. IPCC AR4 WG1, Technical Summary, section TS 5.3. 1997-2017 Available at: https://wg1.ipcc.ch/publications/wg1-ar4/ar4-wg1-ts.pdf. [Accessed: 15-Nov-2024].
  • Bernauer, Thomas, "Climate Change Politics",Annual Review of Political Science, 2013, doi.org/10.1146/annurev-polisci-062011-154926
  • J. Lu, G. Vecchi, A., and T. Reichler, "Expansion of the Hadley cell under global warming". Geophysical Research Letters. 2007, 34 (6). DOI:10.1029/2006GL028443.
  • Bhaskaran K, Hajat S, Haines A, Herrett E, Wilkinson P, Smeeth L. Effects of ambient temperature on the incidence of myocardial infarction. Heart. 2009 Nov;95(21):1760-9. doi: 10.1136/hrt.2009.175000. Epub 2009 Jul 26. PMID: 19635724.
  • Wichmann, J., Ketzel, M., Ellermann, T. et al. Apparent temperature and acute myocardial infarction hospital admissions in Copenhagen, Denmark: a case-crossover study. Environ Health 11, 19 (2012). https://doi.org/10.1186/1476-069X-11-19
  • Z. Zhou, L. Liu and N. Y. Dai, "Day-ahead Power Forecasting Model for a Photovoltaic Plant in Macao Based on Weather Classification Using SVM/PCC/LM-ANN," 2021 IEEE Sustainable Power and Energy Conference (iSPEC), Nanjing, China, 2021, pp. 775-780, doi: 10.1109/iSPEC53008.2021.9735777.
  • A. P. Kurniawan, A. N. Jati, and F. Azmi, ‘Weather prediction based on fuzzy logic algorithm for supporting general farming automation system’, in 2017 5th International Conference on Instrumentation, Control, and Automation (ICA), 2017, pp. 152–157.
  • A. Y. Ardiansyah, R. Sarno and O. Giandi, "Rain detection system for estimate weather level using Mamdani fuzzy inference system," 2018 International Conference on Information and Communications Technology (ICOIACT), Yogyakarta, Indonesia, 2018, pp. 848-854, doi: 10.1109/ICOIACT.2018.8350711.
  • A. H. Setyaningrum and P. M. Swarinata, "Weather prediction application based on ANFIS (Adaptive neural fuzzy inference system) method in West Jakarta region," 2014 International Conference on Cyber and IT Service Management (CITSM), South Tangerang, Indonesia, 2014, pp. 113-118, doi: 10.1109/CITSM.2014.7042187.
  • D. Munandar, "Optimization weather parameters influencing rainfall prediction using Adaptive Network-Based Fuzzy Inference Systems (ANFIS) and linier regression," 2015 International Conference on Data and Software Engineering (ICoDSE), Yogyakarta, Indonesia, 2015, pp. 1-6, doi: 10.1109/ICODSE.2015.7436990.
  • P. G. Krishna, K. Chandra Bhanu, S. A. Ahamed, M. Umesh Chandra, N. Prudhvi and N. Apoorva, "Artificial Neural Network (ANN) Enabled Weather Monitoring and Prediction System using IoT," 2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), Bengaluru, India, 2023, pp. 46-51, doi: 10.1109/IDCIoT56793.2023.10053534.
  • K. B. Maheswari and S. Gomathi, "Analyzing the Performance of Diverse Deep Learning Architectures for Weather Prediction," 2023 5th International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 2023, pp. 738-746, doi: 10.1109/ICIRCA57980.2023.10220887.
  • Macao Meteorological and Geophysical Bureau. (2021, Jan.) Query observation data. [Online]. Available: https://www.smg.gov.mo/en/subpage/345/embed-path/p/query-weather-e_panel. [Accessed: 17-Nov-2024].

ADVANCED MODELING FOR SEA LEVEL PRESSURE PREDICTION: A COMPARATIVE EVALUATION OF ANN AND ANFIS TECHNIQUES

Year 2025, Volume: 13 Issue: 2, 476 - 488, 01.06.2025

Erman Özer

https://doi.org/10.36306/konjes.1607132

Abstract

Pressure forecast plays a crucial role in weather forecasting, and this has a direct effect on the many fields including disaster management, agriculture, energy systems etc. The goal of this study is to compare the performances between ANN and ANFIS-based models for predicting around distribution over a range of different sea-level pressure values using various meteorological attributes as inputs. This study focuses on air temperature, wind speed, and humidity data sourced from the Macau Meteorological and Geophysical Office. We populated the dataset with missing values and performance metrics were used to train and test both models (RMSE, MAPE, R²). Overall results show that both models are good for Prediction but in accuracy, we can say that ANFIS is performing better of all the ANN types at RMSE and R² than others for Sea Level Pressure Forecasting. This increased accuracy can help in a wide variety of fields, from weather-related risk management and infrastructure planning to agricultural yield forecasting.

Keywords

Artificial Neural Networks ANFIS Sea Level Pressure Forecasting

References

  • Haykin, Simon Neural networks and learning machines / Simon Haykin.—3rd ed. p. cm. Rev. ed of: Neural networks. 2nd ed., 1999. Includes bibliographical references and index. ISBN-13: 978-0-13-147139-9
  • J. . -S. R. Jang, "ANFIS: adaptive-network-based fuzzy inference system," in IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, no. 3, pp. 665-685, May-June 1993, doi: 10.1109/21.256541
  • Walia, Navneet & Singh, Harsukhpreet & Sharma, Anurag. (2015). ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey. International Journal of Computer Applications. 123. 32-38. 10.5120/ijca2015905635.
  • E. Özer, N. Sevinçkan and E. Demiroğlu, "Comparative Analysis of Computational Intelligence Techniques in Financial Forecasting: A Case Study on ANN and ANFIS Models," 2024 32nd Signal Processing and Communications Applications Conference (SIU), Mersin, Turkey, 2024, pp. 1-4, doi: 10.1109/SIU61531.2024.10600769.
  • E. Özer, “Thyroid Disease Diagnosis: A Study on the Efficacy of Feature Reduction and Biomarker Selection in Artificial Neural Network Models”, IJMSIT, c. 8, sy. 2, ss. 59–62, 2024.
  • M. Elsisi, M. -Q. Tran, K. Mahmoud, M. Lehtonen and M. M. F. Darwish, "Robust Design of ANFIS-Based Blade Pitch Controller for Wind Energy Conversion Systems Against Wind Speed Fluctuations," in IEEE Access, vol. 9, pp. 37894-37904, 2021, doi: 10.1109/ACCESS.2021.3063053.
  • J. . -S. R. Jang, "Input selection for ANFIS learning," Proceedings of IEEE 5th International Fuzzy Systems, New Orleans, LA, USA, 1996, pp. 1493-1499 vol.2, doi: 10.1109/FUZZY.1996.552396
  • Teruko Tamura, Clothing as a Mobile Environment for Human Beings, Journal of the Human-Environment System, 2007, Volume 10, Issue 1, Pages 1-6, Released on J-STAGE December 20, 2007, Online ISSN 1349-7723, Print ISSN 1345-1324, https://doi.org/10.1618/jhes.10.1,
  • Betsill, M. M. (2001). Mitigating Climate Change in US Cities: Opportunities and obstacles. Local Environment, 6(4), 393–406. https://doi.org/10.1080/13549830120091699
  • Valdivieso, P., Andersson, K.P. & Villena-Roldán, B. Institutional drivers of adaptation in local government decision-making: evidence from Chile. Climatic Change 143, 157–171 (2017). https://doi.org/10.1007/s10584-017-1961-9
  • Q. Fu, D. Niu, Z. Zang, J. Huang and L. Diao, "Multi-Stations’ Weather Prediction Based on Hybrid Model Using 1D CNN and Bi-LSTM," 2019 Chinese Control Conference (CCC), Guangzhou, China, 2019, pp. 3771-3775, doi: 10.23919/ChiCC.2019.8866496.
  • Information guide of prediction and warning service of Czech Hydrometeorological Institute for water managers: Flash floods and possibilities of their prediction - Formation of flash floods [online],Available:http://portal.chmi.cz/files/portal/docs/poboc/CB/pruvodce/pruvodce_vodohospodari_ffg. html. [Accessed: 01-Nov-2024].
  • Flash Flood Guidance, Flood Forecast Service of Czech Hydrometeorological Institute. Available : http://hydro.chmi.cz/hpps/main_rain.php?mt=ffg. [Accessed: 10-Nov-2024].
  • Mcgovern, Amy & Elmore, Kimberly & Gagne, David & Haupt, Sue & Karstens, Christopher & Lagerquist, Ryan & Smith, Travis & Williams, John. (2017). Using Artificial Intelligence to Improve Real-Time Decision-Making for High-Impact Weather. Bulletin of the American Meteorological Society. 98. 10.1175/BAMS-D-16-0123.1. IPCC AR4 WG1, Technical Summary, section TS 5.3. 1997-2017 Available at: https://wg1.ipcc.ch/publications/wg1-ar4/ar4-wg1-ts.pdf. [Accessed: 15-Nov-2024].
  • Bernauer, Thomas, "Climate Change Politics",Annual Review of Political Science, 2013, doi.org/10.1146/annurev-polisci-062011-154926
  • J. Lu, G. Vecchi, A., and T. Reichler, "Expansion of the Hadley cell under global warming". Geophysical Research Letters. 2007, 34 (6). DOI:10.1029/2006GL028443.
  • Bhaskaran K, Hajat S, Haines A, Herrett E, Wilkinson P, Smeeth L. Effects of ambient temperature on the incidence of myocardial infarction. Heart. 2009 Nov;95(21):1760-9. doi: 10.1136/hrt.2009.175000. Epub 2009 Jul 26. PMID: 19635724.
  • Wichmann, J., Ketzel, M., Ellermann, T. et al. Apparent temperature and acute myocardial infarction hospital admissions in Copenhagen, Denmark: a case-crossover study. Environ Health 11, 19 (2012). https://doi.org/10.1186/1476-069X-11-19
  • Z. Zhou, L. Liu and N. Y. Dai, "Day-ahead Power Forecasting Model for a Photovoltaic Plant in Macao Based on Weather Classification Using SVM/PCC/LM-ANN," 2021 IEEE Sustainable Power and Energy Conference (iSPEC), Nanjing, China, 2021, pp. 775-780, doi: 10.1109/iSPEC53008.2021.9735777.
  • A. P. Kurniawan, A. N. Jati, and F. Azmi, ‘Weather prediction based on fuzzy logic algorithm for supporting general farming automation system’, in 2017 5th International Conference on Instrumentation, Control, and Automation (ICA), 2017, pp. 152–157.
  • A. Y. Ardiansyah, R. Sarno and O. Giandi, "Rain detection system for estimate weather level using Mamdani fuzzy inference system," 2018 International Conference on Information and Communications Technology (ICOIACT), Yogyakarta, Indonesia, 2018, pp. 848-854, doi: 10.1109/ICOIACT.2018.8350711.
  • A. H. Setyaningrum and P. M. Swarinata, "Weather prediction application based on ANFIS (Adaptive neural fuzzy inference system) method in West Jakarta region," 2014 International Conference on Cyber and IT Service Management (CITSM), South Tangerang, Indonesia, 2014, pp. 113-118, doi: 10.1109/CITSM.2014.7042187.
  • D. Munandar, "Optimization weather parameters influencing rainfall prediction using Adaptive Network-Based Fuzzy Inference Systems (ANFIS) and linier regression," 2015 International Conference on Data and Software Engineering (ICoDSE), Yogyakarta, Indonesia, 2015, pp. 1-6, doi: 10.1109/ICODSE.2015.7436990.
  • P. G. Krishna, K. Chandra Bhanu, S. A. Ahamed, M. Umesh Chandra, N. Prudhvi and N. Apoorva, "Artificial Neural Network (ANN) Enabled Weather Monitoring and Prediction System using IoT," 2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), Bengaluru, India, 2023, pp. 46-51, doi: 10.1109/IDCIoT56793.2023.10053534.
  • K. B. Maheswari and S. Gomathi, "Analyzing the Performance of Diverse Deep Learning Architectures for Weather Prediction," 2023 5th International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 2023, pp. 738-746, doi: 10.1109/ICIRCA57980.2023.10220887.
  • Macao Meteorological and Geophysical Bureau. (2021, Jan.) Query observation data. [Online]. Available: https://www.smg.gov.mo/en/subpage/345/embed-path/p/query-weather-e_panel. [Accessed: 17-Nov-2024].
There are 26 citations in total.

Details

Primary Language English
Subjects Neural Engineering, Green-House Technologies
Journal Section Research Article
Authors

Erman Özer 0000-0002-9638-0233

Publication Date June 1, 2025
Submission Date December 25, 2024
Acceptance Date April 5, 2025
Published in Issue Year 2025 Volume: 13 Issue: 2

Cite

IEEE E. Özer, “ADVANCED MODELING FOR SEA LEVEL PRESSURE PREDICTION: A COMPARATIVE EVALUATION OF ANN AND ANFIS TECHNIQUES”, KONJES, vol. 13, no. 2, pp. 476–488, 2025, doi: 10.36306/konjes.1607132.
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