Abstract
The atmospheric pressure plays a critical role in ecology because it serves as an essential indicator of environmental phenomena. Hence, reliable atmospheric pressure forecasts are crucial for the precise assessment of ecosystems. Therefore, the article investigates a novel framework of Adaptive Neuro-Fuzzy Inference System (ANFIS) for addressing a complex problem involving the hourly prediction of atmospheric pressure. In this study, ANFIS was hybridized with Snake Optimizer (SO), and dimensionality problems were addressed using Induced Ordered Weighted Average (IOWA). This framework is known as IOWA-ANFIS-SO. To accomplish hourly atmospheric pressure, the datasets featuring meteorological factors including air temperature, sea surface temperature, surge wind speed, wind speed, and wind direction were collected from weather buoy network stations in Ireland. The IOWA-ANFIS-SO model is assessed against the atmospheric dataset, using 70% of the data for training and 30% of the data for testing the model. Further, a comparison was made between the new hybrid IOWA-ANFIS-SO model and the more traditional IOWA-ANFIS at various alpha levels, utilizing the four statistical benchmarks: the RMSE, MAE, MAPE, and R2. Among the models, IOWA-ANFIS-SO produced optimal results for hourly atmospheric pressure prediction with RMSE (0.4698), MAE (0.3593), MAPE (0.0003), and R2 (0.9903) at alpha = 0. Further, the developed model was compared to IOWA-ANFIS and ANFIS-SO at various alpha levels, demonstrating that IOWA-ANFIS-SO outperformed them both. Finally, the results emphasized the importance of incorporating the IOWA and a snake optimizer as a means to augment the functionality of ANFIS. Therefore, this hybrid ANFIS mechanism might be advantageous for monitoring ecological systems with a wide array of input variables that are highly diverse and unpredictable.
Graphical Abstract
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The data that support the findings of this study are publicly available online at https://data.gov.ie/organization/marine-institute
References
Ausati S, Amanollahi J (2016) Assessing the accuracy of ANFIS, EEMD-GRNN, PCR, and MLR models in predicting PM2.5. Atmos Environ 142:465–474. https://doi.org/10.1016/J.ATMOSENV.2016.08.007
Bataineh K, Naji M, Saqer M (2011) A comparison study between various fuzzy clustering algorithms. Jordan J Mech Ind Eng 5:335–343
Bilgili M, Ilhan A, Ünal Ş (2022) Time-series prediction of hourly atmospheric pressure using ANFIS and LSTM approaches. Neural Comput Appl 34:15633–15648. https://doi.org/10.1007/S00521-022-07275-5
Chen MY (2013) A hybrid ANFIS model for business failure prediction utilizing particle swarm optimization and subtractive clustering. Inf Sci (ny) 220:180–195. https://doi.org/10.1016/J.INS.2011.09.013
Chen J, He H, Quan S et al (2024) Real-time power optimization based on PSO feedforward and perturbation & observation of fuel cell system for high altitude. Fuel 356:129551. https://doi.org/10.1016/J.FUEL.2023.129551
Chiu S (1995) Extracting fuzzy rules for pattern classification by cluster estimation. Proc IFSA 95:273–276
Chollet Ramampiandra E, Scheidegger A, Wydler J, Schuwirth N (2023) A comparison of machine learning and statistical species distribution models: Quantifying overfitting supports model interpretation. Ecol Modell 481:110353. https://doi.org/10.1016/J.ECOLMODEL.2023.110353
Dias JM, Muhammad R, Ikram A et al (2023) Improving significant wave height prediction using a neuro-fuzzy approach and marine predators algorithm. J Mar Sci Eng 11:1163. https://doi.org/10.3390/JMSE11061163
Dutta B, Mitra S (2011) Better prediction of humidity using artificial neural network. In: 4th Int Conf Appl Digit Inf Web Technol ICADIWT 2011, pp 59–64. https://doi.org/10.1109/ICADIWT.2011.6041395
Dyvak M, Spivak I, Melnyk A et al (2023) Modeling based on the analysis of interval data of atmospheric air pollution processes with nitrogen dioxide due to the spread of vehicle exhaust gases. Sustainability 15:2163. https://doi.org/10.3390/SU15032163
Erdil A, Arcaklioglu E (2012) The prediction of meteorological variables using artificial neural network. Neural Comput Appl 22:1677–1683. https://doi.org/10.1007/S00521-012-1210-0
Fujita M, Sugiura N, Kouketsu S (2024) Prediction of atmospheric profiles with machine learning using the signature method. Geophys Res Lett 51:e2023GL106403. https://doi.org/10.1029/2023GL106403
Gaspar P, Ponte RM (1997) Relation between sea level and barometric pressure determined from altimeter data and model simulations. J Geophys Res Ocean 102:961–971. https://doi.org/10.1029/96JC02920
Gu Y, Li B, Meng Q (2022) Hybrid interpretable predictive machine learning model for air pollution prediction. Neurocomputing 468:123–136. https://doi.org/10.1016/J.NEUCOM.2021.09.051
Harandizadeh H, Armaghani DJ (2021) Prediction of air-overpressure induced by blasting using an ANFIS-PNN model optimized by GA. Appl Soft Comput 99:106904. https://doi.org/10.1016/J.ASOC.2020.106904
Hashim FA, Hussien AG (2022) Snake Optimizer: A novel meta-heuristic optimization algorithm. Knowledge-Based Syst 242:108320. https://doi.org/10.1016/J.KNOSYS.2022.108320
He A, Singh RP, Sun Z et al (2016) Comparison of regression methods to compute atmospheric pressure and earth tidal coefficients in water level associated with wenchuan earthquake of 12 May 2008. Pure Appl Geophys 173:2277–2294. https://doi.org/10.1007/S00024-016-1310-3/METRICS
Hossain M, Rekabdar B, Louis SJ, Dascalu S (2015) Forecasting the weather of Nevada: a deep learning approach. Proc Int Jt Conf Neural Networks 1–6. https://doi.org/10.1109/IJCNN.2015.7280812
Hou J, Wang Y, Zhou J, Tian Q (2022) Prediction of hourly air temperature based on CNN–LSTM. Geomatics, Nat Hazards Risk 13:1962–1986. https://doi.org/10.1080/19475705.2022.2102942
Hussain W, Merigó JM, Raza MR, Gao H (2022) A new QoS prediction model using hybrid IOWA-ANFIS with fuzzy C-means, subtractive clustering and grid partitioning. Inf Sci (ny) 584:280–300. https://doi.org/10.1016/J.INS.2021.10.054
Jang JSR (1993) ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685. https://doi.org/10.1109/21.256541
La Rocca P, Riggi D, Riggi F (2010) Time series analysis of barometric pressure data. Eur J Phys 31:645. https://doi.org/10.1088/0143-0807/31/3/022
Li Q, Zhao Y, Yu AF (2020) A novel multichannel long short-term memory method with time series for soil temperature modeling. IEEE Access 8:182026–182043. https://doi.org/10.1109/ACCESS.2020.3028995
Li W, Kiaghadi A, Dawson C (2021) High temporal resolution rainfall–runoff modeling using long-short-term-memory (LSTM) networks. Neural Comput Appl 33:1261–1278. https://doi.org/10.1007/S00521-020-05010-6
Mamdani EH (1976) Advances in the linguistic synthesis of fuzzy controllers. Int J Man Mach Stud 8:669–678. https://doi.org/10.1016/S0020-7373(76)80028-4
Marine Institute - Publishers - data.gov.ie. https://data.gov.ie/organization/marine-institute. Accessed 11 Sep 2023
Mateus P, Catalão J, Mendes VB, Nico G (2020) An ERA5-Based Hourly Global Pressure and Temperature (HGPT) Model. Remote Sens 12:1098. https://doi.org/10.3390/RS12071098
Mathur B (2022) Predicting atmospheric variables in the MERRA-2 database using neural networks. In: 2022 International Conference on Emerging Techniques in Computational Intelligence (ICETCI), pp 125–131. https://doi.org/10.1109/ICETCI55171.2022.9921371
Mohammadi K, Shamshirband S, Petković D et al (2016) Using ANFIS for selection of more relevant parameters to predict dew point temperature. Appl Therm Eng 96:311–319. https://doi.org/10.1016/J.APPLTHERMALENG.2015.11.081
Oliaye A, Kim SH, Bae DH (2023) A new approach to weather radar adjustment for heavy rainfall events using ANFIS-PSO. J Hydrol 617:128956. https://doi.org/10.1016/J.JHYDROL.2022.128956
Optis M, Perr-Sauer J (2019) The importance of atmospheric turbulence and stability in machine-learning models of wind farm power production. Renew Sustain Energy Rev 112:27–41. https://doi.org/10.1016/J.RSER.2019.05.031
Pados DA, Papantoni-Kazakos P (1994) A note on the estimation of the generalization error and the prevention of overfitting [machine learning]. Proc 1994 IEEE Int Conf Neural Networks 1:321–326. https://doi.org/10.1109/ICNN.1994.374183
Rahman MS, Sumathy V (2024) Forecasting failure-prone air pressure systems (FFAPS) in vehicles using machine learning. Automatika 65:1–13. https://doi.org/10.1080/00051144.2023.2269514
Rahman M, Islam AHMS, Nadvi SYM, Rahman RM (2013) Comparative study of ANFIS and ARIMA model for weather forecasting in Dhaka. In: Int Conf Informatics, Electron Vision, ICIEV 2013, pp 1–6. https://doi.org/10.1109/ICIEV.2013.6572587
Rezaei M, Mousavi SF, Moridi A et al (2021) A new hybrid framework based on integration of optimization algorithms and numerical method for estimating monthly groundwater level. Arab J Geosci 14:1–15. https://doi.org/10.1007/S12517-021-07349-Z/METRICS
Santhanam T, Subhajini AC (2011) An efficient weather forecasting system using radial basis function neural network. J Comput Sci 7:962–966
Suganya S, Meyyappan T (2023) Prediction of the level of air pollution using adaptive neuro-fuzzy inference system. Multimed Tools Appl 82(24):1–20. https://doi.org/10.1007/S11042-023-15046-0/METRICS
Takagi T, Sugeno M (1983) Derivation of fuzzy control rules from human operator’s control actions. IFAC Proc 16:55–60. https://doi.org/10.1016/S1474-6670(17)62005-6
Tektaş M (2010) Weather forecasting using ANFIS and ARIMA models. A case study for Istanbul. Environ Res Eng Manag 51:5–10
Tunckaya Y (2020) Performance analysis of novel air pollution forecasting system design in a Turkish cement plant via neural and neuro-fuzzy soft computing. Energy Sources, Part A Recover Util Environ Eff 1–16. https://doi.org/10.1080/15567036.2020.1825561
Vazhuthi PPI, Prasanth A, Manikandan SP, Sowndarya KKD (2023) A hybrid ANFIS reptile optimization algorithm for energy-efficient inter-cluster routing in internet of things-enabled wireless sensor networks. Peer-to-Peer Netw Appl 16:1049–1068. https://doi.org/10.1007/S12083-023-01458-0/METRICS
Xu A, Li R, Chang H et al (2022) Artificial neural network (ANN) modeling for the prediction of odor emission rates from landfill working surface. Waste Manag 138:158–171. https://doi.org/10.1016/J.WASMAN.2021.11.045
Yager RR (1988) On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Trans Syst Man Cybern 18:183–190. https://doi.org/10.1109/21.87068
Yager RR, Filev DP (1994) Generation of fuzzy rules by mountain clustering. J Intell Fuzzy Syst 2:209–219. https://doi.org/10.3233/IFS-1994-2301
Yager RR, Filev DP (1999) Induced ordered weighted averaging operators. IEEE Trans Syst Man, Cybern Part B Cybern 29:141–150. https://doi.org/10.1109/3477.752789
Yonar A, Yonar H (2023) Modeling air pollution by integrating ANFIS and metaheuristic algorithms. Model Earth Syst Environ 9:1621–1631. https://doi.org/10.1007/S40808-022-01573-6/TABLES/2
Yuk JH, Kang JS, Myung H (2022) Applicability study of a global numerical weather prediction model MPAS to storm surges and waves in the South Coast of Korea. Atmosphere (basel) 13:591. https://doi.org/10.3390/ATMOS13040591
Zadeh LA (1965) Fuzzy sets. Inf. Control 8:338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
Acknowledgements
The authors are grateful to Vellore Institute of Technology, Vellore for their endless support of this research.
Funding
This research has no funding.
Author information
Authors and Affiliations
Contributions
Thandra Jithendra: Conceptualization, Investigation, Methodology, Writing original draft, Data curation, Formal analysis. Sharief Basha S: Supervision, Conceptualization, Resources, Methodology, Writing review and editing. Raja Das: Supervision, Writing review and editing. MATLAB Software, Validation.
Corresponding author
Ethics declarations
Ethical approval and consent to participate
Since this study did not collect personal information, no ethical approval was required.
Consent for publication
All authors have agreed and given consent for publication of this manuscript.
Competing interests
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Jithendra, T., Basha, S.S. & Das, R. Modelling atmospheric pressure through the hybridization of an ANFIS using IOWA and a snake optimizer. Model. Earth Syst. Environ. (2024). https://doi.org/10.1007/s40808-024-02015-1
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s40808-024-02015-1