Atmosphere air temperature forecasting using the honey badger optimization algorithm: on the warmest and coldest areas of the world

References

• Abramson, B., Brown, J., Edwards, W., Murphy, A., & Winkler, R. L. (1996). Hailfinder: A bayesian system for forecasting severe weather. International Journal of Forecasting, 12(1), 57–71. https://doi.org/10.1016/0169-2070(95)00664-8
   Web of Science ®Google Scholar
• Alomar, M. K., Khaleel, F., Aljumaily, M. M., Masood, A., Razali, S. F. M., AlSaadi, M. A., Al-Ansari, N., & Hamed, M. M. (2022). Data-driven models for atmospheric air temperature forecasting at a continental climate region. PLoS ONE, 17(11), e0277079. https://doi.org/10.1371/journal.pone.0277079
   PubMed Web of Science ®Google Scholar
• Anushka, P., Hazi, MD,A, & Upaka, R. (2020). Comparison of different artificial neural network (ANN) training algorithms to predict the atmospheric temperature in Tabuk, Saudi Arabia. MAUSAM, 71(2), 233–244. https://doi.org/10.54302/mausam.v71i2.22
   Web of Science ®Google Scholar
• Attar, N. F., Khalili, K., Behmanesh, J., & Khan Mohammadi, N. (2018). On the reliability of soft computing methods in the estimation of dew point temperature: The case of arid regions of Iran. Computers and Electronics in Agriculture, 153, 334–346. https://doi.org/10.1016/j.compag.2018.08.029
   Web of Science ®Google Scholar
• Band, S. S., Hegi, E., Bateni, S., Karimi, H., Rabiee, M., Samadianfard, S., Chau, K.W., & Mosavi, A. (2021). Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression. Engineering Applications of Computational Fluid Mechanics, 15(1), 1147–1158.
   Web of Science ®Google Scholar
• Begg, C. M., Begg, K. S., Du Toit, J. T., & Mills, M. G. L. (2003). Scent-marking behavior of the honey badger, mellivora capensis (mustelidae), in the southern Kalahari. Animal Behaviour, 66(5), 917–929. https://doi.org/10.1006/anbe.2003.2223
   Google Scholar
• Begg, C. M., Begg, K. S., Du Toit, J. T., & Mills, M. G. L. (2005). Life-history variables of an atypical mustelid, the honey badger Mellivora capensis. Journal of Zoology, 265(1), 17–22. https://doi.org/10.1017/S0952836904005990
   Google Scholar
• Chen, L., & Lai, X. (2011). Comparison between arima and ann models used in short-term wind speed forecasting, in: 2011 Asia-Pacific Power and Energy Engineering Conference, IEEE. pp. 1–4.
   Google Scholar
• Chevalier, R. F., Hoogenboom, G., McClendon, R. W., & Paz, A. J. (2011). Support vector regression with reduced training sets for air temperature prediction: A comparison with artificial neural networks. Neural Computing and Applications, 20(1), 151–159. https://doi.org/10.1007/s00521-010-0363-y
   Web of Science ®Google Scholar
• Cifuentes, J., Marulanda, G., Bello, A., & Reneses, J. (2020). Air temperature forecasting using machine learning techniques: A review. Energies, 13(16), 4215. https://doi.org/10.3390/en13164215
   Web of Science ®Google Scholar
• Cofino, A. S., Cano Trueba, R., Sordo, C. M., & Guti 'errez Llorente, J. M. (2002). Bayesian networks for probabilistic weather prediction.
   Google Scholar
• Curceac, S., Ternynck, C., Ouarda, T. B., Chebana, F., & Niang, S. D. (2019). Short-term air temperature forecasting using nonparametric functional data analysis and SARMA models. Environmental Modelling & Software, 111, 394–408. https://doi.org/10.1016/j.envsoft.2018.09.017
   Web of Science ®Google Scholar
• Deng, T., Duan, H. F., & Keramat, A. (2022). Spatiotemporal characterization and forecasting of coastal water quality in the semi-enclosed Tolo Harbour based on machine learning and EKC analysis. Engineering Applications of Computational Fluid Mechanics, 16(1), 694–712. https://doi.org/10.1080/19942060.2022.2035257
   Web of Science ®Google Scholar
• Elseify, M. A., Kamel, S., Abdel-Mawgoud, H., & Elattar, E. (2022). A novel approach based on honey badger algorithm for optimal allocation of multiple DG and capacitor in radial distribution networks considering power loss sensitivity. Mathematics, 10(12), 2081. https://doi.org/10.3390/math10122081
   Web of Science ®Google Scholar
• Ferreira, C. (2001a). Gene expression programming in problem solving. In: 6th Online World Conference on Soft computing in Industrial Applications (invited tutorial).
   Google Scholar
• Ferreira, C. (2001b). Gene expression programming: A new adaptive algorithm for solving problems. Complex Systems, 13(2), 87–129. https://doi.org/10.48550/arXiv.cs/0102027
   Google Scholar
• Handler, S. L., Reeves, H. D., & McGovern, A. (2020). Development of a probabilistic subfreezing road temperature nowcast and forecast using machine learning. Weather and Forecasting, 1845–1863. https://doi.org/10.1175/WAF-D-19-0159.1
   Web of Science ®Google Scholar
• Hanoon, M. S., Najah Ahmed, A., Zaini, N., Kumar, P., Sherif, M., Sefelnasr, A., & El-Shafie, A. (2021). Developing machine learning algorithms for meteorological temperature and humidity forecasting at terengganu state in Malaysia. Scientific Reports, 11(1), 18935. https://doi.org/10.1038/s41598-021-96872-w
   PubMed Web of Science ®Google Scholar
• Haykin, S. S. (1999). Neural networks: A comprehensive foundation. Prentice Hall.
   Google Scholar
• Horenko, I., Klein, R., Dolaptchiev, S., & Schütte, C. (2008). Automated generation of reduced stochastic weather models i: Simultaneous dimension and model reduction for time series analysis. Multiscale Modeling & Simulation, 6(4), 1125–1145. https://doi.org/10.1137/060670535
   Web of Science ®Google Scholar
• Hossain, M., Rekabdar, B., Louis, S. J., & Dascalu, S. (2015). Forecasting the weather of nevada: A deep learning approach, in: 2015 international joint conference on neural networks (IJCNN), IEEE. pp. 1–6.
   Google Scholar
• Hou, J., Wang, Y., Zhou, J., & Tian, Q. (2022). Prediction of hourly air temperature based on CNN–LSTM. Geomatics, Natural Hazards and Risk, 13(1), 1962–1986. https://doi.org/10.1080/19475705.2022.2102942
   Web of Science ®Google Scholar
• Kapner, D. J., Cook, T. S., Adelberger, E. G., Gundlach, J. H., Heckel, B. R., Hoyle, C. D., & Swanson, H. E. (2007). Tests of the gravitational inverse-square law below the dark-energy length scale. Physical Review Letters, 98(2), 021101. https://doi.org/10.1103/PhysRevLett.98.021101
   PubMed Web of Science ®Google Scholar
• Kazemi, S. M., Saffarian, M., & Babaiyan, V. (2021). Time series forecasting of air temperature using an intelligent hybrid model of genetic algorithm and neural network. Journal of Industrial and Systems Engineering, 13(3), 1–15. 20.1001.1.17358272.2021.13.3.1.3
   Google Scholar
• Khan, M. I., & Maity, R. (2022). Hybrid deep learning approach for multi-step-ahead prediction for daily maximum temperature and heatwaves. Theoretical and Applied Climatology, 149(3-4), 945–963. https://doi.org/10.1007/s00704-022-04103-7
   Web of Science ®Google Scholar
• Khatibi, R., Ghorbani, M. A., Kashani, M. H., & Kisi, O. (2011). Comparison of three artificial intelligence techniques for discharge routing. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2011.03.007.
   Web of Science ®Google Scholar
• Kuligowski, R. J., & Barros, A. P. (1998). Localized precipitation forecasts from a numerical weather prediction model using artificial neural networks. Weather and Forecasting, 13(4), 1194–1204. https://doi.org/10.1175/1520-0434(1998)013<1194:LPFFAN>2.0.CO;2
   Web of Science ®Google Scholar
• Lan, L., Lian, Z., & Pan, L. (2010). The effects of air temperature on office workers’ well-being, workload and productivity-evaluated with subjective ratings. Applied Ergonomics, 42(1), 29–36. https://doi.org/10.1016/j.apergo.2010.04.003
   PubMed Web of Science ®Google Scholar
• Li, G., & Yang, N. (2022). A hybrid SARIMA-LSTM model for Air temperature forecasting. Advanced Theory and Simulations. A Hybrid SARIMA-LSTM Model for Air Temperature Forecasting, 2200502. https://doi.org/10.1002/adts.202200502
   Google Scholar
• Lin, M. L., Tsai, C. H. W., & Chen, C. K. (2021). Daily maximum temperature forecasting in changing climate using a hybrid of multi-dimensional complementary ensemble empirical mode decomposition and radial basis function neural network. Journal of Hydrology: Regional Studies, 38(2021), 100923. https://doi.org/10.1016/j.ejrh.2021.100923
   Google Scholar
• Liu, J. N., Hu, Y., He, Y., Chan, P. W., & Lai, L. (2015). Deep neural network modeling for big data weather forecasting, in: Information granularity, Big data, and computational intelligence (pp. 389–408). Springer.
   Google Scholar
• Marchuk, G. (2012). Numerical methods in weather prediction. Elsevier.
   Google Scholar
• McGovern, A., John Gagne, D., Troutman, N., Brown, R. A., Basara, J., & Williams, J. K. (2011). Using spatiotemporal relational random forests to improve our understanding of severe weather processes. Statistical Analysis and Data Mining: The ASA Data Science Journal, 4(4), 407–429. https://doi.org/10.1002/sam.10128
   Google Scholar
• Mcgovern, A., Supinie, T., Gagne, I., Collier, M., Brown, R., Basara, J., & Williams, J. (2010). Understanding severe weather processes through spatiotemporal relational random forests, in: 2010 NASA conference on intelligent data understanding (to appear).
   Google Scholar
• Mirarabi, A., Nassery, H. R., Nakhaei, M., Adamowski, J., Akbarzadeh, A. H., & Alijani, F. (2019). Evaluation of datadriven models (SVR and ANN) for groundwater-level prediction in confined and unconfined systems. Environmental Earth Sciences, 78(15), 489. https://doi.org/10.1007/s12665-019-8474-y
   Web of Science ®Google Scholar
• Mittelman, R., Kuipers, B., Savarese, S., & Lee, H. (2014). Structured recurrent temporal restricted boltzmann machines (pp. 1647–1655). International Conference on Machine Learning.
   Google Scholar
• Mohsenzadeh Karimi, N., Kisi, O. Porrajabali, M., Rouhani-Nia, M., and Shiri, J. (2018). Evaluation of the support vector machine, random forest and geo-statistical methodologies for predicting long-term air temperature. ISH Journal of Hydraulic Engineering, 26(4), 376–386. https://doi.org/10.1080/09715010.2018.1495583
   Google Scholar
• Moriasi, D. A., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2015). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. American Society of Agricultural and Biological Engineers, 50(3), 885–900.
   Google Scholar
• Moravej, M., Amani, P., & Hosseini-Moghari, S. M. (2020). Groundwater level simulation and forecasting using interior search algorithm-least square support vector regression (ISA-LSSVR). Groundw Sustain Dev, 11, 100447. https://doi.org/10.1016/j.gsd.2020.100447
   Google Scholar
• Naganna, S. R., Deka, P. C., Ghorbani, M. A., Biazar, S. M., Al-Ansari, N., & Yaseen, Z. M. (2019). Dew point temperature estimation: Application of artificial intelligence model integrated with nature-inspired optimization algorithms. Water, 11(4), 742. https://doi.org/10.3390/w11040742
   Web of Science ®Google Scholar
• Nayak, P. C., Rao, V. R. S., & Sudheer, K. P. (2006). Groundwater level forecasting in shallow aquifer using artificial neural network approach. Water Resources Management, 20(1), 77–90. https://doi.org/10.1007/s11269-006-4007-z
   Web of Science ®Google Scholar
• Radhika, Y., & Shashi, M. (2009). Atmospheric temperature prediction using support vector machines. International Journal of Computer Theory and Engineering, 1, 55. https://doi.org/10.7763/IJCTE.2009.V1.9
   Google Scholar
• Rajendra, P., Murthy, K. V. N., Subbarao, A., & Boadeh, R. (2019). Use of ANN models in the prediction of meteorological data. Modeling Earth Systems and Environment, 5(3), 1051–1058. https://doi.org/10.1007/s40808-019-00590-2
   Web of Science ®Google Scholar
• Rasnopolsky, V. M., & Fox-Rabinovitz, M. S. (2006). Complex hybrid models combining deterministic and machine learning components for numerical climate modeling and weather prediction. Neural Networks, 19(2), 122–134. https://doi.org/10.1016/j.neunet.2006.01.002
   PubMed Web of Science ®Google Scholar
• Richardson, L. F. (2007). Weather prediction by numerical process. Cambridge university press.
   Google Scholar
• Roy, D. S. (2020). Forecasting the Air temperature at a weather station using deep neural networks. Procedia Computer Science, 178, 38–46. https://doi.org/10.1016/j.procs.2020.11.005
   Google Scholar
• Salcedo-Sanz, S., Deo, R., Carro-Calvo, L., & Saavedra-Moreno, B. (2016). Monthly prediction of air temperature in Australia and New Zealand with machine learning algorithms. Theoretical and Applied Climatology, 125(1-2), 13–25. https://doi.org/10.1007/s00704-015-1480-4
   Web of Science ®Google Scholar
• Sari, Y., Arifin, Y. F., Novitasari, N., & Faisal, M. L. (2022). Deep learning approach using the GRU-LSTM hybrid model for Air temperature prediction on daily basis. International Journal of Intelligent Systems and Applications in Engineering, 10(3), 430–436.
   Google Scholar
• Schulte, P. A., Bhattacharya, A., Butler, C. R., Chun, H. K., Jacklitsch, B., Jacobs, T., Kiefer, M., Lincoln, J., Pendergrass, S., Shire, J., et al. (2016). Advancing the framework for considering the effects of climate change on worker safety and health. Journal of Occupational and Environmental Hygiene, 13(11), 847–865. https://doi.org/10.1080/15459624.2016.1179388
   PubMed Web of Science ®Google Scholar
• Shabani, E., Hayati, B., Pishbahar, E., Ghorbani, M. A., & Ghahremanzadeh, M. (2021). A novel approach to predict CO2 emission in the agriculture sector of Iran based on Inclusive Multiple Model. Journal of Cleaner Production, 279(2021), 123708. https://doi.org/10.1016/j.jclepro.2020.123708
   Google Scholar
• Singh, V. K., Panda, K., Sagar, A., Al-Ansari, N., Duan, H. F., Paramaguru, P. K., Vishwakarma, D. K., Kumar, A., Kumar, D., Kashya, P. S., Singh, R. M., & Elbeltagi, A. (2022). Novel genetic algorithm (GA) based hybrid machine learning-pedotransfer function (ML-PTF) for prediction of spatial pattern of saturated hydraulic conductivity. Engineering Applications of Computational Fluid Mechanics, 16(1), 1082–1099. doi: 10.1080/19942060.2022.2071994
   Web of Science ®Google Scholar
• Smith, B. A., McClendon, R. W., & Hoogenboom, G. (2005). An enhanced artificial neural network for air temperature prediction IEC (Prague) 1:7–12.
   Google Scholar
• Sutskever, I., Hinton, G. E., & Taylor, G. W. (2009). The recurrent temporal restricted boltzmann machine (pp. 1601–1608). Advances in neural information processing systems.
   Google Scholar
• Taylor, K. E. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres, 106(D7), 7183–7192. https://doi.org/10.1029/2000JD900719
   Web of Science ®Google Scholar
• Voyant, C., Muselli, M., Paoli, C., & Nivet, M. L. (2012). Numerical weather prediction (nwp) and hybrid arma/ann model to predict global radiation. Energy, 39(1), 341–355. https://doi.org/10.1016/j.energy.2012.01.006
   Web of Science ®Google Scholar
• Xingjian, S., Chen, Z., Wang, H., Yeung, D. Y., Wong, W. K., & Woo, W. C. (2015). Convolutional lstm network: A machine learning approach for precipitation now casting, in: Advances in neural information processing systems, pp. 802–810.
   Google Scholar
• Yakut, E., & Süzülmüş, S. (2020). Modelling monthly mean air temperature using artificial neural network, adaptive neuro-fuzzy inference system and support vector regression methods: A case of study for Turkey. Network: Computation in Neural Systems, 31(1-4), 1–36. https://doi.org/10.1080/0954898X.2020.1759833
   PubMed Web of Science ®Google Scholar
• Yang, Q., Lee, C. Y., & Tippett, M. K. (2020). A long short-term memory model for global rapid intensification prediction. Weather and Forecasting, 35(4), 1203–1220. https://doi.org/10.1175/WAF-D-19-0199.1
   Web of Science ®Google Scholar
• Zhang, G., Band, S. S., Ardabili, S., Chau, K. W., & Mosavi, A. (2022). Integration of neural network and fuzzy logic decision making compared with bilayered neural network in the simulation of daily dew point temperature. Engineering Applications of Computational Fluid Mechanics, 16(1), 713–723. https://doi.org/10.1080/19942060.2022.2043187
   Web of Science ®Google Scholar
• Zhu, S., Hadzima-Nyarko, M., Gao, A., Wang, F., Wu, J., & Wu, S. (2019). Two hybrid data-driven models for modeling water-air temperature relationship in rivers. Environmental Science and Pollution Research, 26(12), 12622–12630. https://doi.org/10.1007/s11356-019-04716-y
   PubMed Web of Science ®Google Scholar