References
- Bauer P, Thorpe A, Brunet G. The quiet revolution of numerical weather prediction. Nature. 2015;525(7567):47–55. DOI:https://doi.org/10.1038/nature14956.
- Holmstrom M, Liu D, Vo C. Machine learning applied to weather forecasting; 2016.
- Alley RB, Emanuel KA, Zhang F. Advances in weather prediction. Science. 2019;363(6425):342–344. doi: https://doi.org/10.1126/science.aav7274
- Abrahamsen E, Brastein OM, Lie B. Machine learning in python for weather forecast based on freely available weather data. Linkping University Electronic Press; 2018. doi: https://doi.org/10.3384/ecp18153169
- Dueben PD, Bauer P. Challenges and design choices for global weather and climate models based on machine learning. Geosci Model Dev Discuss. 2018;1:17. DOI:https://doi.org/10.5194/gmd-2018-148.
- Lawrimore JH, Menne MJ, Gleason BE, et al. An overview of the global historical climatology network monthly mean temperature data set, version 3. J Geophys Res. 2011;116:D19. DOI:https://doi.org/10.1029/2011jd016187.
- NCDC. National climatic data center. NOAA’s National Centers for Environmental Information (NCEI). 2016; Available from: https://www.ncdc.noaa.gov/data-access/landbased-station-data/land-based-atasets.
- Sankaralingam BP, Sarangapani U, Thangavelu R. An efficient agro-meteorological model for evaluating and forecasting weather conditions using support vector machine. In: Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems. Springer; 2016. p. 65–75.
- Liu Y, Racah E, Correa J, et al. Application of deep convolutional neural networks for detecting extreme weather in climate datasets. arXiv preprint arXiv:160501156. 2016.
- Wu W, Li AD, He XH, et al. A comparison of support vector machines, artificial neural network and classification tree for identifying soil texture classes in southwest China. Comput Electron Agric. 2018;144(86):93.
- Doreswamy, GI, Manjunatha BR. Multi-label classification of big NCDC weather data using deep learning model. In: Soft computing systems. Singapore: Springer; 2018. p. 232–241. DOI:https://doi.org/10.1007/978-981-13-1936-5-26.
- Zekic-Susac M, Pfeifer S, Sarlija N. A comparison of machine learning methods in a high-dimensional classi_cation problem. Bus Syst Res J. 2014;5(3):82–96. DOI:https://doi.org/10.2478/bsrj-2014-0021.
- Breiman L, Friedman JH, Olshen RA, et al. Classification and regression trees. Routledge; 2017. doi: https://doi.org/10.1201/9781315139470
- Amin A, Al-Darwish N. Structural description to recognizing hand-printed arabic characters using decision tree learning techniques. Int J Comput Appl. 2006;28:2. DOI:https://doi.org/10.2316/journal.202.2006.2.202-1551.
- Breiman L. Random forests. Mach Learn. 2001;45(1):5–32. DOI:https://doi.org/10.1023/A:1010933404324.
- Wu S, Nagahashi H. Parameterized AdaBoost: introducing a parameter to speed up the training of real AdaBoost. IEEE Signal Process Lett. 2014;21(6):687–691. DOI:https://doi.org/10.1109/lsp.2014.2313570.
- Chaya JD, Usha RN. Predictive analysis by ensemble classier with machine learning models. Int J Comput Appl. 2019 Nov;1:8. DOI:https://doi.org/10.1080/1206212x.2019.1675019.
- Wang F, Zhen Z, Wang B, et al. Comparative study on KNN and SVM based weather classification models for day ahead short term solar PV power forecasting. Appl Sci. 2017;8(1):28. DOI:https://doi.org/10.3390/app8010028.
- Chauhan D, Thakur J. Data mining techniques for weather prediction: a review. Int J Rec Innov Trends Comput Commun. 2014;2:8.
- Naik AR, Pathan S. Weather classification and forecasting using back propagation feed forward neural network. Int J Sci Res Publ. 2012;2(12):1–3.
- Sawaitul SD, Wagh K, Chatur P. Classification and prediction of future weather by using back propagation algorithm-an approach. Int J Emerg Technol Adv Eng. 2012;2(1):110–113.
- Scher S, Messori G. Predicting weather forecast uncertainty with machine learning. Q J R Metereol Soc. 2018 Oct;144(717):2830–2841. DOI:https://doi.org/10.1002/qj.3410.
- Guerra JCV, Khanam Z, Ehsan S, et al. Weather classification: A new multi-class dataset, data augmentation approach and comprehensive evaluations of convolutional neural networks. In: 2018 NASA/ESA Conference on Adaptive Hardware and Systems (AHS). IEEE; 2018. p. 305–310. DOI:https://doi.org/10.1109/ahs.2018.8541482.
- Grover A, Kapoor A, Horvitz E. A deep hybrid model for weather forecasting. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – KDD. ACM Press; 2015. p. 379–386. DOI:https://doi.org/10.1145/2783258.2783275.
- Cramer S, Kampouridis M, Freitas AA, et al. An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives. Expert Syst Appl. 2017;85:169–181. DOI:https://doi.org/10.1016/j.eswa.2017.05.029.
- Hern_andez E, Sanchez-Anguix V, Julian V, et al. Rainfall prediction: A deep learning approach. In: Lecture notes in computer science. Springer International Publishing; 2016. p. 151–162. DOI:https://doi.org/10.1007/978-3-319-32034-213.
- Zhang M, Xu S, Fulcher J. Anser: adaptive neuron artificial neural network system for estimating rainfall. Int J Comput Appl. 2007;29:3. DOI:https://doi.org/10.2316/journal.202.2007.3.202-1585.
- Khodayar M, Wang J, Manthouri M. Interval deep generative neural network for wind speed forecasting. IEEE Trans Smart Grid. 2018;1:1. DOI:https://doi.org/10.1109/tsg.2018.2847223. doi: https://doi.org/10.1049/iet-stg.2018.0050
- Lu C, Lin D, Jia J, et al. Two-class weather classification. IEEE Trans Pattern Anal Mach Intell. 2017 Dec;39(12):2510–2524. doi: https://doi.org/10.1109/TPAMI.2016.2640295
- Hosahalli D, Gad I. A generic approach of _lling missing values in NCDC weather stations data. In: 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE; 2018. p. 143–149. DOI:https://doi.org/10.1109/icacci.2018.8554394.
- Bouktif S, Fiaz A, Ouni A, et al. Optimal deep learning LSTM model for electric load forecasting using feature selection and genetic algorithm: comparison with machine learning approaches y. Energies. 2018;11(7):1636. DOI:https://doi.org/10.3390/en11071636.
- Chakraborty C. Chronic wound image analysis by particle swarm optimization technique for tele-wound network. Wirel Pers Commun. 2017;96(3):3655–3671. DOI:https://doi.org/10.1007/s11277-017-4281-5.
- Sokolova M, Lapalme G. A systematic analysis of performance measures for classification tasks. Inf Process Manag. 2009;45(4):427–437. DOI:https://doi.org/10.1016/j.ipm.2009.03.002.