Abstract
Climate is the absolute most occasions that influence the human life in each measurement, running from nourishment to fly while then again it is the most tragic wonders. In this manner, expectation of climate wonders is of significant enthusiasm for human culture to keep away from or limit the devastation of climate risks. Climate forecast is unpredictable because of clamor and missing qualities dataset. Various endeavors were made to make climate forecast as precise as would be prudent, yet at the same time the complexities of commotion are influencing exactness. In this paper, the five-year rainfall record of weather is used for predicting the rainfall by calculating the performance and accuracy through 10 cross-fold validation technique. Its initial step is gathering, isolating, sorting, and detachment of datasets dependent on future vectors. Arrangement strategy has numerous calculations, some of them are Support Vector Machine (SVM), Naïve Bayes, Random Forest, and Decision Tree. Prior to the execution of each strategy, the model is made and afterward preparing of dataset has been made on that model. Learning the calculation created model must be fit for both the information dataset and estimate the records of class name. Various classifiers, for example, Linear SVM, Ensemble, Decision tree has been utilized and their precision and time broke down on the dataset. At last, all the calculation and results have been determined and analyzed in the terms of accuracy and execution time.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Borges, L.R.: Analysis of the wiscons in breast cancer dataset and machine learning for breast cancer detection. In: Proceedings of XI Workshop de Visão Computational, 05th–07th October 2015, pp. 15–19 (2015)
Brownlee, J.: Logistic regression for machine learning. Machine Learning Mastery. https://machinelearningmastery.com/logistic-regression-for-machine-learning. Accessed 1 Apr 2016
Brownlee, J.: Support vector machine for machine learning. Machine Learning Mastery. https://machinelearningmastery.com/support-vector-machines-for-machine-learning/. Accessed 1 Apr 2016
Brownlee, J.: Machine Learning Mastery (2018). https://machinelearningmastery.com/k-fold-cross-validation
Cortez, P.: Using data mining for wine quality assessment (2010)
Vijayarani, M.S.: Liver disease prediction using SVM and Naive Bayes. Int. J. Sci. Eng. Technol. Res. (IJSETR) 4(4), 816-820 (2015)
Olaniyi, E.O.A.: Liver disease diagnosis based on neural networks. In: Advances in Computational Intelligence, pp. 48–53 (2015)
Woods, W.P.K.: Combination of multiple classifiers using local accuracy estimates. IEEE Trans. Pattern Anal. Mach. Intell. 19(4), 405–410 (1997)
Zhou, C., Cule, B., Goethals, B.: Pattern based sequence classification. IEEE Trans. Knowl. Data Eng. 28(5), 1285–1298 (2016)
Quinan, P.S., Meyer, M.: Visually comparing weather features in forecasts. IEEE Trans. Vis. Comput. Graph. 22(1), 389–398 (2016)
Sheena Angra, S.A.: Machine learning and it’s applications: a review. In: 2017 International Conference on Big data and Computational Intelligence, pp. 57–60 (2017)
Ahmed, F., et al.: Wireless mesh network IEEE 802.11 s. Int. J. Comput. Sci. Inf. Secur. 14(12), 803–809 (2016)
Aslam, N., Sarwar, N., Batool, A.: Designing a model for improving CPU scheduling by using machine learning. Int. J. Comput. Sci. Inf. Secur. 14(10), 201 (2016)
Bilal, M., Sarwar, N., Saeed, M.S.: A hybrid test case model for medium scale web based applications. In: 2016 Sixth International Conference on Innovative Computing Technology (INTECH), pp. 632–637 (2016)
Bajwa, I.S., Sarwar, N.: Automated generation of express-g models using NLP. Sindh Univ. Res. J. -SURJ (Sci. Ser.) 48(1), 5–12 (2016
Cheema, S.M., Sarwar, N., Yousaf, F.: Contrastive analysis of bubble & merge sort proposing hybrid approach. In: 2016 Sixth International Conference on Innovative Computing Technology (INTECH), pp. 371–375 (2016)
Sarwar, N., Latif, M.S., Aslam, N., Batool, A.: Automated object role model generation. Int. J. Comput. Sci. Inf. Secur. 14(9), 301–308 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sarwar, N. et al. (2020). Prediction and Analysis of Sun Shower Using Machine Learning. In: Bajwa, I., Sibalija, T., Jawawi, D. (eds) Intelligent Technologies and Applications. INTAP 2019. Communications in Computer and Information Science, vol 1198. Springer, Singapore. https://doi.org/10.1007/978-981-15-5232-8_16
Download citation
DOI: https://doi.org/10.1007/978-981-15-5232-8_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5231-1
Online ISBN: 978-981-15-5232-8
eBook Packages: Computer ScienceComputer Science (R0)