Abstract
Weather forecasting is critical for, for example, the operation of hydroelectric power plants and flood control. Mechanical structures are considered to be challenging in computational terms. Developing models can predict weather conditions quicker than conventional meteorological models which is therefore of interest. The Field of Machine Learning has generated a great deal of attention from the scientific community. It is our aim to research whether an artificial neural network has a good potential for forecasting weather conditions in addition of large datasets, due to its applicability in a number of fields. It is a benefit to have meteorological data available from through online outlets.
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Selvi, S., Bala Nivetha, V., Divya, R., Gayathri, S., Pavithra, G.S. (2021). Change Point Detection Technique for Weather Forecasting Using Bi-LSTM and 1D-CNN Algorithm. In: Kumar, R., Mishra, B.K., Pattnaik, P.K. (eds) Next Generation of Internet of Things. Lecture Notes in Networks and Systems, vol 201. Springer, Singapore. https://doi.org/10.1007/978-981-16-0666-3_11
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DOI: https://doi.org/10.1007/978-981-16-0666-3_11
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