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- W4301431321 abstract "It is unlikely to have a big impact on a country's economic progress. It also helps to reduce the loss of life and property caused by natural disasters. The study of rainfall prediction using machine learning techniques, with a special emphasis on India. In India, about 70% of the population is dependent on agriculture and related activities. Rainfall forecasting has been an issue of major technical and economic importance in the agricultural sector. This rain prediction model is still mostly based on artificial neural networks, and it has only been used in India so far. In the present study, a comparative analysis of the two rainfall forecasting techniques, and is more accurate. In today’s technology, the ability to predict the rainfall is not very well-informed about the complexity of the data. The methods used are the methods of statistical and mathematical methods that don’t work if there is a situation of a nonlinear pattern. An existing installation may fail when the level of complexity of the information contained in it, increase it in the past. Now, this is the best way to get to the waiting; in the rain, it is a study of deep learning and neural networks, and genetic algorithms, is that it gives you more precise, it is used to predict the future. In order to come up with a good rain is a joint, an appraisal is not required. The simplest and easiest approach to get on a larger scale is to use weather forecasting. In the present study, it can be used in all types of weather stations, and the forecast will give access to some parts of the country. A technique for predicting month-to-month rain for a specific area in India using a deep CNN learning method as a replacement fortuning technique. The CNN was compared against an ANN." @default.
- W4301431321 created "2022-10-05" @default.
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- W4301431321 date "2022-10-06" @default.
- W4301431321 modified "2023-10-16" @default.
- W4301431321 title "A Nobel Approach to Identify the Rainfall Prediction Using Deep Convolutional Neural Networks Algorithm" @default.
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- W4301431321 doi "https://doi.org/10.1007/978-981-19-3575-6_42" @default.
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