METEOROLOGICAL FORBODE USING ADABOOST ALGORITHM

Computer Engineering

Owing to the increasing evidence of the climatic changes worldwide is becoming the reason to understand a lot more about the weather like what’s going to happen tomorrow or the next day. To forecast the weather, we need to analyse a large set of data’s, therefore use of big data in climatic predictions will provide accurate predicting of seasonal weathering. Big Data is a field to analyse systematically to extract information’s from data sets. In enduring system, a concept of data mining called Linear Regression is used to predict the climate, it doesn’t have a solid software that recovers from a component failure and also leads to immense output fields and consuming vast compute and storage resource. The planned system explored various application domains that could benefit from weather forecasting using big data and ADABOOST Algorithm. The algorithm is a supervised learning that uses both classification and regression challenges with high accuracy and less computation power. WEKA tool is implemented which uses a GUI software and sanctioned with GNU General Public License. It is kind of Create and Load Database with IA-32, X86-64, Java SE platforms. Data volumes and variety are growing at very fast rate and this is becoming a great challenge in weather forecasting, as now difficulty is to mix these data to provide correct forecast. Some examples of these domains include Forecasting solar power for Utility operations, large scale crop production forecasts for global food security, in precision agriculture for future farming and space weather forecasting. In order to know how these applications could impact normal operations this project defines various climate predictions and challenges. In future work, day to day predictions should be implemented with the help of API’s and accuracy should be added for more efficient purposes.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.