Estimating nature of atmosphere Using Data Mining Technique

IJCSNS International Journal of Computer Science and Network Security, 2019

In Meteorological field, where a huge database takes place; weather prediction is a vital process as it affects people's daily life. In the last century, the accuracy of weather predictions has been one of the most challenging concern facing meteorologists around the world. Atmospheric dust is considered to be a harmful air pollutant causing respiratory diseases and infections from one side as well as affecting the earth's energy budget from the other side, so an early prediction of dust phenomena occurrence can be very useful in reducing its harmful effects. Data mining is mainly a machine learning process for extracting useful information form extremely large data base as it is capable of handling huge, noisy, ambiguous, random and missing data, so it represents a very helpful tool in predicting different weather elements. The virtue of using data mining techniques is that they not only analyse the huge historical data base, but also learn from it for future predictions. In this work, we investigate the use of data mining techniques in forecasting different atmospheric phenomena specially atmospheric dust using Decision Tree, k-NN and Naïve biased algorithms as well as making a comparison between them by evaluating each model results. The proposed models are implemented using the open source data mining tool Rapidminer.

Asian Journal of Research in Computer Science, 2021

Weather forecasting is the process of predicting the status of the atmosphere for certain regions or locations by utilizing recent technology. Thousands of years ago, humans tried to foretell the weather state in some civilizations by studying the science of stars and astronomy. Realizing the weather conditions has a direct impact on many fields, such as commercial, agricultural, airlines, etc. With the recent development in technology, especially in the DM and machine learning techniques, many researchers proposed weather forecasting prediction systems based on data mining classification techniques. In this paper, we utilized neural networks, Naïve Bayes, random forest, and K-nearest neighbor algorithms to build weather forecasting prediction models. These models classify the unseen data instances to multiple class rain, fog, partly-cloudy day, clear-day and cloudy. These model performance for each algorithm has been trained and tested using synoptic data from the Kaggle website. T...

Data mining is the computer assisted process of digging through and analysing enormous sets of data and then extracting the meaningful data. Data mining tools predicts behaviours and future trends, allowing businesses to make proactive decisions. It can answer questions that traditionally were very time consuming to resolve. Therefore they can be used to predict meteorological data that is weather prediction. Weather prediction is a vital application in meteorology and has been one of the most scientifically and technologically challenging problems across the world in the last century. Predicting the weather is essential to help preparing for the best and the worst of the climate. Accurate Weather Prediction has been one of the most challenging problems around the world. Many weather predictions like rainfall prediction, thunderstorm prediction, predicting cloud conditions are major challenges for atmospheric research. This paper presents the review of Data Mining Techniques for Weather Prediction and studies the benefit of using it. The paper provides a survey of available literatures of some algorithms employed by different researchers to utilize various data mining techniques, for Weather Prediction. The work that has been done by various researchers in this field has been reviewed and compared in a tabular form. For weather prediction, decision tree and k-mean clustering proves to be good with higher prediction accuracy than other techniques of data mining.

— An application of data mining is a rich focus to Classification algorithm, Association algorithm, Clustering algorithm which can be applied to the field of various resources it concerns with developing methods that discover the knowledge from data origination. In this paper, focuses on meteorological data analysis in form of data mining is concerned to predict the knowledge of weather condition. Rainfall analysis, temperature analysis, based on climatic condition, cyclone form data analysis is vital application role for meteorological analysis in data mining techniques. Prediction, association and forecasting are the several method in data mining used for meteorological analysis. Many countries have already experienced deadly droughts and floods also climate-induced natural disasters have displaced hundreds of thousands of people across the world. Mainly due to over ambitious strategies and actions of human beings on the ecosystem , data mining play a significant role in determining the climate trends in crucial manner. In this research work is discussing the application of different data mining techniques applied in several ways to predict or to associate or to classify or to cluster the pattern of meteorological data. It can be provided for future direction for research.

Data Mining is a technology that facilitates extracting relevant and which have factors in common from the set of data. It is the process of analysis data from different perspectives and discovering problems, patterns, and correlations in data sets that are useful for predicting outcomes that help you make a correct decision. Weather Prediction is a field of meteorology that is created by collecting dynamic data related to the current state of the weather such as temperature, humidity, rainfall, wind. In this paper, we designed a system using a classification method by k-Nearest Neighbors algorithm for predict whether through previous data to determine the expected temperature and humidity the prediction results were compared with real results, the comparison was good and acceptable.

Data mining is the computer assisted process of digging through and analysing enormous sets of data and then extracting the meaningful data. Data mining tools predicts behaviours and future trends, allowing businesses to make proactive decisions. It can answer questions that traditionally were very time consuming to resolve. Therefore they can be used to predict meteorological data that is weather prediction. Weather prediction is a vital application in meteorology and has been one of the most scientifically and technologically challenging problems across the world in the last century. Predicting the weather is essential to help preparing for the best and the worst of the climate. Accurate Weather Prediction has been one of the most challenging problems around the world. Many weather predictions like rainfall prediction, thunderstorm prediction, predicting cloud conditions are major challenges for atmospheric research. This paper presents the review of Data Mining Techniques for Weather Prediction and studies the benefit of using it. The paper provides a survey of available literatures of some algorithms employed by different researchers to utilize various data mining techniques, for Weather Prediction. The work that has been done by various researchers in this field has been reviewed and compared in a tabular form. For weather prediction, decision tree and kmean clustering proves to be good with higher prediction accuracy than other techniques of data mining.

International Journal of Information Engineering and Electronic Business, 2012

Weather forecasting is a vital application in meteorology and has been one of the most scientifically and technologically challenging problems around the world in the last century. In this paper, we investigate the use of data mining techniques in forecasting maximum temperature, rainfall, evaporation and wind speed. This was carried out using Artificial Neural Network and Decision Tree algorithms and meteorological data collected between 2000 and 2009 from the city of Ibadan, Nigeria. A data model for the meteorological data was developed and this was used to train the classifier algorithms. The performances of these algorithms were compared using standard performance metrics, and the algorithm which gave the best results used to generate classification rules for the mean weather variables. A predictive Neural Network model was also developed for the weather prediction program and the results compared with actual weather data for the predicted periods. The results show that given enough case data, Data Mining techniques can be used for weather forecasting and climate change studies.

2018

Weather forecasting is a critical application in meteorology and has been a standout amongst the most logically and mechanically difficult issues the world over. In this paper, we research the utilization of data mining strategies in forecasting most extreme temperature and rainfall. Weather prediction approaches are tested by complex weather wonders with restricted perceptions and past data. Weather wonders have numerous parameters that are difficult to identify and measure. Expanding improvement on correspondence systems empowered weather forecast master systems to coordinate and offer assets and along these lines hybrid system has risen. Despite the fact that these upgrades on weather forecast, these master systems can't be completely dependable since weather forecast is primary issue.

International Journal of engineering Research and science, 2015

Weather forecasting is an important application in meteorology and has been one of the most scientifically and technologically challenging problems around the world. In this paper, we analyse the use of data mining techniques in forecasting weather. This can be carried out using Artificial Neural Network and Decision tree Algorithms and meteorological data collected in specific time. The performance of these algorithms was compared using standard performance metrics, and the algorithm which gave the best results used to generate classification rules for the mean weather variables. The results show that given enough case data mining techniques can be used for weather forecasting.

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2021

Weather Forecasting is the attempt to predict the weather conditions based on parameters such as temperature, wind, humidity and rainfall. These parameters will be considered for experimental analysis to give the desired results. Data used in this project has been collected from various government institution sites. The algorithm used to predict weather includes Neural Networks(NN), Random Forest, Classification and Regression tree (C &RT), Support Vector Machine, K-nearest neighbor. The correlation analysis of the parameters will help in predicting the future values. This web based application we will have its own chat bot where user can directly communicate about their query related to Weather Forecast and can have experience of two-way communication.