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Application of Data Mining Techniques in Weather Prediction and Climate Change Studies

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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.
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... Artificial Neural Networks [ANNs] can be used to identify the relationships between all input variables and produce output based on observations. Hence using enough existing data, ANN's can obtain the relationships amongst weather parameters and make them use to guess conditions for future weather relating to future values of Evaporation, Speed of wind, Radiations, Temperature range and Rainfall when the Month along with Year is given [21] [29] Deep architecture have proven its worth over the shallow ones in various application areas and have been proven in this research [25]. Particularly, in the past few years, researchers have seen the wide acceptability of Convolutional Neural Networks (CNN) as a superior model for finding solutions to promising tasks in the domain of computer vision. ...
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Weather forecasting is the interesting area of research as so many living and survival conditions are heavily dependent on it in many living and non-living regions round the globe. Nowadays prediction of weather effecting human life in so many ways in different areas like Agriculture, Fishing, Military Surveillance and many more. In the current work various problems in the prediction of weather forecasting have been identified also existing Forecasting methods along with the parameters, domain and advantages, have been discussed such as machine learning based models, Artificial Neural Networks based models, Convolutional Neural Network, Deep neural network, Numerical weather prediction, support vector regression based models and Linear Regression model on the basis of Domain, Advantages and used parameters.
... The findings highlight the effectiveness of certain algorithms in predicting wind speed based on specific input variables. The study of [28] introduced a machine learning-based method that leverages both ANN and SVM to enhance the prediction accuracy of wind speed. By utilizing data from the Nigeria Meteorological Agency for 2016, the study presented a well-structured forecasting model that offers reliable predictions. ...
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... AI systems, through data assimilation and machine learning methods, can provide a reliable or best possible estimation of data, and it would be beneficial for assessing and evaluating the proposed or claimed data sets. Big data mining, cleaning and verification methods [56] would enable climate change managers to have a better understanding of free riders and climate disaster factors in different regions. ...
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... The specific mathematical form of the gating signals can be expressed as, h˜t = tanh Wh˜ · rt ht1, xt ht = (1 zt) ht1 + zt h˜t, (8) where ht1 is the state at time t1, xt and ht are the input and the output of the GRU module at the current time, respectively. The two gates are called as an update gate zt and a reset gate rt , which are zt = sigmoid (Wzxt + Uzht1 + bz) rt = sigmoid (Wr xt + Urht1 + br), (9) where Wz, Wr, Uz, Ur, bz, br are trainable weights. The output of the lth layer H(l) 2 in GRU can be written as H(l) 2 = GRU(H(l1) 2 ) , (10) In the third channel, we use FNN to extract the features that may be omitted by the other two channels, such as weather changes, event factors and supplemented spatiotemporal features, since these may be reflected in the data series. ...
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Weather forecasting [12] has been one of the most scientifically and technologically challenging problems around the world in the last century. This is due mainly to two factors: firstly, the great value of forecasting for many human activities; secondly, due to the opportunism created by the various technological advances that are directly related to this concrete research field, like the evolution of computation and the improvement in measurement systems. This paper describes several techniques belonging to the paradigm of artificial intelligence which try to make a short-term forecast of rainfalls (24 hours) over very spatially localized regions. The objective is to compare four different data-mining [1] methods for making a rainfall forecast [7], [10] for the next day using the data from a single weather station measurement.
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This is the third edition of the premier professional reference on the subject of data mining, expanding and updating the previous market leading edition. This was the first (and is still the best and most popular) of its kind. Combines sound theory with truly practical applications to prepare students for real-world challenges in data mining. Like the first and second editions, Data Mining: Concepts and Techniques, 3rd Edition equips professionals with a sound understanding of data mining principles and teaches proven methods for knowledge discovery in large corporate databases. The first and second editions also established itself as the market leader for courses in data mining, data analytics, and knowledge discovery. Revisions incorporate input from instructors, changes in the field, and new and important topics such as data warehouse and data cube technology, mining stream data, mining social networks, and mining spatial, multimedia and other complex data. This book begins with a conceptual introduction followed by a comprehensive and state-of-the-art coverage of concepts and techniques. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. Wherever possible, the authors raise and answer questions of utility, feasibility, optimization, and scalability. relational data. -- A comprehensive, practical look at the concepts and techniques you need to get the most out of real business data. -- Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning, -- Scores of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects. -- Complete classroom support for instructors as well as bonus content available at the companion website. A comprehensive and practical look at the concepts and techniques you need in the area of data mining and knowledge discovery.
A Decision Tree for Weather Prediction
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