PREDICTING RAINFALL AND FORECAST WEATHER SENSITIVITY USING DATA MINING TECHNIQUES

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1 Volume 119 No , ISSN: (on-line version) url: ijpam.eu PREDICTING RAINFALL AND FORECAST WEATHER SENSITIVITY USING DATA MINING TECHNIQUES Aswini.R 1, Kamali.D 2, Jayalakshmi.S 3, R.Rajesh 4 1,2,3,4 Department Of CSE, IFET College of Engineering and Technology, Villupuram, India Abstract : A wide assortment of conjecture strategies are realistic in India, since India is a rural nation and the triumph of agribusiness depends of soil, precipitation and moistness. The present information mining framework is fit for assembly climate measurements from some climate stations and anticipating the dampness of the dirt for the following day progressively. Since it is non sensical, can't be precise amid the catastrophic events like flooding and so forth. Farmers face insecurity in their business from several factors, where rainfall is a key determinant for both the nature of the production system and in financial returns. We are utilizing the information digging strategies for determining the month to month Rainfall. This was done utilizing traditional factual strategy - Multiple Linear Regression. The datasets, for instance, most outrageous temperature, minimum temperature, weight, and wind bearing, relative moisture et cetera will complete the gauge of Rainfall using Multiple Linear Regression (MLR). Index Terms data mining technique, Multiple Linear Regression 1. Introduction Data mining technique is used to analysis the data and predicts the future trends. By using data mining techniques, predicts the rainfall for cultivate the crops. Cultivation of crops even through in heavy rainfall season, without affecting the fields. The main approaches to predicting the rainfall in India by using the Empirical and dynamical techniques. Based on the empirical approach the analysis of historical data is done. The most often used empirical approaches can predict the climate regression, Artificial Neural Network (ANN), Decision Tree algorithm, fuzzy logic and group of data handling. By using the dynamical approach, based on the physical models predictions are generated on the systems of the equations that predict the global climate system evolution in reply to the initial atmospheric environments. Data mining techniques are capably used in rainfall forecast. The Crop Prediction is an information providing application which contains data regarding various crops that can be cultivated. The application is supported by a database consist of the details about the crops. Add more crops to the database. 2. Related Work Some of the related papers that currently exist are given below i.e., Machine Learning Perspective for Prediction Agricultural Droughts this paper addresses the supervised learning mechanism and its one of the method Naive Bayesian Classification. Mainly Naive Bayesian classification performs large number of real world applications and it is to evaluate the parameters. Compared to other natural disasters to society may cause more losses due to drought and more numbers of people can be affected. For agricultural production and developments has increasingly severe threat can be frequent appearance of drought poses. Here monitoring of agricultural drought accurately done by integrating multisource information. It has more accurate and inclusive way in the Bayesian model attempts to specify drought. This model application using a range of methods and data, there is still some work to be done in the future research because of the complex spatial and temporal specify of drought. Another existing system is the Artificial Intelligence based on the expert system in Tomato crop. Nowadays major growing vegetable crop in the world is tomato. The main vegetable grown in the world is tomato. There are two parts in the Expert System i.e., Tomato Information System and Tomato Crop Expert System, where the user has to get all relevant information by the user. i.e., altered diseases, preventions, symptoms, annoyance, Virus of Tomato fruits and plants. The Advisory System having interaction with the expert system by user. The Expert System answered the questions from the user. It decides and displays its control measure of diseases depends on the acknowledgement given by the user through online. 3. Proposed Work In the proposed system, a Rainfall prediction is done by using empirical statistical technique. The datasets such as extreme temperature, minimum temperature, strain, wind direction, relative humidness etc and it will perform Rainfall prediction by using Multiple Linear Regression- MLR. This method divines the monthly rainfall quantity in summer monsoon condition (in mm). The rainfall amounts are planned to help the farmers in making decision regarding with their crop. The rainfall is one of 843

2 the causes of possible calamities similar to the floods and typhoons, forecasting the existence of rainfall will support us to prepare for the possible calamities. Separated Value (CVS) file format and the datasets were normalized to decrease the consequence of the scaling on the data. Data entry a framework is in place for data gathering and management. Initial reporting (feedback) systems have been created and are continuing. The development of a new method of interpretation and display for this data, continuous rainfall deficit / surplus analysis, reveals the relationship between local rainfall and the developing season (or other time period) in terms of likely boundary conditions and averages specific to the users property. The presentation of local spatial rainfall mapping gives the users an opportunity to explore (spatial) rainfall patterns that may exist on and around their property, once again based on their own rainfall and local conditions. The analysis can be performed for any season or time period, allowing examination of extreme events, wet or dry seasons and more average conditions. Data pre processing to test the usefulness of the rainfall data in a predictive or interpretive capacity, hence adding value to the rainfall records already held by many farmers. This analysis is also universally applicable, but provides information to allow users to interpret existing seasonal or regional forecast in more detail, specific to their property. A statistical approach, with multiple large data sets, is possibly a good option for dealing with the uncertainties in the rainfall systems Table 1 4. Experimental Results Learning and gathering in this stage, the data model was developed to resolve the missing data in a consistent format, it finding the duplicated data, and weeding out the bad data. As a final point, the cleaned data is then transformed into a proper format fit for the data mining. An exact low-quality data is accessible in numerous information bases and on the Net; various administrations are involved to transform the information into the cleaned forms which will be used in high-profit purposes. The goal is to generate an urgent need data analysis directed at cleaning the fresh information. In this stage the prediction, information associated to the study was determined on and recovered from the dataset. It was initially thought that years with similar rainfall values to the selected year year to date may have similar patterns for the residue of the year, and so provide the required analogue. A number of other criteria were selected to test the validity of this option, and possible improvements. Broadcasting is also noted as data consolidation. In this stage the selected information is transformed into format apt for data mining. The data file was saved in Commas According to the previous work done by researchers presented in the literature review, a comparison can be done. Several data mining techniques were used to predicting the different parameters of the weather like humidity, temperature and wind gust. Several attributes are used for the evaluation on application, authors, data mining techniques, algorithms, attributes, time period, dataset size, accuracy percentage, advantages and disadvantages. They yield different results with their cons and pros. For weather prediction, decision tree and k-mean clustering shows to be a good with a higher prediction accuracy than other techniques of data mining and it is proved By using the MATLAB Software Tool. Regression technique could not find correct value of prediction. However, estimated value could be retrieved. It is also observed that with the rise in dataset size, the exactness is first increases but then decreases after a certain extent. One of the reasons may be due to over appropriate the of training dataset. 844

3 The work done by different researchers and their evaluation is jotted down in Table.2. The above figure shows the accuracy of different data mining algorithms. Here, Decision Tree is individual the better accuracy to compare with the other algorithm. 5. Conclusion In the proposed system, the study of several general data mining algorithms is presented for a rainfall forecast. Data Mining deploys techniques based on the machine learning, alongside the conventional methods. More importantly, these techniques were generate the decision or prediction models, based on these historical data get from Tamilnadu government website. Based on this analysis BP is combined with various other algorithms. Recent algorithms analyzed in this work are Naïve Bayes, K- Nearest Neighbour algorithm, Decision Tree, ANFIS, ARIMA, SLIQ, Neural Network and fuzzy logic are some of the algorithms compared in this paper. A evaluation is made with which shows the decision trees and k-mean clustering are best suited data mining technique for this application. With the increase in size of training set, the accuracy is first increased but then decreased after a certain limit. References [1] Valmik B Nikam and B.B. Meshram, Modeling Rainfall Prediction using Data Mining Method, Fifth International Conference on Computational Intelligence, Modeling and Simulation, Issue No: , PP: , [2] Evangelos Tsagalidis and Georgios Evangelidis, The effect of Training Set Selection in Meteorological Datamining, 14th Pan-Hellenic Conference on Informatics, Volume: 3, Issue No: , PP: 61-65, [3] Mehmet Yesillbudak, Seref Sagiroglu and Ilhami Colak, A new approach to very short term wind speed prediction using K-nearest neighbour classification, energy conversion and management, volume- 69, PP: 77-86, [4] Pierre Julien Trombe and Pierre Pinson, Automatic classification of offshore wind regimes with weather radar observations, IEEE journal of selected topics in applied earth observations and remote sensing, volume: 07, No: 1, PP: , January [5] M.Iqbal, M.Azam, M.Naeem, A.S.Khwaja and Anpalagan, Optimization classification algorithms and tools for renewable energy: a review, renewable and sustainable energy reviews, volume: 39, PP: , [6] T.V. Rajini Kanth, Balaram, N.Rajashekar, Analysis of Data Sets using Data Mining Techniques, Computer science and technology, DOI: /esit , PP- No 89-94, [7] Kavita Pabreja, Research scholar, Clustering Technique to Interpret Numerical Weather Prediction Output Products for Forecast of Cloudburst, International Journal of Computer Science and Information Technologies (IJCSIT), Vol-3(1), ISSN: , PP , [8] Badhiye S.S, Dr.Chatur P.N, Wakode B.V, Temperature and Humidity Data Analysis for Future Value Prediction using clustering Technique: An Approach, International Journal of Emerging Technology and Advanced Engineering, ISSN: , volume-2, Issue-1, PP , January [9] Sarah N.Kohail, Alaa M.EL-Halees, Implementation of Data Mining Techniques for Meteorological Data Analysis, International Journal of Information and Communication Technology Research, ISSN: , Volume- 1, No: 3, PP: , July [10] Meghali A.Kalyankar, Prof.S.J. Alaspurkar, Data Mining Technique to Analyze the Meteorological Data, International Journal of Advanced Research in Computer science and Software Engineering, ISSN: X, Volume- 3, Issue: 2, PP: , February [11] Sanjay Chakra borty, Prof.N.K. Nagwani, Lopamudra dey, Weather Forecasting using Incremental K-means Clustering, International Conference in High Performance Architecture and Grid Computing, Vol.169, Part-2, PP: ,2011. [12] Marwa F.AI-Roby, Alaa M.El-Halees, Data Mining Techniques for Wind Speed Analysis, Journal of 845

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