International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 08 | Aug 2022
p-ISSN: 2395-0072
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CLOUD BURST FORECAST USING EXPERT SYSTEMS G. Bhuvaneswar Reddy1, J. Chethan2, Dr. M. Saravanamuthu3 1Student, Department
of Computer Applications, Madanapalle institute of technology and science, India of Computer Applications, Madanapalle institute of technology and science, India 3Asst. Professor, Department of Computer Applications, Madanapalle institute of technology and science, India ---------------------------------------------------------------------***--------------------------------------------------------------------2.LITERATURE REVIEW Abstract - Rainfall prediction is the one of the necessary 1Student, Department
strategies to predict the climatic prerequisites in any country. This paper proposes a rainfall prediction mannequin the usage of Multiple Linear Regression (MLR) for Indian dataset. The enter statistics is having a couple of meteorological parameters and to predict the rainfall in extra precise. The Mean Square Error (MSE), accuracy, correlation are the parameters used to validate the proposed model. From the results, the proposed computing device mastering mannequin gives higher outcomes than the different algorithms in the literature.
P. Goswami and Srividya [1] have mixed RNN and TDNN elements and conclusion of their work used to be that composite fashions offers higher accuracy than the single model. C. Venkatesan et al. [2] used Multilayer Feed Forward Neural Networks (MLFNN) for predicting Indian summer season monsoon rainfall. Error Back Propagation (EBP) algorithm is educated and utilized to predict the rainfall. Three community fashions with two, three and ten enter parameters have analyzed. They additionally in contrast the output end result with the statistical models. A.Sahai et al. [3] used error again propagation algorithm for Summer Monsoon Rainfall prediction of India on month-to-month and seasonal time series. They used facts of preceding 5 years of month-to-month and seasonal suggest rainfall values for rainfall prediction. N. Philip and K.Josheph [5] used ABF neural community for every year rainfall forecasting Kerala region. Their work suggests that ABFNN performs higher than the Fourier analysis. V. Somvanshi et al. [7] predictied rainfall of Hyderabad, INDIA place the usage of ANN model. They additionally in contrast ANN with ARIMA technique. They used previous 4 months rainfall facts as inputs to neural community model. S. Chattopadhyay and M. Chattopadhyay [9] have used two parameters minimal temperature and most temperature for rainfall forecasting. S. Chattopadhyaya and G. Chattopadhyaya [10] used Conjugate Gradient Decent (CGD) and Levenberg–Marquardt (LM) getting to know algorithm for training. Performances of each algorithms had been equal in prediction task. C. Wu et al. [12] expected the rainfall of India and China. They utilized Modular Artificial Neural Network (MANN). MANN’s overall performance was once in contrast with LR, K-NN and ANN. K. Htike and O. Khalifa [13] used yearly, biannually, quarterly and month-to-month rainfall statistics for rainfall prediction. They trained 4 one of a kind Focused Time Delay Neural Networks (FTDNN) for rainfall forecasting. Highest prediction accuracy was once supplied by means of the FTDNN mannequin when every year rainfall records is taken for training. S. Kannan and S. Ghosh [14] contributed in the direction of growing K- imply clustering method mixed with choice tree algorithm, CART, is used for rainfall states technology from massive scale atmospheric variables in a river basin. Rainfall country on day by day foundation is derived from the historic every day multi-site rainfall facts the usage of K-mean clustering. M. Kannan et al. [15]
Key Words:
Multiple Linear Regression, rainfall, prediction, machine learning, accuracy
1.INTRODUCTION Rainfall prediction is vital in Indian civilization, and it performs predominant position in human lifestyles to a highquality extent. It is disturbing accountability of meteorological branch to predict the frequency of rainfall with uncertainty. It is intricate to predict the rainfall precisely with altering climatic conditions. It is difficult to forecast the rainfall for each summertime and wet seasons. Researchers in all over the world have developed several fashions to predict the rain fall typically the use of random numbers and they are comparable to the local weather data. The proposed mannequin is developed the usage of more than one linear regression. The proposed technique makes use of Indian meteorological date to predict the rain fall. Usually, desktop mastering algorithms are labeled into two principal categories: (i) unsupervised studying (ii) supervised learning. All the clustering algorithms come beneath supervised computer learning. Figure 1 represents the special classification of desktop getting to know algorithms. Figure two describes the rainfall prediction lookup primarily based on neural community for Indian scenario. Even even though many fashions have developed, however it is quintessential for doing lookup the usage of laptop studying algorithms to get correct prediction. The error free prediction gives higher planning in the agriculture and different industries.
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