# Time Series Data Mining in Rainfall Forecasting Using Artificial Neural Network

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3 result is already known to the network. We then calculate the error of each neuron, which is computed desired output. This error is then back propagate to change the weights in such a way that the error will get smaller. In order for the hidden layer to serve any useful function, multilayer networks must have nonlinear activation functions for the multiple layers. Back propagation algorithm requires that the activation function used by the artificial neurons be differentiable. This algorithm operates in either of the 2 modes: Incremental mode in which each propagation is followed immediately by the weight adjustment and batch mode in which the weight updates take place after various consecutive propagations ALGORITHM: 1) Randomly initialize the weights in the network. 2) Apply the input to the network and obtained computed output 3) Calculate the error (e) e = desired-computed 4) Calculate the Δw i for all weights in backward pass from hidden layer to output layer. 5) Calculate the ΔW i for all weights in backward pass from input layer to hidden layer. 6) Update the weights in the network 7) Repeat the step 2 to 6 for every training pattern until all pattern classified correctly. A flowchart of methodology has been drawn. 3.3 Design of data set: Hoshangabad is located at N E. An average height from the sea level is 331 meters and average rain fall is 134 cms. Discharge and rainfall data is collected for Hoshangabad site from Central Water Commission, Ministry of Water Resources India. The data set is divided in three subsets : Training, validation and testing sets. Input data contain four months (June to September) rainfall and discharge mean data of year s The data set contain 43 exemplar MSE NMSE MAE Min Abs Error Max Abs Error r for the year s 2001 to In this study, 75% of data is used for training, 15% of data is used for validation and 10% data is used for testing. 4.Results: In this study, discharge and rainfall data of year were used to trained the network. Rainfall and discharge data as an input parameter and next year rainfall data as a desired output parameter. Various experiments were perform on network s structure and algorithm by change in number of hidden layer, number of neurons, learning algorithm and activation function. After performing all experiments, the model with 2 hidden layer along with online update method for updating weight is used with momentum learning rule, found optimum for this research work. Fig4 shows the result obtained from training, cross validation and testing phase of the model. MSE NMSE MAE Min Abs Error Max Abs Error r Fig4(a). Desired versus actual network output at training stage and error measures Fig 3 : Flow chart of process Page 1062

4 mean absolute error as compare to linear regression for rainfall forecast in table 1. This is significant improvement over the current forecasts and yields a good model for producing future forecasts. This result clearly indicating that artificial neural network approach is a more convincing and relatively predicting more accurate simulation results than linear regression. our most promising line of future work is to apply our methods on other Narmada Basin Sites in Madhya Pradesh. MSE NMSE MAE Min Abs Error Max Abs Error r Fig4(b). Desired versus actual network output at cross validation stage and error measures 7.Acknowlegement: The authors would like to thank the Central Water Commission, Ministry of Water Resources, India for providing Water Level and Discharge data. References: i. Abhishek, K., Kumar, A., Ranjan, R., and Kumar, S. (2012), A Rainfall Prediction Model using Artificial Neural Network, IEEE ii. Behzad M., Asghari K., Eazi M. and Palhang M., Generalized performance of SVM and NN in runoff modelling, 2009, ELSEVIER SCIENCES, Expert System with Application, Vol. 36, Issue 4, pp iii. Burbridge Robert and Buxton Bernard, An Introduction to Support Vector Machines for Data Mining, UCL, Gower Street, WC1E 6BT, UK. MSE NMSE MAE Min Abs Error Max Abs Error r Fig4(c) Desired versus actual network output at testing stage and error measures. 5.performance Evaluataion and Comparision: To measure the performance of this research, statistics of correlation coefficient, Mean absolute error (MAE), Mean Square Error (MSE) are used. If value of Correlation coefficient is more, than it shows strong correlation between the parameter. This study compared four MLP neural network model with two linear regression models to find the best model for rainfall forecasting. The best model has minimum MSE and MAE. MLP-2-O-M (multilayer perceptron) found the best model among others model. It has minimum MSE, MAE and maximum correlation coefficient value. Table 1 shows the summary of all network performance. 6.Conclusion and Future Work: Current results using MLP with a Backpropagation yield a low mean square error and iv. Ingsrisawang L., Ingsrisawang S., Somchit S., Aungsuratana P. and Khantiyanan W. (2008), Machine Learning techniques For Short-Term rain forecasting System in the northeastern part of Thailand, World Academy of Science, Engineering and Technology, vol:2 v. Kumar, R., and Yadav, G. S. (2013), Forecasting of Rain Fall in Varanasi District, Uttar Pradesh Using Artificial Neural Network, JECET. vi. Mahdizadeh, M.B., (2004), Artificial Neural Networks and their Application in Civil Engineering,!st Edn., Ebady publication, iran, ISBN: ,pp:192 vii. McCullagh, J., Bluff and E. Ebert, (1995), A neural network model for rainfall estimation, artificial neural network and expert system, proceeding of 2 nd New Zealand International two stream conference on artificial neural networks and expert system. viii. Mishra S., Majumder S., and Dwivedi V.K., pattern discovery in hydrological time series data mining in a Sustainable Water resources Management And Climate Change Adaptation, Vol.- II, pp , February 17-19, 2011, NIT Durgapur. ix. Mishra S., Dwivedi V.K., Sarvanan C. and Pathak K. K., Pattern Discovery in Hydrological Time Series Data Mining during the Monsoon Period of the High Flood Years in Brahmaputra River Basin, IJCA( ), doi / , Vol. 67, No.6, pp.7-14,april, x. Mishra S., Sarvanan C., Dwivedi V.K., and Pathak K. K., Discovering Flood Rising Pattern in Hydrological Time Series Data Mining during the Pre Monsoon Period, Indian. Journal of Geo-Marine Science, Accepted on 12/01/2014. xi. Mutlu, E., Chaubey, I., Hexmoor, H., and Bajwa, S. G. (2008), Comparison of artificial neural network models for hydrologic Page 1063

5 predictions at multiple gauging stations in an agricultural watershed, Hydological Process. xii. Shamseldin, A. Y., O'CONNOR, K. M., and Nasr, A.E. (2007), A comparative study of three neural network forecast combination methods for simulated river flows of different rainfall runoff models, Hydrological Sciences Journal. xiii. Wang, Z. L., and Sheng, H. H. (2010), Rainfall Prediction Using Generalized Regression Neural Network: Case study Zhengzhou,IEEE Table 1: Summary of all networks Summary of All Networks Metrics Training Cross Validation Testing Model Name MSE r MAE MSE r MAE MSE r MAE MLP-1-O-M (Multilayer Perceptron) LR-0-B-M (Linear Regression) LR-0-B-L (Linear Regression) MLP-1-B-M (Multilayer Perceptron) MLP-2-O-M (Multilayer Perceptron) MLP-2-B-M (Multilayer Perceptron) Page 1064

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