Rainfall Prediction using Neural Net based Frequency Analysis Approach

Abstract

Rainfall prediction is very complex hydrologic process and is important as it holds the key to any countries' economy. Proposed model presents a new approach for yearly rainfall prediction of 30 Indian subdivisions. Yearly rainfall data of the Indian subdivision is available from IITM, Pune. The combination of Fast Fourier Transform (FFT) and Feed Forward Neural Network (FFNN) is applied for next one year rainfall prediction. Fast Fourier transform with filtering is performed on interpolated rainfall data to separate periodic components. These periodic components and delayed periodic components are given as input and desired output respectively to an FFNN for training. While testing the output of FFNN, inverse FFT gives the predicted rainfall value by amount of training input-output delay. This model is tested with 140 year's Indian subdivisions rainfall data. The experimental results of 30 subdivisions show that next one year rainfall prediction accuracy is above 92%.

Performance Analysis of Multilayer Perceptron Neural Network Models in Week-Ahead Rainfall Forecasting

“…The training algorithm is the parameter that tunes the network so that its outputs are close to the desired values [4,10,19]. Among the function approximation algorithms, most of the studies in rainfall forecasting use Backpropagation, Resilient Propagation and Quick Propagation as their training algorithms [1,4,7,8,18]. After evaluating more researches and looking at related studies, the researchers were able to identify two additional function approximation algorithms aside from the three mentioned that were suited for rainfall forecasting namely: Scaled Conjugate Gradient and Levenberg-Marquardt [7,12,19].…”

“…Among the function approximation algorithms, most of the studies in rainfall forecasting use Backpropagation, Resilient Propagation and Quick Propagation as their training algorithms [1,4,7,8,18]. After evaluating more researches and looking at related studies, the researchers were able to identify two additional function approximation algorithms aside from the three mentioned that were suited for rainfall forecasting namely: Scaled Conjugate Gradient and Levenberg-Marquardt [7,12,19]. A total of five training algorithms, namely, Backpropagation, Resilient Propagation, Quick propagation, Scaled Conjugate Gradient, and Levenberg-Marquardt were used.…”

“…Results showed that most of Gaussian and Sin models did not reach the maximum error so these models will not be included in the testing phase while most of Sigmoid and Hyperbolic Tangent models except Models 5 & 10 with Levenberg-Marquardt training algorithm reached a maximum error. A study which had almost similar setup trained an MLPNN with Sigmoid activation function using 50 hidden neurons and found out that although 50 hidden neurons was faster to learn, the model produces a smooth curve with more error, thus increasing the number of hidden neurons to 300 solved that problem [7]. During the testing phase, models that reached maximum error were used.…”

“…For this reason, MLPNN have been successfully used by most of the researchers in the field of forecasting, science and engineering to predict the behavior of both linear and nonlinear systems without the need to make assumptions that are implicit in most traditional statistical approaches [2,4,5,6]. With all its promising results, the biggest challenge with MLPNN is the selection of an appropriate model since there are different MLPNN model structures, training algorithms, activation functions, learning rate, momentum and number of epochs to choose from [1,7]. This makes it hard to find the proper model for a particular problem [4].…”

“…Out of the historical data collected from various rain gauges, MLPNN models show great potential in discovering patterns from preprocessed data which in turn forecast rainfall used for life-saving applications such as flood management and airport administration. The nature of the combination of meteorological parameters such as relative humidity, air pressure, wet bulb temperature, cloudiness, and rainfall at the point of measurement as well as from surrounding stations poses challenges in data preparation as well as in the input and hidden layers of MLPNN models [7,8]. Furthermore, in the output layers of MLPNN applications for rainfall forecasting, modelers usually generate week-ahead forecast to give ample time for decision makers in the dissemination of disaster preparedness measures to the affected stakeholders [3].…”