Soft Computing Research For Weather Prediction Using Multilayer Architecture
S. Sheik Mohideen Shah1, S. Meganathan2, A. Kamali3
1S. Sheik Mohideen Shah, Dept. of Computer Science & Engineering, SRC, Sastra Deemed University, Thanjavur, (Tamil Nadu), India.
2S. Meganathan, Dept. of Computer Science & Engineering, SRC, Sastra Deemed University, Thanjavur, (Tamil Nadu), India.
3A. Kamali, Dept. of Computer Science & Engineering, SRC, Sastra Deemed University, Thanjavur, (Tamil Nadu), India.
Manuscript received on February 03, 2019. | Revised Manuscript received on February 14, 2019. | Manuscript published on August 30, 2019. | PP: 3779-3783 | Volume-8 Issue-6, August 2019. | Retrieval Number: F9390088619/19©BEIESP | DOI: 10.35940/ijeat.F9390.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Rainfall prediction is helpful for the agriculture sector. Early prediction of drought and torrent situations is achieved through time series data. For the precise prediction, Artificial Neural Network(ANN) technique is used. The rainy dataset is tested using Feed Forward Neural Network(FFNN). The performance of this model is evaluated using Mean Square Error(MSE) and Magnitude of Relative Error(MRE). Better performance achieved when compared with other data mining techniques.
Keywords: Artificial Neural Network, Multi-Layer Perceptron, Time-Series Data.