Use of Appropriate Loss Function in Rainfall Prediction using Deep Learning
Vimal B. Patel1, R.D. Morena2
1Vimal B. Patel*, College of Agriculture, NAU, Waghai, Dangs, Gujarat, India.
2R. D. Morena, Department of Computer Science, Veer Narmad South Gujarat University, Surat, Gujarat, India.
Manuscript received on July 02, 2020. | Revised Manuscript received on July 10, 2020. | Manuscript published on August 30, 2020. | PP: 293-297 | Volume-9 Issue-6, August 2020. | Retrieval Number: D7515049420/2020©BEIESP | DOI: 10.35940/ijeat.D7515.089620
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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: India is an agricultural country, and rainfall is the main source of irrigation for agriculture. Prediction of rainfall is very crucial for farmers to make decisions. In this research paper, the prediction model has been developed through deep learning using historical data of 10 years of rainfall. A deep learning approach used Keras API with an artificial neural network technique to predict the daily rainfall. The prediction model has been assessed by four-loss function, i.e., MSE, MAE, Hinge, and Binary Cross-Entropy.
Keywords: ANN, deep learning, loss function, rainfall prediction.