Performance evaluation of a simple feed-forward deep neural network model applied to annual rainfall anomaly index (RAI) over Indramayu, Indonesia

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Sandy Hardian Susanto Herho , Dasapta Erwin Irawan, Faiz Rohman Fajary, Rusmawan Suwarman, Siti Nurzannah Kaban


Indramayu is a district in West Java that is known for being the leading producer of rice and brackish salt. The production of these two commodities is strongly influenced by hydroclimatological conditions, making accurate and reliable long-term estimates crucial. In this study, we evaluated a simple feed-forward deep neural network (DNN) model that could potentially be used as a candidate for statistical guidance to improve the accuracy of numerical climate models.

We used the spatial average of the accumulated annual rainfall of the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data as an input time series with a time range from 1981 to 2022. This data was then processed into annual rainfall anomaly index (RAI) data. The Annual RAI was divided into training and test sets, and the feed-forward DNN model was fitted to the annual RAI in the training set. The accuracy of the model was then tested in the test set using the root-mean-square error (RMSE) metric.

Our study shows that the feed-forward DNN model is not suitable for estimating the annual RAI over Indramayu. This is because the RMSE values are significantly high in both the training and test sets.



Climate, Hydrology, Physical Sciences and Mathematics, Statistical Models, Statistics and Probability, Sustainability, Water Resource Management


Deep learning, drought, Indramayu, feed-forward neural networks, RAI


Published: 2023-06-30 00:06

Last Updated: 2023-06-30 07:06


CC BY Attribution 4.0 International

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