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Development and validation of a deep neural network for predicting SPI of Nakdong river basin
Journal of the Korean Data & Information Science Society 2019;30:1277-87
Published online November 30, 2019;  https://doi.org/10.7465/jkdi.2019.30.6.1277
© 2019 Korean Data and Information Science Society.

Youngtae Choi1 · Kyeong Eun Lee2 · Gwangseob Kim3

12Department of Statistics, Kyungpook National University
3Department of Civil Engineering, Kyungpook National University
Correspondence to: Professor, Department of Civil Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Korea. E-mail: kimgs@knu.ac.kr

This work was supported by Korea Environmet Industry & Technology Institute (KEITI) through Advanced Water Management Research Program, funded by Korea Ministry of Environment (Grant. 83067).
Received October 31, 2019; Revised November 15, 2019; Accepted November 15, 2019.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
In this study, we applied a kind of deep neural network, a multilayer perceptron (MLP), to predict SPI6 of Nakdong River Basin. The monthly standard precipitation indices, temperature, temperature normal value, precipitation, normal precipitation, precipitation date, and various global climate indexes are used as explanatory variables. To find the optimal model, we consider two active functions, four types of the neurons per a hidden layer, five types of dropout rates, five types of the number of hidden layers, and two loss functions. We use the validation mean square error for the evaluation. The validation mean square error was always lower in the model with (L1 normalization than in the model without the normalization. In most areas, the smaller the number of hidden layers, the lower the validation mean square error.
Keywords : Deep neural network, L1 regularization, Mean square error, Multilayer perceptron, Neural network, SPI6, Validation dataset.