Monthly rainfall forecasting by a hybrid neural network of discrete wavelet transform and deep learning Article Swipe

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YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.22541/au.163947881.10573287/v1

Actual rainfall forecast is critical to the management and allocation of water resources. In recent years, deep learning has been proved to be superior to traditional forecasting methods when predicting rainfall time series with high temporal and spatial variability. In this study, the discrete wavelet transform (DWT) and two typical deep learning approaches, namely long-short term memory (LSTM) and dilated causal convolutional neural network (DCCNN), are integrated innovatively and the hybrid model (DWT-CLSTM-DCCNN) is used for monthly rainfall forecasting for the first time. Monthly rainfall time series of four major cities in China (Beijing, Tianjin, Chongqing and Guangzhou) are used as the dataset of DWT-CLSTM-DCCNN. Firstly, two methods of sample construction are used to train DWT-CLSTM-DCCNN and their effects on the model performance are analyzed. Then, LSTM and the dilated causal convolutional network (DCCNN) are established as the benchmark models, and their forecast accuracy is compared with that of DWT-CLSTM-DCCNN. From the results of the evaluation criteria such as mean absolute error (MAE), root mean squared error (RMSE) and Nash-Sutcliffe model efficiency coefficient (NSE) as well as the fitting curve for forecasted rainfall, it can be concluded that the DWT-CLSTM-DCCNN developed in this study outperforms the benchmark models in model accuracy, peak and mutational rainfall capturing ability. Compared with the previous studies, DWT-CLSTM-DCCNN is proven to be better peak capture and more suitable for long-term rainfall forecasting.

Related Topics

Discrete Wavelet Transform

Benchmark (Surveying)

Computer Science

Convolutional Neural Network

Artificial Intelligence

Concepts

Discrete wavelet transform Benchmark (surveying) Mean squared error Artificial neural network Computer science Convolutional neural network Artificial intelligence Deep learning Series (stratigraphy) Rain gauge Wavelet transform Statistics Wavelet Mathematics Geography Geology Radar Paleontology Telecommunications Geodesy

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Type: preprint
Language: en
Landing Page: https://doi.org/10.22541/au.163947881.10573287/v1
PDF: https://www.authorea.com/doi/pdf/10.22541/au.163947881.10573287
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Cited By: 1
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DOI: https://doi.org/10.22541/au.163947881.10573287/v1

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Title: Monthly rainfall forecasting by a hybrid neural network of discrete wavelet transform and deep learning

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Type: preprint

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Language: en

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Publication year: 2021

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Publication date: 2021-12-14

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Authors: Xue‐yi You, Ming Wei

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Landing page: https://doi.org/10.22541/au.163947881.10573287/v1

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Concepts: Discrete wavelet transform, Benchmark (surveying), Mean squared error, Artificial neural network, Computer science, Convolutional neural network, Artificial intelligence, Deep learning, Series (stratigraphy), Rain gauge, Wavelet transform, Statistics, Wavelet, Mathematics, Geography, Geology, Radar, Paleontology, Telecommunications, Geodesy

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Cited by: 1

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abstract_inverted_index.DWT-CLSTM-DCCNN.	104, 149
abstract_inverted_index.(DWT-CLSTM-DCCNN)	72
cited_by_percentile_year.max	94
cited_by_percentile_year.min	89
countries_distinct_count	1
institutions_distinct_count	2
sustainable_development_goals[0].id	https://metadata.un.org/sdg/6
sustainable_development_goals[0].score	0.8299999833106995
sustainable_development_goals[0].display_name	Clean water and sanitation
citation_normalized_percentile.value	0.42479763
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	False