Medium-Term Rainfall Forecasts Using Artificial Neural Networks with Monte-Carlo Cross-Validation and Aggregation for the Han River Basin, Korea
In this study, artificial neural network (ANN) models were constructed to predict the rainfall during May and June for the Han River basin, South Korea. This was achieved using the lagged global climate indices and historical rainfall data. Monte-Carlo cross-validation and aggregation (MCCVA) was ap...
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ftdoajarticles:oai:doaj.org/article:050011d3d3f8472eb6450a83fedfa716 2023-05-15T15:17:29+02:00 Medium-Term Rainfall Forecasts Using Artificial Neural Networks with Monte-Carlo Cross-Validation and Aggregation for the Han River Basin, Korea Jeongwoo Lee Chul-Gyum Kim Jeong Eun Lee Nam Won Kim Hyeonjun Kim 2020-06-01T00:00:00Z https://doi.org/10.3390/w12061743 https://doaj.org/article/050011d3d3f8472eb6450a83fedfa716 EN eng MDPI AG https://www.mdpi.com/2073-4441/12/6/1743 https://doaj.org/toc/2073-4441 doi:10.3390/w12061743 2073-4441 https://doaj.org/article/050011d3d3f8472eb6450a83fedfa716 Water, Vol 12, Iss 1743, p 1743 (2020) medium-term rainfall forecast artificial neural network Monte-Carlo cross-validation and aggregation prediction uncertainty interval Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 article 2020 ftdoajarticles https://doi.org/10.3390/w12061743 2022-12-31T03:36:59Z In this study, artificial neural network (ANN) models were constructed to predict the rainfall during May and June for the Han River basin, South Korea. This was achieved using the lagged global climate indices and historical rainfall data. Monte-Carlo cross-validation and aggregation (MCCVA) was applied to create an ensemble of forecasts. The input-output patterns were randomly divided into training, validation, and test datasets. This was done 100 times to achieve diverse data splitting. In each data splitting, ANN training was repeated 100 times using randomly assigned initial weight vectors of the network to construct 10,000 prediction ensembles and estimate their prediction uncertainty interval. The optimal ANN model that was used to forecast the monthly rainfall in May had 11 input variables of the lagged climate indices such as the Arctic Oscillation (AO), East Atlantic/Western Russia Pattern (EAWR), Polar/Eurasia Pattern (POL), Quasi-Biennial Oscillation (QBO), Sahel Precipitation Index (SPI), and Western Pacific Index (WP). The ensemble of the rainfall forecasts exhibited the values of the averaged root mean squared error (RMSE) of 27.4, 33.6, and 39.5 mm, and the averaged correlation coefficient (CC) of 0.809, 0.725, and 0.641 for the training, validation, and test sets, respectively. The estimated uncertainty band has covered 58.5% of observed rainfall data with an average band width of 50.0 mm, exhibiting acceptable results. The ANN forecasting model for June has 9 input variables, which differed from May, of the Atlantic Meridional Mode (AMM), East Pacific/North Pacific Oscillation (EPNP), North Atlantic Oscillation (NAO), Scandinavia Pattern (SCAND), Equatorial Eastern Pacific SLP (SLP_EEP), and POL. The averaged RMSE values are 39.5, 46.1, and 62.1 mm, and the averaged CC values are 0.853, 0.771, and 0.683 for the training, validation, and test sets, respectively. The estimated uncertainty band for June rainfall forecasts generally has a coverage of 67.9% with an average band width of 83.0 mm. It ... Article in Journal/Newspaper Arctic North Atlantic North Atlantic oscillation Directory of Open Access Journals: DOAJ Articles Arctic Pacific Water 12 6 1743 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
medium-term rainfall forecast artificial neural network Monte-Carlo cross-validation and aggregation prediction uncertainty interval Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 |
spellingShingle |
medium-term rainfall forecast artificial neural network Monte-Carlo cross-validation and aggregation prediction uncertainty interval Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 Jeongwoo Lee Chul-Gyum Kim Jeong Eun Lee Nam Won Kim Hyeonjun Kim Medium-Term Rainfall Forecasts Using Artificial Neural Networks with Monte-Carlo Cross-Validation and Aggregation for the Han River Basin, Korea |
topic_facet |
medium-term rainfall forecast artificial neural network Monte-Carlo cross-validation and aggregation prediction uncertainty interval Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 |
description |
In this study, artificial neural network (ANN) models were constructed to predict the rainfall during May and June for the Han River basin, South Korea. This was achieved using the lagged global climate indices and historical rainfall data. Monte-Carlo cross-validation and aggregation (MCCVA) was applied to create an ensemble of forecasts. The input-output patterns were randomly divided into training, validation, and test datasets. This was done 100 times to achieve diverse data splitting. In each data splitting, ANN training was repeated 100 times using randomly assigned initial weight vectors of the network to construct 10,000 prediction ensembles and estimate their prediction uncertainty interval. The optimal ANN model that was used to forecast the monthly rainfall in May had 11 input variables of the lagged climate indices such as the Arctic Oscillation (AO), East Atlantic/Western Russia Pattern (EAWR), Polar/Eurasia Pattern (POL), Quasi-Biennial Oscillation (QBO), Sahel Precipitation Index (SPI), and Western Pacific Index (WP). The ensemble of the rainfall forecasts exhibited the values of the averaged root mean squared error (RMSE) of 27.4, 33.6, and 39.5 mm, and the averaged correlation coefficient (CC) of 0.809, 0.725, and 0.641 for the training, validation, and test sets, respectively. The estimated uncertainty band has covered 58.5% of observed rainfall data with an average band width of 50.0 mm, exhibiting acceptable results. The ANN forecasting model for June has 9 input variables, which differed from May, of the Atlantic Meridional Mode (AMM), East Pacific/North Pacific Oscillation (EPNP), North Atlantic Oscillation (NAO), Scandinavia Pattern (SCAND), Equatorial Eastern Pacific SLP (SLP_EEP), and POL. The averaged RMSE values are 39.5, 46.1, and 62.1 mm, and the averaged CC values are 0.853, 0.771, and 0.683 for the training, validation, and test sets, respectively. The estimated uncertainty band for June rainfall forecasts generally has a coverage of 67.9% with an average band width of 83.0 mm. It ... |
format |
Article in Journal/Newspaper |
author |
Jeongwoo Lee Chul-Gyum Kim Jeong Eun Lee Nam Won Kim Hyeonjun Kim |
author_facet |
Jeongwoo Lee Chul-Gyum Kim Jeong Eun Lee Nam Won Kim Hyeonjun Kim |
author_sort |
Jeongwoo Lee |
title |
Medium-Term Rainfall Forecasts Using Artificial Neural Networks with Monte-Carlo Cross-Validation and Aggregation for the Han River Basin, Korea |
title_short |
Medium-Term Rainfall Forecasts Using Artificial Neural Networks with Monte-Carlo Cross-Validation and Aggregation for the Han River Basin, Korea |
title_full |
Medium-Term Rainfall Forecasts Using Artificial Neural Networks with Monte-Carlo Cross-Validation and Aggregation for the Han River Basin, Korea |
title_fullStr |
Medium-Term Rainfall Forecasts Using Artificial Neural Networks with Monte-Carlo Cross-Validation and Aggregation for the Han River Basin, Korea |
title_full_unstemmed |
Medium-Term Rainfall Forecasts Using Artificial Neural Networks with Monte-Carlo Cross-Validation and Aggregation for the Han River Basin, Korea |
title_sort |
medium-term rainfall forecasts using artificial neural networks with monte-carlo cross-validation and aggregation for the han river basin, korea |
publisher |
MDPI AG |
publishDate |
2020 |
url |
https://doi.org/10.3390/w12061743 https://doaj.org/article/050011d3d3f8472eb6450a83fedfa716 |
geographic |
Arctic Pacific |
geographic_facet |
Arctic Pacific |
genre |
Arctic North Atlantic North Atlantic oscillation |
genre_facet |
Arctic North Atlantic North Atlantic oscillation |
op_source |
Water, Vol 12, Iss 1743, p 1743 (2020) |
op_relation |
https://www.mdpi.com/2073-4441/12/6/1743 https://doaj.org/toc/2073-4441 doi:10.3390/w12061743 2073-4441 https://doaj.org/article/050011d3d3f8472eb6450a83fedfa716 |
op_doi |
https://doi.org/10.3390/w12061743 |
container_title |
Water |
container_volume |
12 |
container_issue |
6 |
container_start_page |
1743 |
_version_ |
1766347722567188480 |