Abstract
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 can be concluded that the neural network with MCCVA enables us to provide acceptable medium-term rainfall forecasts and define the prediction uncertainty interval.
Highlights
Accurate and timely rainfall forecasting is necessary for efficient water resources management, flood protection, and drought risk mitigation [1]
The objective of this study is to develop practical Artificial Neural Network (ANN) models using Monte-Carlo cross-validation and aggregation (MCCVA) to forecast the rainfall for the Han River basin in South Korea during May and June
The Heidke skill score (HSS) for rainfall forecasts in June is 0.32. This means that the forecast quality using the ANN-J model increased by 32%, which is better than the forecasts that are expected by chance
Summary
Accurate and timely rainfall forecasting is necessary for efficient water resources management, flood protection, and drought risk mitigation [1]. Rainfall forecasting on a monthly or seasonal basis has positive effects on effective water resources allocation, water supply planning, and water demand reduction during a drought period. Medium to long-term rainfall forecasting is an interesting and challenging matter in the fields of meteorology and hydrology. The use of data-driven techniques such as artificial neural networks (ANNs), support vector machines (SVMs), and fuzzy logic systems has increased for developing hydrological and meteorological prediction models. ANN has been extensively used for rainfall forecasting because it has the ability to capture the complex nonlinear relationship between input and output variables without requiring detailed knowledge of the physical process [2]. Many researchers suggested that neural networks have the potential to well produce monthly and seasonal rainfall forecasts using the global climate indices as input parameters in many parts of the world. Abbot and Marohasy [6]
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