Application of Artificial Neural Networks to Rainfall Forecasting in the Geum River Basin, Korea

Lee, Jeongwoo; Kim, Chul-Gyum; Lee, Jeong Eun; Kim, Nam Won; Kim, Hyeon-Jun

doi:10.3390/w10101448

Cited by 85 publications

(63 citation statements)

References 32 publications

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“…The ANNs are machine learning models which were initially developed to model the processes in neurons. The ANNs are nonlinear in nature and have been extensively used in the climate sciences for rainfall forecasts 8,9 , climate model parameterization 10 and ENSO forecasting 11 . The ANNs based models essentially consist of an input layer, a number of hidden layers and an output layer.…”

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confidence: 99%

A machine learning based prediction system for the Indian Ocean Dipole

Ratnam

Dijkstra

Behera

2020

Sci Rep

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the indian ocean Dipole (ioD) is a mode of climate variability observed in the indian ocean sea surface temperature anomalies with one pole off Sumatra and the other pole near East Africa. An IOD event starts sometime in May-June, peaks in September-october and ends in november. through atmospheric teleconnections, it affects the climate of many parts of the world, especially that of East Africa, Australia, india, Japan, and europe. owing to its large impacts, previous studies have addressed the predictability of the IOD using state of the art coupled climate models. Here, for the first-time, we predict the IOD using machine learning techniques, in particular artificial neural networks (ANNs). The ioD forecasts are generated for May to november from february-April conditions. the attributes for the ANNs are derived from sea surface temperature, 850 hPa and 200 hPa geopotential height anomalies, using a correlation analysis for the period 1949-2018. An ensemble of ANN forecasts is generated using 500 samples with replacement using jackknife approach. The ensemble mean of the IOD forecasts indicates the machine learning based Ann models to be capable of forecasting the ioD index well in advance with excellent skills. the forecast skills are much superior to the skills obtained from the persistence forecasts that one would guess from the observed data. the Ann models also perform far better than the models of the north American Multi-Model ensemble (nMMe) with higher correlation coefficients and lower root mean square errors (RMSE) for all the target months of May-November. open Scientific RepoRtS | (2020) 10:284 | https://doi.

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confidence: 99%

A machine learning based prediction system for the Indian Ocean Dipole

Ratnam

Dijkstra

Behera

2020

Sci Rep

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“…These values were higher than that of multi-hidden-layer models. In many studies, just one hidden layer has been used due to higher efficiency and also faster performance of the model (Wang et al, 2008;Lee et al, 2018). Therefore, we can state that the deep neural network does not necessarily lead to better forecasts than the use of the shallow neural network in maximum temperature forecasts.…”

Section: Resultsmentioning

confidence: 97%

Is Deep Better in Extreme Temperature Forecasting?

Tran

Lee

2019

J. Korean Soc. Hazard Mitig

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The Earth's climate has changed considerably over the past century due to natural processes and human activities, which has the ability to change extreme intensity and frequency. More severe climate change can trigger drastic effects with unexpected consequences. Therefore, climate extreme projection is important information required to evaluate the effect of future climate change on human beings and on the environment. Such information also enables to help all countries in the world to build long-term strategies to mitigate and adapt effectively to climate change. The process of climate change, especially the changing of temperature and rainfall, is the most significant problem in environmental sciences. Temperature-based forecasting is essential for agriculture, water resources, and human activities. The present study, therefore, focused on daily maximum forecasting. In order to predict the weather in an effective way, in this study, we have suggested a weather forecasting model using Artificial Neural Network (ANN). In ANN, there are an input layer, an output layer, and some hidden layers. The number of neurons per layer and the number of hidden layers determine the ability of the networks to produce accurate

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“…ANN is a data-driven mathematical model that emulates a human brain neural network, which has been used to solve issues such as forecasting and classification [54]. There are different architectures of ANN; however, the most common model is a Multi-Layer Perceptron (MLP) neural network, which has a structure with an input layer, single or multiple hidden layers, and an output layer.…”

Section: Building a Model Using Artificial Neural Networkmentioning

confidence: 99%

“…There are different architectures of ANN; however, the most common model is a Multi-Layer Perceptron (MLP) neural network, which has a structure with an input layer, single or multiple hidden layers, and an output layer. The MLP has been widely used to forecast several phenomena in meteorology and hydroclimatology [3,9,17,[54][55][56][57]. The typical mathematical expression of the ANN is:…”

Section: Building a Model Using Artificial Neural Networkmentioning

confidence: 99%

“…where y k is the output, for time t; x i is the ith input; w ji are the synaptic weights connecting the input layer and the hidden layer; w k j are the synaptic weights connecting the neurons of the hidden and output layers; f 0 and f h are the activation functions in the output and the hidden layers, respectively; n and m are the number of output and hidden layers, respectively, and w jb and w kb are the bias for the hidden and output layers [15,54].…”

Section: Building a Model Using Artificial Neural Networkmentioning

confidence: 99%

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Monthly Rainfall Anomalies Forecasting for Southwestern Colombia Using Artificial Neural Networks Approaches

Canchala

Alfonso-Morales

Carvajal-Escobar

et al. 2020

Water

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Improving the accuracy of rainfall forecasting is relevant for adequate water resources planning and management. This research project evaluated the performance of the combination of three Artificial Neural Networks (ANN) approaches in the forecasting of the monthly rainfall anomalies for Southwestern Colombia. For this purpose, we applied the Non-linear Principal Component Analysis (NLPCA) approach to get the main modes, a Neural Network Autoregressive Moving Average with eXogenous variables (NNARMAX) as a model, and an Inverse NLPCA approach for reconstructing the monthly rainfall anomalies forecasting in the Andean Region (AR) and the Pacific Region (PR) of Southwestern Colombia, respectively. For the model, we used monthly rainfall lagged values of the eight large-scale climate indices linked to the El Niño Southern Oscillation (ENSO) phenomenon as exogenous variables. They were cross-correlated with the main modes of the rainfall variability of AR and PR obtained using NLPCA. Subsequently, both NNARMAX models were trained from 1983 to 2014 and tested for two years (2015–2016). Finally, the reconstructed outputs from the NNARMAX models were used as inputs for the Inverse NLPCA approach. The performance of the ANN approaches was measured using three different performance metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Pearson’s correlation (r). The results showed suitable forecasting performance for AR and PR, and the combination of these ANN approaches demonstrated the possibility of rainfall forecasting in these sub-regions five months in advance and provided useful information for the decision-makers in Southwestern Colombia.

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Application of Artificial Neural Networks to Rainfall Forecasting in the Geum River Basin, Korea

Cited by 85 publications

References 32 publications

A machine learning based prediction system for the Indian Ocean Dipole

A machine learning based prediction system for the Indian Ocean Dipole

Is Deep Better in Extreme Temperature Forecasting?

Monthly Rainfall Anomalies Forecasting for Southwestern Colombia Using Artificial Neural Networks Approaches

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