Use of ANN models in the prediction of meteorological data

Rajendra, P.; Murthy, K. V. Narasimha; Subbarao, A. Nagaraj; Boadh, Rahul

doi:10.1007/s40808-019-00590-2

Cited by 42 publications

(11 citation statements)

References 16 publications

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“…The multilayer feedforward ANN model with a back-propagation algorithm was used to predict the weather conditions in the future, and it was found that the forecasting model could make a highly accurate prediction. The authors in [11] employed the ANN models to forecast air temperature, relative humidity, and soil temperature in India, showing that the ANN model was a robust tool to predict meteorological variables as it showed promising results with 91-96% accuracy for predictions of all cases. In this study, we also aimed to predict the air temperature one day ahead of past observations using the ANN model.…”

Section: Introductionmentioning

confidence: 99%

Increasing Neurons or Deepening Layers in Forecasting Maximum Temperature Time Series?

2020

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Weather forecasting, especially that of extreme climatic events, has gained considerable attention among researchers due to their impacts on natural ecosystems and human life. The applicability of artificial neural networks (ANNs) in non-linear process forecasting has significantly contributed to hydro-climatology. The efficiency of neural network functions depends on the network structure and parameters. This study proposed a new approach to forecasting a one-day-ahead maximum temperature time series for South Korea to discuss the relationship between network specifications and performance by employing various scenarios for the number of parameters and hidden layers in the ANN model. Specifically, a different number of trainable parameters (i.e., the total number of weights and bias) and distinctive numbers of hidden layers were compared for system-performance effects. If the parameter sizes were too large, the root mean square error (RMSE) would be generally increased, and the model’s ability was impaired. Besides, too many hidden layers would reduce the system prediction if the number of parameters was high. The number of parameters and hidden layers affected the performance of ANN models for time series forecasting competitively. The result showed that the five-hidden layer model with 49 parameters produced the smallest RMSE at most South Korean stations.

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Section: Introductionmentioning

confidence: 99%

Increasing Neurons or Deepening Layers in Forecasting Maximum Temperature Time Series?

2020

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“…Researchers have developed numerous methods to predict the ambient air temperature including regression and statistical models and time series analysis (Kloog et al 2014;Salcedo-Sanz et al 2016;Ustaoglu et al 2008). In addition, numerous studies have been carried out on fuzzy time series (Chen and Hwang 2000), support vector machines (SVM) (Xing et al 2018), and artificial neural network (ANN) techniques (Attoue et al 2018;Dombaycı and Gölcü 2009;Rajendra et al 2019;Mba et al 2016;Salcedo-Sanz et al 2016;Ustaoglu et al 2008). In the literature, one of the most commonly used approaches for machine learning is ANNs.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies focused on the prediction of ambient air temperature using neural networks (NN) in the literature (Attoue et al 2018;Dombaycı and Gölcü 2009;Rajendra et al 2019;Mba et al 2016;Salcedo-Sanz et al 2016;Ustaoglu et al 2008). Ustaoglu et al (2008) studied to predict daily mean, minimum, and maximum air temperatures from time series employing three different NN methods and to provide the best-fit prediction with the observed data.…”

Section: Introductionmentioning

confidence: 99%

Application of feed forward and cascade forward neural network models for prediction of hourly ambient air temperature based on MERRA-2 reanalysis data in a coastal area of Turkey

Gündoğdu

Elbir

2021

Meteorol Atmos Phys

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Air temperature forecasting has been a vital climatic factor required for different applications in many areas such as energy, industry, agriculture, health, environment, and meteorology. This study compares the performances of two static neural networks (NNs) used for the prediction of hourly ambient air temperatures in a coastal area of Turkey. Thirteen parameters from Land Surface Diagnostics and Surface Flux Diagnostics Collections from the MERRA-2 reanalysis dataset including pressure, surface specific humidity, wind speed, wind direction, air density at surface, evaporation, planetary boundary layer height, total precipitable water vapor, total precipitation, total cloud area fraction, total column ozone, greenness fraction, and leaf area index were used as input parameters for the models. Feed-Forward Neural Network (FFNN) and Cascade Forward Neural Network (CFNN) models were applied to forecast hourly ambient air temperatures at 2 m height from the surface. The results indicated that the most accurate and reliable predictions were obtained by the CFNN model with 30 neurons, while the lowest prediction performance was obtained by the FFNN model with 5 neurons. The root mean squares error (RMSE), mean absolute error (MAE) and coefficient of determination (R 2 ) values for training (and testing) of the CFNN model with 30 neurons were 0.358 (0.376), 0.273 (0.283), and 0.997 (0.992), respectively, whereas the same parameters were 0.430 (0.447), 0.334 (0.343), and 0.996 (0.989) for the FFNN model with 5 neurons. The CFNN model had a lower RMSE and MAE, and a higher R 2 than the FFNN model. These results showed that increasing the number of neurons of hidden layers from 5 to 30 provided better model performance.

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“…ML-based postprocessing methods work similarly; however, while MOS techniques tend to be based on linear regression (e.g., Glahn and Lowry 1972), ML techniques are not necessarily linear. A variety of ML approaches, other than regression, have been applied to weather prediction since the 1980s and include: artificial neural networks (ANNs; e.g., Key et al 1989;Marzban and Stumpf 1996;Kuligowski and Barros 1998;Hall et al 1999;Manzato 2007;Rajendra et al 2019), support vector machines (e.g., Ortiz-García et al 2014;Adrianto et al 2009), clustering algorithms (e.g., Baldwin et al 2005), genetic algorithms (e.g., Szpiro 1997;Kishtawal et al 2003;Wong et al 2008), and decision tree-based methods (Breiman 1984(Breiman , 2001Herman and Schumacher 2018c).…”

Section: Introductionmentioning

confidence: 99%

Postprocessing Next-Day Ensemble Probabilistic Precipitation Forecasts Using Random Forests

Loken

Clark

McGovern

et al. 2019

Weather and Forecasting

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Most ensembles suffer from underdispersion and systematic biases. One way to correct for these shortcomings is via machine learning (ML), which is advantageous due to its ability to identify and correct nonlinear biases. This study uses a single random forest (RF) to calibrate next-day (i.e., 12–36-h lead time) probabilistic precipitation forecasts over the contiguous United States (CONUS) from the Short-Range Ensemble Forecast System (SREF) with 16-km grid spacing and the High-Resolution Ensemble Forecast version 2 (HREFv2) with 3-km grid spacing. Random forest forecast probabilities (RFFPs) from each ensemble are compared against raw ensemble probabilities over 496 days from April 2017 to November 2018 using 16-fold cross validation. RFFPs are also compared against spatially smoothed ensemble probabilities since the raw SREF and HREFv2 probabilities are overconfident and undersample the true forecast probability density function. Probabilistic precipitation forecasts are evaluated at four precipitation thresholds ranging from 0.1 to 3 in. In general, RFFPs are found to have better forecast reliability and resolution, fewer spatial biases, and significantly greater Brier skill scores and areas under the relative operating characteristic curve compared to corresponding raw and spatially smoothed ensemble probabilities. The RFFPs perform best at the lower thresholds, which have a greater observed climatological frequency. Additionally, the RF-based postprocessing technique benefits the SREF more than the HREFv2, likely because the raw SREF forecasts contain more systematic biases than those from the raw HREFv2. It is concluded that the RFFPs provide a convenient, skillful summary of calibrated ensemble output and are computationally feasible to implement in real time. Advantages and disadvantages of ML-based postprocessing techniques are discussed.

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Use of ANN models in the prediction of meteorological data

Cited by 42 publications

References 16 publications

Increasing Neurons or Deepening Layers in Forecasting Maximum Temperature Time Series?

Increasing Neurons or Deepening Layers in Forecasting Maximum Temperature Time Series?

Application of feed forward and cascade forward neural network models for prediction of hourly ambient air temperature based on MERRA-2 reanalysis data in a coastal area of Turkey

Postprocessing Next-Day Ensemble Probabilistic Precipitation Forecasts Using Random Forests

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