A Correction Method of Environmental Meteorological Model Based on Long‐Short‐Term Memory Neural Network

Yu-liang, Dai; Lu, Zhenyu; Zhang, Hengde; Zhan, Tianming; Jia, L. K.; Wang, Peng

doi:10.1029/2019ea000641

Cited by 6 publications

(4 citation statements)

References 30 publications

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“…Additionally, the Root Mean Squared Error (RMSE) and Root Mean Squared Error Skill Score (RMSE-SS) were calculated. The RMSE measures the deviation between an observed value and a bias-corrected value, indicating the accuracy of the bias-corrected data [73]. The RMSE-SS is a normalized measure of the RMSE that presents the capabilities of a bias correction model compared to those of the WRF_RAW [34].…”

Section: Statistical Assessment Methodsmentioning

confidence: 99%

Comparison of Bias Correction Methods for Summertime Daily Rainfall in South Korea Using Quantile Mapping and Machine Learning Model

Seo

Ahn

2023

Atmosphere

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This study compares the bias correction techniques of empirical quantile mapping (QM) and the Long Short-Term Memory (LSTM) machine learning model for summertime daily rainfall simulation focusing on precipitation-dependent bias and temporal variation. Numerical experiments using Weather Research and Forecasting (WRF) were conducted over South Korea with lateral boundary conditions of ERA5 reanalysis data. For the spatial distribution of mean summertime rainfall, the bias-uncorrected WRF simulation (WRF_RAW) showed dry bias for most of the region of South Korea. The WRF results corrected by QM and LSTM (WRF_QM and WRF_LSTM, respectively) were improved for the mean summer rainfall simulation with the root mean square error values of 0.17 and 0.69, respectively, which were smaller than those of the WRF_RAW (1.10). Although the WRF_QM performed better than the WRF_LSTM in terms of the summertime mean and monthly precipitation, the WRF_LSTM presented a closer interannual rainfall variation to the observation than the WRF_QM. The coefficient of determination for calendar-day mean rainfall was the highest in the following order: the WRF_LSTM (0.451), WRF_QM (0.230), and WRF_RAW (0.201). However, the WRF_LSTM had a limitation in reproducing extreme rainfall exceeding 50 mm/day due to the few cases of extreme precipitation in training data. Nevertheless, the WRF_LSTM better simulated the observed light-to-moderate precipitation (10–50 mm/day) than the others.

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Section: Statistical Assessment Methodsmentioning

confidence: 99%

Comparison of Bias Correction Methods for Summertime Daily Rainfall in South Korea Using Quantile Mapping and Machine Learning Model

Seo

Ahn

2023

Atmosphere

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“…If the learning rate specifies a high value that means it is undesirable to the network, which may lead to the risk of overshooting. 68 Thus, the proposed ANN network is associated with a slow learning rate that takes more operational time. Figure 12 describes the error histogram plot during the training process.…”

Section: Error Evaluation and Performance Analysismentioning

confidence: 99%

Intelligent based hybrid renewable energy resources forecasting and real time power demand management system for resilient energy systems

2022

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The rapid growth of hybrid renewable Distributed Energy Resources (DERs) generation possess various challenges with inaccurate forecast models in stochastic power systems. The prime objective of this research is to maximum utilization of scheduled power from hybrid renewable based DERs to maintain the load-demand profile with reduce distributed grid burden. The proposed mixed input-based cascaded artificial neural network [Formula: see text] is realized for the prediction of a short-term based hourly solar irradiance and wind speed. The testing approach is performed through a historical hourly dataset of the proposed site. Further, the normalized data sets are divided into hourly-based samples for validating the load demand power with respect to the variation in metrological data. In this paper, Adaptive Neuro-Fuzzy Inference System (ANFIS) model is simulated for short-term power demand prediction. This adaptive methodology is an effective approach for load-demand management which is based on cross-entropy. It also confirmed that during testing, the forecasting mean error and cross-entropy are less than 5% under a specific time slap of an individual day. The regression analysis is performed through the time series fitting simulation tool at different time horizons. The performance evaluation of the designed model is compared with the multi-layer perceptron model. Simulation results display the proposed mixed input-based cascaded system has enhanced accuracy and optimal performance than the multi-output correlated perceptron model.

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“…Some researchers have begun to apply ensemble learning concepts to visibility prediction. For instance, Dai et al 14 employ Random Forest to achieve more accurate predictions.…”

Section: Introductionmentioning

confidence: 99%

Visibility forecast in Jiangsu province based on the GCN-GRU model

Chen,

Xu,

Gao

et al. 2024

Sci Rep

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Low visibility weather easily leads to traffic accidents, posing threats to human life and property. To accurately forecast visibility, we conduct an empirical study focusing on Jiangsu Province. Firstly, we collect the monitoring data from meteorological stations and environmental stations for 2017-2018. Secondly, we analyze the changes in visibility from both spatial and temporal perspectives. Next, the maximum Relevance Minimum Redundancy (mRMR) algorithm is employed to select factors affecting visibility, finding that humidity and $$PM_{2.5}$$ P M 2.5 concentrations are the primary factors. Finally, we propose GCN-GRU (Graph Convolutional Network and Gated Recurrent Unit) model for short-term visibility forecasting, which employs GCN to capture the interactions between stations and uses GRU to learn the interactions between times. Experimental results indicate that GCN-GRU outperforms the standalone GRU model and three machine learning models regarding 6-hour visibility forecasting. Compared to the best competitor, GCN-GRU achieves an average increase of 3.32% in Correlation Coefficient (CORR), a decrease of 17.52% in Root Mean Square Error (RMSE), a reduction of 26.62% in Mean Absolute Percentage Error (MAPE), and a decline of 16.53% in Mean Absolute Error (MAE).

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A Correction Method of Environmental Meteorological Model Based on Long‐Short‐Term Memory Neural Network

Cited by 6 publications

References 30 publications

Comparison of Bias Correction Methods for Summertime Daily Rainfall in South Korea Using Quantile Mapping and Machine Learning Model

Comparison of Bias Correction Methods for Summertime Daily Rainfall in South Korea Using Quantile Mapping and Machine Learning Model

Intelligent based hybrid renewable energy resources forecasting and real time power demand management system for resilient energy systems

Visibility forecast in Jiangsu province based on the GCN-GRU model

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