Traffic Accident Prediction Based on LSTM-GBRT Model

Zhang, Zhihao; Chen, Danny; Silamu, Wushour

doi:10.1155/2020/4206919

Cited by 25 publications

(13 citation statements)

References 13 publications

(12 reference statements)

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“…Where M is the total number of data points, y t is the actual value, andŷ t is the forecasted value. RMSLE error is the logarithmic relationship between the model's actual data value and the forecasted value [41]. By applying the proposed model, we obtained 0.02 RMSLE, which is the lowest value compared to other models.…”

Section: Model Evaluation Indicatorsmentioning

confidence: 87%

Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction

Byun

2020

IEEE Access

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Smart grids are developing rapidly, leading to the need for accurate forecasts of power consumption. However, developing a precise time series model for energy forecasting is difficult. It has to be trained using optimal meteorological features such as temperature and time lags to qualify for a beneficial model. We have proposed an approach that uses an ensemble machine learning model based on XGBoost, support vector regressor (SVR), and K-nearest neighbors (KNN) regressor algorithms. We have also used the genetic algorithm (GA) to predict total load consumption from optimal feature selection. Using Jeju island's electricity consumption data as a case study shows that the proposed ensemble model optimized with GA is more accurate than the individual machine learning models. Using only the best-selected weather and time features, the proposed model records all the features of a complicated time series and shows a reduction in the mean absolute percentage error (MAPE) and the root mean square log error for the week ahead forecasts. We got 3.35 % MAPE of the three months test data by applying the proposed model. The smart grids operators can manage resources effectively to provide excellent services to the consumers based on the recommended model outcomes.

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Section: Model Evaluation Indicatorsmentioning

confidence: 87%

Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction

Byun

2020

IEEE Access

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“…In this paper, Root Mean Square Error (RM SE) is selected to evaluate the accuracy of the model, while the trend accuracy (T A) is considered to evaluate the trend accuracy, which are defined as formula (17). In addition, the computation efficiency is evaluated by the total time of hyperparameter search (HS) and the average single run time (AST ) under a given hyperparameter.…”

Section: B Model Performance Indicatorsmentioning

confidence: 99%

“…The above mentioned methods can improve the modelling efficiency to some extent, however, the results obtained may fluctuate due to different kinds of initialization methods. Inspired by Zhang [17] for the LSTM parameter setting method, at the same time for further accelerate the LSTM network in high-frequency financial data, this paper introduces SGS technique to improve the computation accuracy, which is able to avoid the long computation and makes more stable prediction, SGS method to optimize LSTM super parameters is the biggest innovation of this paper. In Section 2 we introduce the LSTM model and SGS algorithm, Section 3 condLucts data preprocessing and evaluation index selection, Section 4 shows the empirical results, and Section 5 summarizes the research in this paper.…”

Section: Introductionmentioning

confidence: 99%

Financial High-Frequency Time Series Forecasting Based on Sub-Step Grid Search Long Short-Term Memory Network

Tian

2020

IEEE Access

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“…The gradient boosted regression tree (GBRT) is an ensemble ML algorithm consisting of multiple decision trees, which is robust in model training (Zhang et al, 2020). First, the decision tree algorithm is a classical ML method, and it can deal with inherent nonlinear relationships in variables and missing values (Quinlan, 1987;Fayyad and Stolorz, 1997).…”

Section: Introductionmentioning

confidence: 99%

Tropical Cyclones Intensity Prediction in the Western North Pacific Using Gradient Boosted Regression Tree Model

Zhu

Zhao

et al. 2022

Front. Earth Sci.

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As an artificial intelligence method, machine learning (ML) has been widely used in prediction models of high-dimensional datasets. This study proposes an ML method, the Gradient Boosted Regression Tree (GBRT), to predict the intensity changes of tropical cyclones (TCs) in the Western North Pacific at 12-, 24-, 36-, 48-, 60-, and 72-h (hr) forecasting lead time and the model is optimized by the Bayesian Optimization algorithm. The model predictands are the TCs intensity changes at different forecasting lead times, obtained from the best track data of the Shanghai Typhoon Institute (STI) and the Joint Typhoon Warning Center (JTWC) from 2000 to 2019. The model predictors are the synoptic variables, climatological and persistent variables derived from the reanalysis data obtained from the National Centers for Environmental Prediction (NCEP), and the sea surface temperature (SST) data obtained from the National Oceanic and Atmospheric Administration (NOAA). The results show that the GBRT model can capture the TCs intensity changes well for the succeeding 12-h, 24-h, 36-h, and 72-h. Compared with the traditional multiple linear regression (MLR) model, the GBRT model has better performance in predicting TCs intensity changes. Compared with the MLR model, R2 of the GBRT model for TCs intensity forecast increases by an average of 8.47% and 4.45% for STI data and JTWC data. MAE (RMSE) drops by 26.24% (25.14%) and 10.51% (4.68%) for the two datasets, respectively. The potential future intensity change (POT), the intensity changes during the previous 12 h (Dvmax), Initial storm maximum wind speed (Vmax), SST, and the Sea-Land ratio are the most significant predictors for the GBRT model in predicting TCs intensity change over the Western North Pacific.

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Traffic Accident Prediction Based on LSTM-GBRT Model

Cited by 25 publications

References 13 publications

Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction

Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction

Financial High-Frequency Time Series Forecasting Based on Sub-Step Grid Search Long Short-Term Memory Network

Tropical Cyclones Intensity Prediction in the Western North Pacific Using Gradient Boosted Regression Tree Model

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