Comparison of random forest and support vector machine regression models for forecasting road accidents

Antoine, Gatera; Kuradusenge, Martin; Bajpai, Gaurav; Mikeka, Chomora; Shrivastava, Sarika

doi:10.1016/j.sciaf.2023.e01739

Cited by 13 publications

(4 citation statements)

References 29 publications

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“…High VIF values indicate problematic levels of multicollinearity, implying that particular predictor parameters might be highly correlated with other parameters, potentially impacting the reliability of regression coefficients (Belsley et al, 1980;Hair et al, 2010). Equations (1-6) represent the formulas for each statistical metric (Gatera et al, 2023) as follows:…”

Section: Modeling and Validationmentioning

confidence: 99%

Quantifying soil organic matter for sustainable agricultural land management with soil color and machine learning technique

Kang,

Lee,

Kim

et al. 2024

Agronomy Journal

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As interest in sustainable agricultural land management continues to grow, there is a need for advanced techniques that enable easy and rapid quantification of soil characteristics. Soil organic matter (SOM) is a critical factor in determining soil health. Unfortunately, contemporary techniques for the SOM content analysis are laborious, time‐consuming and resource‐intensive. In response to this challenge, our study has developed a statistic model for forecasting the SOM content using soil color indices and machine learning algorithms. Color indices, including brightness, hue, and saturation, were derived from the soil images captured by a smartphone. The correlation between color indices and SOM reveals the negative correlations between brightness (‐0.790) and hue (‐0.420) with the SOM content at significance level (p) < 0.01. Conversely, the saturation of soil color, performed best with the predictive model, exhibited a positive relationship (+0.120, p<0.05). The predictive performance of our model outperformed randomforest (RF) algorithm compared to both multiple linear regression (MLR) and support vector machine (SVM). The RF algorithm achieved the highest coefficient of determination (R2) value of 0.984. Furthermore, it demonstrated the lowest error metrics. Notably, the RMSE value for the RF algorithm was only 0.025% with the training dataset, whereas the MLR and SVM algorithms yielded relatively higher values at 0.029 and 0.110%, respectively. These findings highlight the presence of a non‐linear relationship in predicting the SOM content, which the RF algorithm effectively captures. This approach offers accurate predictions of the SOM content, supporting sustainable agricultural land management through rapid and easy quantification.This article is protected by copyright. All rights reserved

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Section: Modeling and Validationmentioning

confidence: 99%

Quantifying soil organic matter for sustainable agricultural land management with soil color and machine learning technique

Kang,

Lee,

Kim

et al. 2024

Agronomy Journal

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show abstract

“…RF has been established as an effective approach for accident prediction [26,28,29], with an increased level of prediction accuracy [30]. Gatera [31] developed two models using training and validation datasets to predict traffic accidents using RF. Su et al [32] used an RF model to study and evaluate the importance of five continuous variables (average speed, queue length, cumulative number of vehicles in queue, cumulative duration, and cumulative number of vehicles) on traffic flow variables.…”

Section: Random Forestmentioning

confidence: 99%

Investigating Micro-Driving Behavior of Combined Horizontal and Vertical Curves Using an RF Model and SHAP Analysis

Wang,

Wei,

Wang

2024

Applied Sciences

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The free-flowing traffic environment of the freeway is an important application scenario for automatic driving. In this scenario, the freeway’s geometric design is an important factor because no other vehicle affects the driving process of the target vehicle. The freeway’s combined curves have more safety problems, but there are no quantitative guidelines for their geometric design. They present more challenges for automatic driving or driver assistance functions. If the relationship between human-drivers’ micro-behavior and the geometric design of combined curves is examined, it could provide theoretical support for the enhancement of automated driving and driver assistance functions as well as the quantitative design of combined curves. The paper analyzed the speed change and lane departure behaviors of combined curves, considering downslope curves, upslope curves, sag curves, and crest curves. The relationship between micro-driving behaviors and combined curves’ geometric design were determined using random forest models. The SHAP values of each variable were calculated. The results showed that (1) on a downslope curve and sag curve the speed change behavior should be paid more attention; on an upslope curve and crest curve, the lane departure behavior should be paid more attention; (2) the priority of geometric design parameters for four types of combined curves were different. Based on the results, drivers and autonomous vehicles can pay different levels of attention to their speed change and departure behavior on different combination curves, and take targeted improvement measures in time according to the driving status of the vehicles. Road designers can also prioritize more important road design parameters in the design process to avoid serious accidents caused by excessive speed changes and departures.

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“…The model used by Oyebayo Ridwan Olaniran et al [14] was utilized for high-dimensional categorization of genomic data. This model was used by Antoine Gatera et al [15] to predict traffic accidents.…”

Section: Introductionmentioning

confidence: 99%

Integration of Effective Models to Provide a Novel Method to Identify the Future Trend of Nikkei 225 Stocks Index

Zhu,

2024

IJACSA

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The stock market refers to a financial market in which individuals and institutions engage in the buying and selling of shares of publicly listed firms. The valuation of stocks is influenced by the interplay between the forces of supply and demand. The act of allocating funds to the stock market entails a certain degree of risk, while it presents the possibility of substantial gains over an extended period. The task of predicting stock prices in the securities market is further complicated by the presence of non-stationary and non-linear characteristics in financial time series data. While traditional techniques have the potential to enhance the accuracy of forecasting, they are also associated with computational complexities that might lead to an elevated occurrence of prediction mistakes. This is the reason why the financial industry has seen a growing prevalence of novel methods, particularly in the stock market. This work introduces a novel model that effectively addresses several challenges by integrating the random forest methodology with the artificial bee colony algorithm. In the current study, the hybrid model demonstrated superior performance and effectiveness compared to the other models. The proposed model exhibited optimum performance and demonstrated a significant degree of effectiveness with low errors. The efficiency of the predictive model for stock price forecasts was established via the analysis of data obtained from the Nikkei 225 index. The data included the timeframe from January 2013 to December 2022. The results reveal that the proposed framework demonstrates efficacy and reliability in evaluating and predicting the price time series of equities. The empirical evidence suggests that, when compared to other current methodologies, the proposed model has a greater degree of accuracy in predicting outcomes.

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Comparison of random forest and support vector machine regression models for forecasting road accidents

Cited by 13 publications

References 29 publications

Quantifying soil organic matter for sustainable agricultural land management with soil color and machine learning technique

Quantifying soil organic matter for sustainable agricultural land management with soil color and machine learning technique

Investigating Micro-Driving Behavior of Combined Horizontal and Vertical Curves Using an RF Model and SHAP Analysis

Integration of Effective Models to Provide a Novel Method to Identify the Future Trend of Nikkei 225 Stocks Index

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