In this paper, Infiltration rate of the soil is investigated by using predictive models of Random forest regression and their performance were compared with Artificial neural network (ANN) and M5P model tree techniques. We utilized 132 field measurements comprising this dataset. 88 models were trained using observations, while the remaining 44 were used to validate it. The cumulative time (Tf), the impurity type (It), the impurity concentration (Ci), and the moisture content (Wc) were utilized as input variables, and the rate of infiltration was employed as the output. To evaluate the efficiency of the two modeling methodologies, correlation coefficients we estimated root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative square error are all terms that may be used to describe errors (RRSE). The random forest regression approach outperforms the other two models when compared to evolutionary data (ANN and M5P model tree). Using a random forest as a model, regression can properly estimate the infiltration rate within a 25% error range. According to the results of the sensitivity research, cumulative time plays an important influence in determining the soil's penetration rate.

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This research focuses on optimizing the decision support system in investment risk management using Firefly Algorithm. The data used in this research is obtained from the Kaggle repository. The research aims to compare the performance of Firefly Algorithm with other optimization algorithms such as Genetic Algorithm, Particle Swarm Optimization, and Simulated Annealing. This research applies Firefly Algorithm and other optimization algorithms individually to the investment risk management system to analyze and compare their performance. The results show that Firefly Algorithm outperforms other optimization algorithms in terms of finding optimal solutions and minimizing investment risk. Firefly Algorithm can effectively identify the best investment options to avoid risks, which can bring significant benefits to investors and companies. The findings of this study show that Firefly Algorithm can be a useful tool in the investment risk management system. The application of Firefly Algorithm in the investment risk management decision-making process can improve decision-making and help investors avoid risks and maximize their profits. The novelty of this research is the application of the Firefly algorithm to optimize the decision support system in investment risk management. It aims to identify the best option in avoiding investment risk and maximizing profit. In addition, this research also compares the performance of the Firefly algorithm with other algorithms such as Genetic Algorithm, Particle Swarm Optimization, and Simulated Annealing in solving optimization problems in the investment field.