Vol. 5 No. 1 (2025): Issue 5
Articles

Economic Loss Prediction through response surface methods

PDF
  • Ji Soo Kim,
  • Osman Kaya,
  • Min Jae Park,
  • Soo Hyun Lee
Ji Soo Kim
Department of Economics and Applied Statistics, Pukyong National University, Busan, 48513, South Korea
Min Jae Park
Center for Economic Dynamics and Predictive Analysis, Dong-A University, Busan, 49315, South Korea
Soo Hyun Lee
Institute of Industrial and Financial Engineering, Chonbuk National University, Jeonju, 54896, South Korea

Published 2025-06-07

Keywords

  • Economic Loss,
  • Prediction,
  • Response Surface Methods,
  • Risk Management,
  • Decision-Making

How to Cite

Kim, J. S., Kaya, O., Park, M. J., & Lee, S. H. (2025). Economic Loss Prediction through response surface methods. Economic and Financial Research Letters, 5(1). Retrieved from https://ojs.mri-pub.com/index.php/EFRL/article/view/126

Abstract

This paper discusses the importance of accurate economic loss prediction in various fields such as insurance, finance, and disaster management. The current research faces challenges due to the complexity and uncertainty of economic systems, making precise predictions difficult to achieve. In response, this study introduces a novel approach utilizing response surface methods to improve the accuracy of economic loss prediction models. By integrating response surface methods with traditional predictive models, this research aims to enhance the estimation of economic losses under different scenarios, ultimately providing valuable insights for decision-making and risk management.

PDF

References

  1. J. Yang et al., "A tropical cyclone risk prediction framework using flood susceptibility and tree-based machine learning models: County-level direct economic loss prediction in Guangdong Province," Int. J. Disaster Risk Reduction, 2024.
  2. C. Wang et al., "Research on the economic loss prediction and vulnerability risk zoning of coastal erosion disaster: A case study from Liaoning, China," Front. Mar. Sci., 2024.
  3. W. Chen and L. Zhang, "An automated machine learning approach for earthquake casualty rate and economic loss prediction," Reliab. Eng. Syst. Saf., 2022.
  4. A. Chen et al., "The Economic Loss Prediction of Flooding Based on Machine Learning and the Input-Output Model," Atmosphere, 2021.
  5. X. Chao et al., "The potential distribution analysis and economic loss prediction of Spodoptera frugiperda in Yunnan Province," J. South. Agric., 2019.
  6. S. Arunachalam, "Multiobjective multi-verse optimization algorithm to solve dynamic economic emission dispatch problem with transmission loss prediction by an artificial neural network," Appl. Soft Comput., 2022.
  7. B. Cheng et al., "Typhoon Economic Loss Prediction in China by Applying General Regression Neural Network and Hierarchical Cluster Analysis," Journal of Disaster Research, 2014.
  8. J. Wang and P. Du, "Quarterly PM2.5 prediction using a novel seasonal grey model and its further application in health effects and economic loss assessment: Evidences from Shanghai and Tianjin, China," Nat. Hazards, 2021.
  9. Y. Shi et al., "Estimation of economic loss by earthquakes in Taiwan Region," NPJ Nat. Hazards, 2024.
  10. H. Ishibashi, "Framework for risk assessment of economic loss from structures damaged by rainfall-induced landslides using machine learning," Georisk, 2023.
  11. H. Mensah-Akutteh et al., "Optimizing coagulation–flocculation processes with aluminium coagulation using response surface methods," Applied Water Science, vol. 12, 2022.
  12. D. Li et al., "Response surface methods for slope reliability analysis: Review and comparison," Engineering Geology, vol. 203, 2016.
  13. M. Rashki et al., "Classification correction of polynomial response surface methods for accurate reliability estimation," Structural Safety, 2019.
  14. Y. Wang et al., "Variability Analysis of Crosstalk Among Differential Vias Using Polynomial-Chaos and Response Surface Methods," IEEE Transactions on Electromagnetic Compatibility, vol. 59, 2017.
  15. O. Ali et al., "Optimization of Biodiesel-Diesel Blended Fuel Properties and Engine Performance with Ether Additive Using Statistical Analysis and Response Surface Methods," Energies, vol. 8, 2015.
  16. C. Li and Y. Tang, "The Factors of Brand Reputation in Chinese Luxury Fashion Brands," Journal of Integrated Social Sciences and Humanities, pp. 1–14, 2023.
  17. Q. Zhu, ‘Autonomous Cloud Resource Management through DBSCAN-based unsupervised learning’, Optimizations in Applied Machine Learning, vol. 5, no. 1, Art. no. 1, Jun. 2025, doi: 10.71070/oaml.v5i1.112.
  18. S. Dan and Q. Zhu, ‘Enhancement of data centric security through predictive ridge regression’, Optimizations in Applied Machine Learning, vol. 5, no. 1, Art. no. 1, May 2025, doi: 10.71070/oaml.v5i1.113.
  19. S. Dan and Q. Zhu, ‘Highly efficient cloud computing via Adaptive Hierarchical Federated Learning’, Optimizations in Applied Machine Learning, vol. 5, no. 1, Art. no. 1, Apr. 2025, doi: 10.71070/oaml.v5i1.114.
  20. Q. Zhu and S. Dan, ‘Data Security Identification Based on Full-Dimensional Dynamic Convolution and Multi-Modal CLIP’, Journal of Information, Technology and Policy, 2023.
  21. Q. Zhu, ‘An innovative approach for distributed cloud computing through dynamic Bayesian networks’, Journal of Computational Methods in Engineering Applications, 2024.
  22. Z. Luo, H. Yan, and X. Pan, ‘Optimizing Transformer Models for Resource-Constrained Environments: A Study on Model Compression Techniques’, Journal of Computational Methods in Engineering Applications, pp. 1–12, Nov. 2023, doi: 10.62836/jcmea.v3i1.030107.
  23. H. Yan and D. Shao, ‘Enhancing Transformer Training Efficiency with Dynamic Dropout’, Nov. 05, 2024, arXiv: arXiv:2411.03236. doi: 10.48550/arXiv.2411.03236.
  24. H. Yan, ‘Real-Time 3D Model Reconstruction through Energy-Efficient Edge Computing’, Optimizations in Applied Machine Learning, vol. 2, no. 1, 2022.
  25. Y. Shu, Z. Zhu, S. Kanchanakungwankul, and D. G. Truhlar, ‘Small Representative Databases for Testing and Validating Density Functionals and Other Electronic Structure Methods’, J. Phys. Chem. A, vol. 128, no. 31, pp. 6412–6422, Aug. 2024, doi: 10.1021/acs.jpca.4c03137.
  26. C. Kim, Z. Zhu, W. B. Barbazuk, R. L. Bacher, and C. D. Vulpe, ‘Time-course characterization of whole-transcriptome dynamics of HepG2/C3A spheroids and its toxicological implications’, Toxicology Letters, vol. 401, pp. 125–138, 2024.
  27. J. Shen et al., ‘Joint modeling of human cortical structure: Genetic correlation network and composite-trait genetic correlation’, NeuroImage, vol. 297, p. 120739, 2024.
  28. K. F. Faridi et al., ‘Factors associated with reporting left ventricular ejection fraction with 3D echocardiography in real‐world practice’, Echocardiography, vol. 41, no. 2, p. e15774, Feb. 2024, doi: 10.1111/echo.15774.
  29. Z. Zhu, ‘Tumor purity predicted by statistical methods’, in AIP Conference Proceedings, AIP Publishing, 2022.
  30. Z. Zhao, P. Ren, and Q. Yang, ‘Student self-management, academic achievement: Exploring the mediating role of self-efficacy and the moderating influence of gender insights from a survey conducted in 3 universities in America’, Apr. 17, 2024, arXiv: arXiv:2404.11029. doi: 10.48550/arXiv.2404.11029.
  31. Z. Zhao, P. Ren, and M. Tang, ‘Analyzing the Impact of Anti-Globalization on the Evolution of Higher Education Internationalization in China’, Journal of Linguistics and Education Research, vol. 5, no. 2, pp. 15–31, 2022.
  32. M. Tang, P. Ren, and Z. Zhao, ‘Bridging the gap: The role of educational technology in promoting educational equity’, The Educational Review, USA, vol. 8, no. 8, pp. 1077–1086, 2024.
  33. P. Ren, Z. Zhao, and Q. Yang, ‘Exploring the Path of Transformation and Development for Study Abroad Consultancy Firms in China’, Apr. 17, 2024, arXiv: arXiv:2404.11034. doi: 10.48550/arXiv.2404.11034.
  34. P. Ren and Z. Zhao, ‘Parental Recognition of Double Reduction Policy, Family Economic Status And Educational Anxiety: Exploring the Mediating Influence of Educational Technology Substitutive Resource’, Economics & Management Information, pp. 1–12, 2024.
  35. Z. Zhao, P. Ren, and M. Tang, ‘How Social Media as a Digital Marketing Strategy Influences Chinese Students’ Decision to Study Abroad in the United States: A Model Analysis Approach’, Journal of Linguistics and Education Research, vol. 6, no. 1, pp. 12–23, 2024.
  36. Z. Zhao and P. Ren, ‘Identifications of Active Explorers and Passive Learners Among Students: Gaussian Mixture Model-Based Approach’, Bulletin of Education and Psychology, vol. 5, no. 1, Art. no. 1, May 2025.
  37. Z. Zhao and P. Ren, ‘Prediction of Student Answer Accuracy based on Logistic Regression’, Bulletin of Education and Psychology, vol. 5, no. 1, Art. no. 1, Feb. 2025.
  38. Z. Zhao and P. Ren, ‘Prediction of Student Disciplinary Behavior through Efficient Ridge Regression’, Bulletin of Education and Psychology, vol. 5, no. 1, Art. no. 1, Mar. 2025.
  39. Z. Zhao and P. Ren, ‘Random Forest-Based Early Warning System for Student Dropout Using Behavioral Data’, Bulletin of Education and Psychology, vol. 5, no. 1, Art. no. 1, Apr. 2025.
  40. P. Ren and Z. Zhao, ‘Recognition and Detection of Student Emotional States through Bayesian Inference’, Bulletin of Education and Psychology, vol. 5, no. 1, Art. no. 1, May 2025.
  41. P. Ren and Z. Zhao, ‘Support Vector Regression-based Estimate of Student Absenteeism Rate’, Bulletin of Education and Psychology, vol. 5, no. 1, Art. no. 1, Jun. 2025.
  42. G. Zhang and T. Zhou, ‘Finite Element Model Calibration with Surrogate Model-Based Bayesian Updating: A Case Study of Motor FEM Model’, IAET, pp. 1–13, Sep. 2024, doi: 10.62836/iaet.v3i1.232.
  43. G. Zhang, W. Huang, and T. Zhou, ‘Performance Optimization Algorithm for Motor Design with Adaptive Weights Based on GNN Representation’, Electrical Science & Engineering, vol. 6, no. 1, Art. no. 1, Oct. 2024, doi: 10.30564/ese.v6i1.7532.
  44. T. Zhou, G. Zhang, and Y. Cai, ‘Unsupervised Autoencoders Combined with Multi-Model Machine Learning Fusion for Improving the Applicability of Aircraft Sensor and Engine Performance Prediction’, Optimizations in Applied Machine Learning, vol. 5, no. 1, Art. no. 1, Feb. 2025, doi: 10.71070/oaml.v5i1.83.
  45. Y. Tang and C. Li, ‘Exploring the Factors of Supply Chain Concentration in Chinese A-Share Listed Enterprises’, Journal of Computational Methods in Engineering Applications, pp. 1–17, 2023.
  46. C. Li and Y. Tang, ‘Emotional Value in Experiential Marketing: Driving Factors for Sales Growth–A Quantitative Study from the Eastern Coastal Region’, Economics & Management Information, pp. 1–13, 2024.
  47. C. Y. Tang and C. Li, ‘Examining the Factors of Corporate Frauds in Chinese A-share Listed Enterprises’, OAJRC Social Science, vol. 4, no. 3, pp. 63–77, 2023.
  48. W. Huang, T. Zhou, J. Ma, and X. Chen, ‘An ensemble model based on fusion of multiple machine learning algorithms for remaining useful life prediction of lithium battery in electric vehicles’, Innovations in Applied Engineering and Technology, pp. 1–12, 2025.
  49. W. Huang and J. Ma, ‘Predictive Energy Management Strategy for Hybrid Electric Vehicles Based on Soft Actor-Critic’, Energy & System, vol. 5, no. 1, 2025.
  50. J. Ma, K. Xu, Y. Qiao, and Z. Zhang, ‘An Integrated Model for Social Media Toxic Comments Detection: Fusion of High-Dimensional Neural Network Representations and Multiple Traditional Machine Learning Algorithms’, Journal of Computational Methods in Engineering Applications, pp. 1–12, 2022.
  51. W. Huang, Y. Cai, and G. Zhang, ‘Battery Degradation Analysis through Sparse Ridge Regression’, Energy & System, vol. 4, no. 1, Art. no. 1, Dec. 2024, doi: 10.71070/es.v4i1.65.
  52. Z. Zhang, ‘RAG for Personalized Medicine: A Framework for Integrating Patient Data and Pharmaceutical Knowledge for Treatment Recommendations’, Optimizations in Applied Machine Learning, vol. 4, no. 1, 2024.
  53. Z. Zhang, K. Xu, Y. Qiao, and A. Wilson, ‘Sparse Attention Combined with RAG Technology for Financial Data Analysis’, Journal of Computer Science Research, vol. 7, no. 2, Art. no. 2, Mar. 2025, doi: 10.30564/jcsr.v7i2.8933.
  54. P.-M. Lu and Z. Zhang, ‘The Model of Food Nutrition Feature Modeling and Personalized Diet Recommendation Based on the Integration of Neural Networks and K-Means Clustering’, Journal of Computational Biology and Medicine, vol. 5, no. 1, 2025.
  55. Y. Qiao, K. Xu, Z. Zhang, and A. Wilson, ‘TrAdaBoostR2-based Domain Adaptation for Generalizable Revenue Prediction in Online Advertising Across Various Data Distributions’, Advances in Computer and Communication, vol. 6, no. 2, 2025.
  56. K. Xu, Y. Gan, and A. Wilson, ‘Stacked Generalization for Robust Prediction of Trust and Private Equity on Financial Performances’, Innovations in Applied Engineering and Technology, pp. 1–12, 2024.
  57. A. Wilson and J. Ma, ‘MDD-based Domain Adaptation Algorithm for Improving the Applicability of the Artificial Neural Network in Vehicle Insurance Claim Fraud Detection’, Optimizations in Applied Machine Learning, vol. 5, no. 1, 2025.