Yarasa algoritması ile optimize edilmiş GBM modeli kullanarak mevsim bazlı bisiklet kiralama sayılarının tahmini

Yıl 2024, Cilt: 39 Sayı: 4, 2631 - 2642, 20.05.2024

Kadir İleri

https://doi.org/10.17341/gazimmfd.1362302

Öz

Kentsel bisiklet talebinin etkili kaynak tahsisi için, paylaşımlı bisikletlerin doğru tahmin edilmesi gerekmektedir. Bu tahmin işlemi, Yarasa Algoritması (YA) ile optimize edilen Gradyan Artırmalı Makinesi (GBM) yöntemi kullanılarak gerçekleştirilmiştir. Önerilen modelin etkinliğini göstermek amacıyla, modelin performansı Karar Ağacı (DT), K-En Yakın Komşu (KNN) ve Çok Katmanlı Algılayıcı (MLP) gibi farklı yöntemlerle karşılaştırılmıştır. Bu karşılaştırma işlemi için MAE ve R2 metrikleri kullanılmıştır. En iyi sonuç 0.8780 R2 değerleri ile YA-GBM tarafından elde edilmiştir. Bununla birlikte, bisiklet kiralama sayısının tahminine en fazla ve en az etki eden özellikler de belirlenmiştir. En fazla etkiye sahip özellik hava sıcaklığı iken, en az etkiye sahip özellik ise kar yağışı olmuştur.

Anahtar Kelimeler

Yarasa algoritması, GBM, KNN, MLP, bisiklet kiralama

Kaynakça

• 1. Yan, S., Lu, C. C., Wang, M. H., Stochastic fleet deployment models for public bicycle rental systems, International Journal of Sustainable Transportation, 12 (1), 39–52, 2018.
• 2. Eren, E., Uz, V. E., A review on bike-sharing: The factors affecting bike-sharing demand, Sustainable Cities and Society, 54, 101882, 2020.
• 3. Gao, X., Lee, G. M., Moment-based rental prediction for bicycle-sharing transportation systems using a hybrid genetic algorithm and machine learning, Computers & Industrial Engineering, 128, 60–69, 2019.
• 4. Qi, X., Gao, Y., Li, Y., Li, M., K-nearest Neighbors Regressor for traffic prediction of rental bikes, 14th International Conference on Computer Research and Development (ICCRD), 152–156, January 2022.
• 5. Feng, Y., Wang, S., A forecast for bicycle rental demand based on random forests and multiple linear regression, IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS), 101–105, May 2017.
• 6. Shiao, Y. C., Chung, W. H., Chen, R. C., Using SVM and Random forest for different features selection in predicting bike rental amount, 9th International Conference on Awareness Science and Technology (iCAST), 1–5, September 2018.
• 7. Heidari, E., Sobati, M. A., Movahedirad, S., Accurate prediction of nanofluid viscosity using a multilayer perceptron artificial neural network (MLP-ANN), Chemometrics and Intelligent Laboratory Systems, 155, 73–85, 2016.
• 8. Yatim, F. E., Boumanchar, I., Srhir, B., Chhiti, Y., Jama, C., Alaoui F. E. M., Waste-to-energy as a tool of circular economy: Prediction of higher heating value of biomass by artificial neural network (ANN) and multivariate linear regression (MLR), Waste Management, 153, 293–303, 2022.
• 9. Nsangou, J. C., Kenfack, J., Nzotcha, U., Ngohe Ekam, P. S., Voufo, J., Tamo, T. T., Explaining household electricity consumption using quantile regression, decision tree and artificial neural network, Energy, 250, 123856, 2022.
• 10. Thamarai, M., Malarvizhi, S. P., House price prediction modeling using machine learning, International Journal of Information Engineering & Electronic Business, 12 (2), 2020.
• 11. Baofeng, D., Zhang, H., Liu, Y., Li, J., Chen, N., Stamatopoulos, C. A., Luo, Y., Zhan, Y., Assessing susceptibility of debris flow in southwest China using gradient boosting machine, Scientific Reports, 9 (1), 2019.
• 12. Zhu, J., Fang, S., Yang, Z., Qin, Y., Chen, H., Prediction of concrete strength based on random forest and gradient boosting machine, IEEE 3rd International Conference on Power, Electronics and Computer Applications (ICPECA), 306–312, January 2023.
• 13. Akköse G., Duran A., Gürsel Dino İ., Akgül Ç.M., Machine learning based evaluation of window parameters on building energy performance and occupant thermal comfort under climate change, Journal of the Faculty of Engineering and Architecture of Gazi University, 38 (4), 2069-2084, 2023.
• 14. Gülmez B., Kulluk S., Analysis and price prediction of secondhand vehicles in Türkiye with big data and machine learning techniques, Journal of the Faculty of Engineering and Architecture of Gazi University, 38 (4), 2279-2290, 2023.
• 15. Acı M., Ayyıldız Doğansoy G., Demand forecasting for e-retail sector using machine learning and deep learning methods, Journal of the Faculty of Engineering and Architecture of Gazi University, 37 (3), 1325-1340, 2022.
• 16. Seoul Bike Sharing Demand, UCI Machine Learning Repository, 2020.
• 17. İlgün E.G., Samet R., Increasing the performance of intrusion detection models developed using machine learning method with preprocessing applied to the dataset, Journal of the Faculty of Engineering and Architecture of Gazi University, 39 (2), 679-692, 2023.
• 18. Bisong, E., Building machine learning and deep learning models on Google Cloud Platform, Berkeley, CA: Apress, 2019.
• 19. Lin Y., Wang, J., Research on text classification based on SVM-KNN, IEEE 5th International Conference on Software Engineering and Service Science, 842–844, June 2014.
• 20. Suthaharan, S., Suthaharan, S., Decision tree learning, Machine Learning Models and Algorithms for Big Data Classification: Thinking with Examples for Effective Learning, 237-269. 2016.
• 21. Murtagh, F., Multilayer perceptrons for classification and regression, Neurocomputing, 2 (5–6), 3–197, 1991.
• 22. Friedman, J., Hastie, T., Tibshirani, R., Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors), The Annals of Statistics, 28 (2), 2000.
• 23. Friedman, J. H., Greedy Function Approximation: A Gradient Boosting Machine, Annals of Statistics, 29 (5),1189–1232, 2001.
• 24. Yeşilyurt, S., Dalkılıç, H., Xgboost ve gradient boost machine ile günlük nehir akımı tahmini, 3rd International Symposium of III Engineering Applications on Civil Engineering and Earth Sciences, 2021.
• 25. Yang, X.S., A new metaheuristic bat-inspired algorithm, In Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), 65-74, 2010.
• 26. de Guia, J. D., Concepcion, R. S., Calinao, H. A., Alejandrino, J., Dadios, E.P., Sybingco, E., Using stacked long short term memory with principal component analysis for short term prediction of solar irradiance based on weather patterns, 2020 IEEE Region 10 Conference (Tencon), 946-951, 2020.
• 27. Chicco, D., Warrens, M. J., Jurman, G., The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation, PeerJ Computer Science, 7, 2021.
• 28. DNgo, T.T. T., Pham, H. T., Acosta, J. G., Derrible, S., Predicting bike-sharing demand using random forest, Journal of Science and Transport Technolog, 13-21, 2022.
• 29. Sathishkumar, V. E., Cho, Y., Season wise bike sharing demand analysis using random forest algorithm, Computational Intelligence, 2020.
• 30. Sathishkumar, V. E., Park, J., Cho, Y., Using data mining techniques for bike sharing demand prediction in metropolitan city, Computer Communications, 153, 353-366, 2020.