Research Article
Yarasa algoritması ile optimize edilmiş GBM modeli kullanarak mevsim bazlı bisiklet kiralama sayılarının tahmini
Abstract
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.
Keywords
References
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Year 2024,
Volume: 39 Issue: 4, 2631 - 2642, 20.05.2024
Abstract
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There are 30 citations in total.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Deep Learning |
| Journal Section | Makaleler |
| Authors | |
| Early Pub Date | May 17, 2024 |
| Publication Date | May 20, 2024 |
| Submission Date | September 18, 2023 |
| Acceptance Date | January 6, 2024 |
| Published in Issue | Year 2024 Volume: 39 Issue: 4 |