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Journals / Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi / 2026 / Cilt: 26 Sayı: 4

Comparative Modeling of Afyonkarahisar Population Change Using Regression and ARIMA Approaches

Afyonkarahisar Nüfus Değişiminin Regresyon ve ARIMA Yaklaşımlarıyla Karşılaştırmalı Modellenmesi

Abdil Karakan(AFYON KOCATEPE UNIVERSITY)0000-0003-1651-7568
Yüksel Oğuz(AFYON KOCATEPE UNIVERSITY)0000-0002-5233-151X
Hakan Demir(AFYON KOCATEPE ÜNİVERSİTESİ)0009-0003-9605-378X
Pages
908–917
Publication Date
2026-08-03
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The projected population range of approximately 255,000–260,000 individuals is consistent with observed trends and supports evidence-based regional planning.

📌 3 Key Takeaways

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Objective & Hypothesis:This study analyses population data of Afyonkarahisar covering the 1982–2024 period to generate and validate a forecast for the year 2025 using three statistical modelling approaches: linear regression, second-order polynomial regression, and the ARIMA(1,1,0) time series model.
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Key Findings & Data:Model performances were evaluated using error metrics including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²).
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Conclusion & Impact:The projected population range of approximately 255,000–260,000 individuals is consistent with observed trends and supports evidence-based regional planning.
🔬Design:Modeling & Simulation
📍Scope:2024 Verileri
⚙️Variables:Nüfus Tahmini · Zaman Serisi Analizi · Regresyon Modelleri

Abstract

This study analyses population data of Afyonkarahisar covering the 1982–2024 period to generate and validate a forecast for the year 2025 using three statistical modelling approaches: linear regression, second-order polynomial regression, and the ARIMA(1,1,0) time series model. These models were selected to capture both long-term trend behaviour and temporal dynamics within the population series. Model performances were evaluated using error metrics including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). The polynomial regression model achieved the highest historical fit (RMSE = 4,607; MAE = 3,121; R² ≈ 0.993), while the linear regression model provided stable trend representation (RMSE = 11,569; MAE = 10,656; R² ≈ 0.961). The ARIMA model demonstrated balanced short-term forecasting capability (RMSE = 6,482; MAE = 5,476; R² ≈ 0.942). To assess real-world predictive accuracy, model outputs were compared with the actual 2025 population value. The ARIMA model exhibited the lowest deviation (0.28%), followed by linear regression (2.03%), whereas the polynomial model showed higher projection error (7.74%). The results confirm that no single model fully captures all characteristics of population behaviour; instead, a multi-model framework provides a more reliable and interpretable forecasting structure. The projected population range of approximately 255,000–260,000 individuals is consistent with observed trends and supports evidence-based regional planning.

Özet

Bu çalışma, 1982-2024 dönemini kapsayan Afyonkarahisar nüfus verilerini analiz ederek, üç istatistiksel modelleme yaklaşımı (doğrusal regresyon, ikinci dereceden polinom regresyonu ve ARIMA(1,1,0) zaman serisi modeli) kullanarak 2025 yılı için bir tahmin üretmeyi ve doğrulamayı amaçlamaktadır. Bu modeller, nüfus serisi içindeki hem uzun vadeli trend davranışını hem de zamansal dinamikleri yakalamak için seçilmiştir. Model performansları, Kök Ortalama Kare Hatası (RMSE), Ortalama Mutlak Hata (MAE) ve belirleme katsayısı (R²) dahil olmak üzere hata ölçütleri kullanılarak değerlendirilmiştir. Polinom regresyon modeli en yüksek tarihsel uyumu elde etmiştir (RMSE = 4607; MAE = 3121; R² ≈ 0,993), doğrusal regresyon modeli ise istikrarlı bir trend temsili sağlamıştır (RMSE = 11569; MAE = 10656; R² ≈ 0,961). ARIMA modeli, dengeli kısa vadeli tahmin yeteneği sergiledi (RMSE = 6482; MAE = 5476; R² ≈ 0,942). Gerçek dünya tahmin doğruluğunu değerlendirmek için, model çıktıları 2025 yılındaki gerçek nüfus değeriyle karşılaştırıldı. ARIMA modeli en düşük sapmayı (%0,28) gösterirken, doğrusal regresyon modeli %2,03 hata üretmiş, polinom modeli ise %7,74 ile daha yüksek sapma göstermiştir. Sonuçlar, tek bir modelin nüfus davranışının tüm özelliklerini tam olarak yakalayamadığını; bunun yerine, çoklu model çerçevesinin daha güvenilir ve yorumlanabilir bir tahmin yapısı sağladığını doğrulamaktadır. Yaklaşık 255.000-260.000 bireylik öngörülen nüfus aralığı, gözlemlenen eğilimlerle tutarlıdır ve kanıta dayalı bölgesel planlamayı desteklemektedir.

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Cite This Article

Karakan, A., Oğuz, Y., Demir, H. (2026). Comparative Modeling of Afyonkarahisar Population Change Using Regression and ARIMA Approaches. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, Cilt: 26 Sayı: 4, 908–917. https://doi.org/10.35414/akufemubid.1892136

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