GÜNLÜK ÇÖZÜNMÜŞ OKSİJEN KONSANTRASYONUNUN ÇOK DEĞİŞKENLİ UYARLANABİLİR REGRESYON EĞRİLERİ İLE TAHMİN EDİLMESİ

Abstract

Bu çalışmada su sıcaklığı (T), özgül iletkenlik (Öİ) verilerinden hesaplanmış elektriksel iletkenlik (Eİ), pH ve debi (Q) verileri kullanılarak çok değişkenli uyarlanabilir regresyon eğrileri (MARS) ve regresyon analizi (RA) yöntemleri ile ÇO konsnatrasyonunun tahmin edilmesi amaçlanmıştır. MARS yönteminde en iyi tahmin değerlerini üreten temel fonksiyonlar ve denklemler belirlenmiş, RA yöntemi doğrusal, üs, üstel ve kuadratik olmak üzere dört farklı fonksiyona uygulanmış ve bu fonksiyonlara ait katsayılar hesaplanmıştır. Modelleme çalışmalarında Amerika Birleşik Devletleri’nin Oregon eyaletinin kuzey batısında yer alan Willamette Nehri’nin yan kollarından biri olan ve yaklaşık 2435 km2’lik bir havza alanına sahip Clackamas Nehri’ne ait Eylül 2016 − Ağustos 2017 dönemi günlük ortalama verileri kullanılmıştır. Her bir su kalitesi değişkeninin ÇO konsantrasyonu tahmin performansına etkisini belirlemek amacıyla sekiz farklı model oluşturulmuştur. ÇO konsantrasyonu tahmininde kurulan modellerin ve kullanılan yöntemlerin performanslarının değerlendirilebilmesi için çeşitli istatistikler (ortalama karesel hatanın karekökü, ortalama mutlak hata, saçılım indeksi ve Nash Sutcliffe verimlilik katsayısı) kullanılmıştır. Modelleme çalışmalarından elde edilen sonuçlar irdelendiğinde, MARS yönteminin RA yönteminden daha iyi sonuçlar verdiği anlaşılmıştır. Regresyon fonksiyonları içerisinden ise en başarılı tahmin sonuçlarının kuadratik fonksiyondan elde edildikleri ve MARS yöntemi ile elde edilen değerlere de oldukça yakın oldukları görülmüştür. ÇO konsantrasyonu tahmininde en etkili değişkenlerin T ve Q oldukları dolayısıyla en etkisiz değişkenlerin ise Eİ ve pH oldukları anlaşılmıştır. Model 3, Model 5, Model 7 ve Model 8’den elde edilen sonuçların birbirine çok yakın olması sebebiyle daha az değişken ile güçlü tahminler yapması ve daha sade bir model olması bakımından ÇO tahmininde Model 3’ün kullanılmasının daha avantajlı olacağı sonucuna varılmıştır. 

Keywords

• Çok değişkenli uyarlanabilir regresyon eğrileri
• Çözünmüş oksijen konsantrasyonu
• Regresyon analizi

Estimation of Daily Dissolved Oxygen Concentration using Multivariate Adaptive Regression Splines Methods

Abstract

In this study, it is aimed to estimate DO concentration using the river water temperature (WT), electrical conductivity (EC) computed from specific conductance (SC), pH, and discharge (Q) data by employing multivariate adaptive regression splines (MARS) and regression analysis (RA) methods. For this purpose, the basic functions and equations, which yielded the best estimation values in the MARS method, were determined. The RA method was applied to four different functions, namely linear, power, exponential, and quadratic, and the coefficients for these functions were computed. Daily mean data for a period from September 2016 to August 2017 were used in DO modeling studies for the Clackamas River having a basin area of approximately 2435 km2, which is one of the tributaries of the Willamette River located in the northwestern state of Oregon, USA. Eight different models were generated to determine the effect of each water-quality parameter on the estimation performance of the river DO concentration. In order to evaluate the performances of the methods and the models used in estimating the river DO concentration, various statistics, e.g. the root mean square error, mean absolute error, scatter index, and Nash Sutcliffe coefficient of efficiency, were used. When the results from the modeling efforts were evaluated, it was seen that the MARS method provided better results than RA method. It was also seen that the most successful estimation results were provided by quadratic function among the regression functions and were also quite close to estimation results provided by the MARS method. It was revealed WT and Q parameters were highly effective, that is to say, EC and pH parameters were highly ineffective in estimating the river DO concentration. The estimation results obtained from Model 3, Model 5, Model7, and Model 8 were very close to each other. It was concluded that Model 3 with less parameters would be more advantageous to use in the estimation of the river DO concentration owing to being a simpler model but making strong estimations.

Keywords

• Multivariate adaptive regression splines
• Dissolved oxygen concentration
• Regression analysis

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