The aim of this study was to model, as well as monitor and assess the surface water quality in the Eastern Black Sea (EBS) Basin stream, Turkey. The water-quality indicators monitored monthly for the seven streams were water temperature (WT), pH, total dissolved solids (TDS), and electrical conductivity (EC), as well as luminescent dissolved oxygen (LDO) concentration and saturation. Based on an 18-month data monitoring, the surface water quality variation was spatially and temporally evaluated with reference to the Turkish Surface Water Quality Regulation. First, the teaching–learning based optimization (TLBO) algorithm and conventional regression analysis (CRA) were applied to three different regression forms, i.e., exponential, power, and linear functions, to predict LDO concentrations. Then, the multivariate adaptive regression splines (MARS) method was employed and three performance measures, namely, mean absolute error (MAE), root means square error (RMSE), and Nash Sutcliffe coefficient of efficiency (NSCE) were used to evaluate the performances of the MARS, TLBO, and CRA methods. The monitoring results revealed that all streams showed the same trend in that lower WT values in the winter months resulted in higher LDO concentrations, while higher WT values in summer led to lower LDO concentrations. Similarly, autumn, which presented the higher TDS concentrations brought about higher EC values, while spring, which presented the lower TDS concentrations gave rise to lower EC values. It was concluded that the water quality of the streams in the EBS basin was high-quality water in terms of the parameters monitored in situ, of which the LDO concentration varied from 9.13 to 10.12 mg/L in summer and from 12.31 to 13.26 mg/L in winter. When the prediction accuracies of the three models were compared, it was seen that the MARS method provided more successful results than the other methods. The results of the TLBO and the CRA methods were very close to each other. The RMSE, MAE, and NSCE values were 0.2599 mg/L, 0.2125 mg/L, and 0.9645, respectively, for the best MARS model, while these values were 0.4167 mg/L, 0.3068 mg/L, and 0.9086, respectively, for the best TLBO and CRA models. In general, the LDO concentration could be successfully predicted using the MARS method with various input combinations of WT, EC, and pH variables.
The management and operation of the wastewater treatment plants (WWTP) have an important role in the controlling and monitoring of the plants' operations. Various performance data are taken into account in the controlling of the WWTP. The irregularities between operating parameters often lead to management problems that cannot be overcome. The aim of this study is to provide a simple and reliable prediction model to estimate the biochemical oxygen demand (BOD) with specific water quality parameters like wastewater temperature, pH, chemical oxygen demand, suspended sediment, total nitrogen, total phosphorus, electrical conductivity, and input discharge. The data records in this study were measured between June 2015 and May 2016 and obtained from the laboratory of Antalya Hurma WWTP. In the creation of the model, classical regression analysis, multivariate adaptive regression splines (MARS), artificial bee colony, and teaching-learning based optimization were used. The root mean square error and the mean absolute error were used to evaluate performance criteria for each model. When the results of the analyses were compared with each other, it was observed that the MARS method gave better estimation results than the other methods used in the study. As a result, it was evinced that the MARS method produces acceptable results in the BOD estimation.
Bu çalışmada bir akarsu havzasında takibi yapılan askıda katı madde (AKM) konsantrasyonu kapsamında, mansap verilerinden memba değerlerinin tahmin edilebilirliği hem regresyon analizinin doğrusal, üs, üstel ve kuadratik fonksiyonlara uygulanması hem de yapay sinir ağları (YSA) yöntemi ile araştırılmıştır. Kullanılan veriler Sera Deresi Havzası’nda (Trabzon) seçilen sekiz gözlem istasyonunda Haziran 2019-Mart 2020 döneminde 40 kez gerçekleştirilen örnekleme çalışmaları kapsamında elde edilmiş AKM verileridir. İstasyonlar memba (ilk dördü) ve mansap (son dördü) olarak iki gruba ayrılmıştır. Mansap verilerinin %50’si (iki istasyon) eğitim, %25’i (bir istasyon) doğrulama ve kalan %25’i (bir istasyon) test aşamasında kullanılmıştır. Farklı bağımsız değişken kombinasyonlarına sahip iki model oluşturulmuş olup ilk modelde (M1) sadece AKM verileri, diğer modelde (M2) ise AKM verilerinin yanı sıra örnekleme tarihlerine ait ay ve hafta bilgileri sayısallaştırılmış ve kullanılmıştır. Modellerin ve yöntemlerin tahmin performanslarının değerlendirilmesinde ortalama karesel hatanın karekökü, ortalama mutlak hata ve Nash-Sutcliffe (NS) verimlilik katsayısı olmak üzere üç farklı istatistik kullanılmıştır. Regresyon analizinde en iyi tahmin sonuçları üs fonksiyondan elde edilmiş olup YSA yönteminin regresyon analizine kıyasla daha iyi sonuçlar verdiği belirlenmiştir. Her iki yöntemde de M2 genel olarak daha iyi bir performans göstermiştir. YSA yönteminde M1 ve M2’den hesap edilen NS verimlilik katsayıları eğitim veri seti için sırasıyla 0.980 ve 0.997 ve test veri seti için ise 0.978 ve 0.978 olarak hesaplanmıştır. Bu değerler ile AKM modelleme çalışmalarında, gerçek verilerin ait olduğu tarih bilgilerinin bağımsız değişken olarak kullanımının model performansını olumlu etkileyeceği anlaşılmıştır. Bu çalışma kapsamında, akarsu havzalarının mansap tarafı AKM verilerinden memba tarafı AKM değerlerinin başarılı bir şekilde tahmin edilebileceği sonucuna ulaşılmıştır.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.