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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
In the MR analysis, R2 was determined as 0.63, and a middling significant (p
📌 3 Key Takeaways
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Objective & Hypothesis:The aim of this study is to estimate of organic matter values based on chlorine and turbidity values with the help of ANN and multiple regression (MR) methods.
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Key Findings & Data:Three different models were done with ANN, and the statistical performance of these models was evaluated with statistical parameters like; μ, SE, σ, R2, RMSE and MAPE.
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Conclusion & Impact:In the MR analysis, R2 was determined as 0.63, and a middling significant (p
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters
Abstract
The aim of this study is to estimate of organic matter values based on chlorine and turbidity values with the help of ANN and multiple regression (MR) methods. Three different models were done with ANN, and the statistical performance of these models was evaluated with statistical parameters like; μ, SE, σ, R2, RMSE and MAPE. The R2 value of the selected best model was found to be quite high with 0.94. The relationship between the evaluation results of the ANN model and the empirical data (R2 = 0.92) showed that the model was quite successful. In the MR analysis, R2 was determined as 0.63, and a middling significant (p
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Cite This Article
YILDIZ, S., KARAKUŞ, C. B. (2022). Estimation of organic matter dependent on different variables in drinking water network using artificial neural network and multiple regression methods. Cumhuriyet Science Journal, Cilt: 42 - Sayı: 2, 441–451. https://ajindex.com/articles/477626