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Journals / Turkish Journal of Fisheries and Aquatic Sciences / 2009 / Cilt: 9 - Sayı: 2

Estimation of stream temperature in Degirmendere river (Trabzon- Turkey) using Artificial Neural Network model

Nüket SİVRİ
H. Kurtuluş ÖZCAN
O. Nuri UÇAN
Orkun AKİNCİLAR
Pages
145–150
DOI
—
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AI Summary & Key Takeaways

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

When the other literature studies which had done before have been examined, in the light of the model outputs and statistical evaluations, and regarding the complex and nonlinear structure of the study environment, it was seen that the ANN modeling technique can be utilized in the timely prediction of the temperatures of the stream waters.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Artificial Neural Networks (ANN) is a modeling technique with training which takes the working system of the brain as basis.
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Key Findings & Data:In the structure of the ANN used in the model, the number of the hidden neuron was determined as 16, Sum- Squared Error was determined as 0.005 and the number of the iteration was determined as 40,000.
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Conclusion & Impact:When the other literature studies which had done before have been examined, in the light of the model outputs and statistical evaluations, and regarding the complex and nonlinear structure of the study environment, it was seen that the ANN modeling technique can be utilized in the timely prediction of the temperatures of the stream waters.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:çevresel faktörler · nehir suyu · benzetim modelleri

Özet

Artificial Neural Networks (ANN) is a modeling technique with training which takes the working system of the brain as basis. Learning in ANNs is realized with the renewal of the connection gaps. ANNs make possible to solve the nondefined problems through the learning ability. In this study, we have estimated various environmental factors using an artificial model of the brain, known as Artificial Neural Network (ANN). Here, we has developed and tested an ANN model to predict stream temperature of Degirmendere in the Black Sea, using local water temperature, air temperature, and stream temperature. In the structure of the ANN used in the model, the number of the hidden neuron was determined as 16, Sum- Squared Error was determined as 0.005 and the number of the iteration was determined as 40,000. As a result of the regression analysis realized between the model outputs and measurement results obtained in the study, the value of r = 0.92 was calculated. When the other literature studies which had done before have been examined, in the light of the model outputs and statistical evaluations, and regarding the complex and nonlinear structure of the study environment, it was seen that the ANN modeling technique can be utilized in the timely prediction of the temperatures of the stream waters.

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Cite This Article

SİVRİ, N., ÖZCAN, H. K., UÇAN, O. N., AKİNCİLAR, O. (2009). Estimation of stream temperature in Degirmendere river (Trabzon- Turkey) using Artificial Neural Network model. Turkish Journal of Fisheries and Aquatic Sciences, Cilt: 9 - Sayı: 2, 145–150. https://ajindex.com/articles/408250

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