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Precipitable water modelling using artificial neural network in Çukurova region
Ozan Senkal1, B Yiğit Yıldız, Mehmet Şahin
1Karaisalı Vocational School, Çukurova University, 01770 Karaisalı, Adana, Turkey. osenkal@cu.edu.tr
Environmental Monitoring and Assessment
|March 5, 2011
Summary
Artificial neural networks (ANNs) effectively predict precipitable water (PW) using meteorological data. This study demonstrates ANNs as a reliable alternative to radiosonde observations for atmospheric water content estimation.
Area of Science:
- Atmospheric Science
- Meteorology
- Climate Science
Background:
- Precipitable water (PW) is a crucial atmospheric variable for climate system calculations.
- Accurate measurement of PW is essential for understanding atmospheric processes and climate modeling.
- Traditional methods like radiosonde observations provide valuable but localized data.
Purpose of the Study:
- To model and predict mean precipitable water (PW) in the Çukurova region, Turkey, using an Artificial Neural Network (ANN).
- To evaluate the effectiveness of the ANN method compared to traditional radiosonde observations for PW estimation.
- To explore the use of meteorological and geographical data as inputs for PW prediction.
Main Methods:
- Application of an Artificial Neural Network (ANN) with the Levenberg-Marquardt (LM) learning algorithm and a logistic sigmoid transfer function.
- Training the ANN using historical radiosonde data (1990-2006) from Adana station, representing the Çukurova region.
- Utilizing meteorological and geographical data (altitude, temperature, pressure, humidity) as input variables for PW prediction.
Main Results:
- Achieved a high correlation coefficient (R²) of 94.00% for training and 91.84% for testing, indicating strong agreement between predicted and measured PW values.
- Demonstrated that the ANN-based prediction technique is as effective as meteorological radiosonde observations.
- Confirmed the capability of the ANN method to accurately estimate PW values.
Conclusions:
- Artificial Neural Networks (ANNs) provide a highly effective and reliable method for predicting precipitable water (PW).
- The ANN approach offers a valuable alternative or supplement to conventional radiosonde observations for PW estimation.
- The study highlights the potential of ANNs in advancing atmospheric water content analysis and climate research.
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