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Showing 2 results for Bayesian Network
Evaluation of Experimental Models and Artificial Intelligence in Estimation of Reference Evapotranspiration (Case Study: Boroujerd Station)
Y. Sabzevari, M. Saeidinia,
Volume 25, Issue 2 (9-2021)
Abstract
The FAO Penman-Monteith is a baseline method to estimate reference evapotranspiration. In many cases, it is difficult to access all data, so replacing simpler models with ‎lower input data and appropriate accuracy is necessary. ‎ The purpose of this study is to investigate the capability of the experimental ‎models, gene expression programming, stepwise regression, and Bayesian network in estimating ‎reference evapotranspiration.‎ In this research, daily information of the Boroujerd synoptic station in the period of 1996 -2017 was used as model inputs. ‎Based on the correlation between input and output parameters, six input patterns were ‎determined for modeling. The results showed that the Kimberly-Penman model has the ‎best performance among the experimental models.‎ Gene expression programming with fourth pattern ‎‎and Default Model Operators (R2 = 0.98 and RMSE = 0.9), Bayesian Network with sixth pattern (R2=0.91 and RMSE = 1.01), and stepwise regression with sixth pattern have the most accurate patterns at R2 = 0.91 and RMSE = 0.9 in the ‎training stage.‎ Comparison of the performance of the three models showed that the gene expression ‎programming model was superior to the other two models with the Average Absolute Relative Error (AARE) of 0.12 and the Mean Ratio (MR) of 0.94.‎ The results showed that gene expression programming had an acceptable ability to estimate ‎reference evapotranspiration under the weather conditions of Boroujerd and could be introduced as a ‎suitable model.‎

Comparison of Artificial Neural Network, Decision Tree and Bayesian Network Models in Regional Flood Frequency Analysis using L-moments and Maximum Likelihood Methods in Karkheh and Karun Watersheds
K. Ghaderi, B. Motamedvaziri, M. Vafakhah, A.a. Dehghani,
Volume 25, Issue 4 (3-2022)
Abstract
Proper flood discharge forecasting is significant for the design of hydraulic structures, reducing the risk of failure, and minimizing downstream environmental damage. The objective of this study was to investigate the application of machine learning methods in Regional Flood Frequency Analysis (RFFA). To achieve this goal, 18 physiographic, climatic, lithological, and land use parameters were considered for the upstream basins of the hydrometric stations located in Karkheh and Karun watersheds (46 stations with a statistical length of 21 years). The best Probability Distribution Function (pdf) was then determined using the Kolmogorov-Smirnov test at each station to estimate the flood discharge with a return period of 50-year using maximum likelihood methods and L-moments. Finally, RFFA was performed using a decision tree, Bayesian network, and artificial neural network. The results showed that the log Pearson type 3 distribution in the maximum likelihood method and the generalized normal distribution in the L moment method are the best possible regional pdfs. Based on the gamma test, the parameters of the perimeter, basin length, shape factor, and mainstream length were selected as the best input structure. The results of regional flood frequency analysis showed that the Bayesian model with the L moment method (R2 = 0.7) has the best estimate compared to other methods. Decision tree and artificial neural network were in the following ranks.

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