Modeling of the evaporation process using the hybrid algorithm of the COOT bird and artificial neural network

Introduction

Evaporation is a vital natural phenomenon in the hydrological cycle and reflects the interaction between the sea and the air. Due to global warming, accurately estimating evaporation has become a key challenge in water resource management, and due to the important role it plays in the withdrawal of water from human reach, it has always attracted the attention of researchers. Various tools such as satellite measurements, automatic systems and numerical models are used for estimating evaporation. Therefore, modeling and awareness of the value of evaporation as one of the hydrological variables is of great importance in agricultural research and soil and water conservation. The rate of evaporation in each city depends on multiple factors, such as temperature, humidity, wind speed, and solar radiation. Gorgan, Shiraz, and Kish were selected for the study because Gorgan is located near the Caspian Sea with a humid climate and has a higher evaporation rate compared to other cities. On the other hand, Shiraz has a hot and dry climate, is located in central Iran far from water resources such as seas, and has a lower evaporation rate. Kish also has a warm and humid climate due to its proximity to the sea, with a lower evaporation rate than Shiraz but higher than Gorgan. Several meteorological variables affect the process of evaporation and transpiration, and due to the complexity of the evaporation parameter, a method with high accuracy should be used to determine it. Recently, artificial neural network methods have become very popular among researchers due to their common use and the ease with which they can solve complex problems. Therefore, recently, many intelligent algorithms have been suggested to find the best solution for complex engineering problems, as they can find optimal answers faster and more accurately.



Materials and Methods

Artificial neural networks are designed based on inspiration from the memory and learning mechanisms in the human brain. To train artificial neural networks, a set of valid input and output data is used based on the type of problem. The accuracy of the network output depends on the amount of training data and how the inputs and their features are processed. In order to design different scenarios for adjusting input data, the correlation values of the data with evaporation were used.

In this study, three synoptic stations with different climates, including Gorgan, Shiraz, and Kish, were chosen. three stations with different climates were used in order to better evaluate and repeat the steps of the method, so that the efficiency of the method could be more accurately assessed. Considering the importance of the value of evaporation in nature, evaporation modeling with the ANN and its combination with the COOT algorithm, which mimics the natural life of a COOT bird, was performed using five meteorological parameters, including the minimum air temperature, maximum air temperature, wind speed, average relative humidity, and sunshine hours on a monthly between 2000 and 2022. The dataset was divided into two phases of training (70% of dataset) and testing (30% of dataset). To evaluate the performance of developed models, statistical indices of these models such as correlation coefficient (R), root mean square error (RMSE), Nash-Sutcliffe coefficient (N-S), and their graphical representations were compared with each other.



Results and Discussion

Comparing of obtained results from statistical indices for the testing phase revealed that the COOT-ANN4 model had the best performance for Gorgan with the R, RMSE and N-S equals to 0.99, 8.19 and 0.99 respectively. For Shiraz also obtained values of the R, RMSE and N-S equals to 0.99, 18.43 and 0.98 respectively. Similarly for Kish, the values of the R, RMSE and N-S equals to 0.97, 19.36 and 0.93 respectively was obtained that have better performance than the other models. Additionally, to evaluate accuracy of developed models, Scatter plots, Violin plots, Relative error percent plots (RE%), Taylor diagrams, and Histograms were drawn. By comparing the graphical representations, it can be determined that the hybrid COOT with ANN4 namely COOT-ANN4 model has improved the artificial neural network at Gorgan, Shiraz, and Kish stations.



Conclusion

algorithm of the COOT is an optimization algorithm that is generally used to solve optimization problems. As observed from the overall performance of the results of the hybrid model in predicting evaporation, the objective function was minimized. The results indicated that scenario 4 of the COOT-ANN4 hybrid model with input parameters of minimum temperature, maximum temperature, sunshine hours, relative humidity, and wind speed has better accuracy and performance at all three stations. In general, the findings of this study revealed that the COOT algorithm can improve the artificial neural network (ANN) structure in any climate and provide a hybrid model with higher accuracy and less error for modeling the evaporation parameter. Considering that the COOT algorithm is a powerful and efficient algorithm, it is better to use it in various fields to improve the performance and accuracy of models.