Comparison of multi-gene genetic programming and dynamic evolving neural-fuzzy inference system in modeling pan evaporation

Eray, Okan; Mert, Cihan; Kişi, Özgür

doi:10.2166/nh.2017.076

Cited by 47 publications

(11 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where M i and E i are the modeled and experimented d s /L values, respectively, and M and E are the mean modeled and experimented d s /L values, respectively. Recently, a new goodness-of-fit index was defined by Eray et al [67] as combined accuracy (CA). It is a combination of RMSE, MAE, and R 2 as follows:…”

Section: The Goodness Of Fit Statisticsmentioning

confidence: 99%

A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

Bonakdari

Moradi

Ebtehaj

et al. 2020

Water

View full text Add to dashboard Cite

Abutment scour is a complex three-dimensional phenomenon, which is one of the leading causes of marine structure damage. Structural integrity is potentially attainable through the precise estimation of local scour depth. Due to the high complexity of scouring hydrodynamics, existing regression-based relations cannot make accurate predictions. Therefore, this study presented a novel expansion of extreme learning machines (ELM) to predict abutment scour depth (ds) in clear water conditions. The model was built using the relative flow depth (h/L), excess abutment Froude number (Fe), abutment shape factor (Ks), and relative sediment size (d50/L). A wide range of experimental samples was collected from the literature, and data was utilized to develop the ELM model. The ELM model reliability was evaluated based on the estimation results and several statistical indices. According to the results, the sigmoid activation function (correlation coefficient, R = 0.97; root mean square error, RMSE = 0.162; mean absolute percentage error, MAPE = 7.69; and scatter index, SI = 0.088) performed the best compared with the hard limit, triangular bias, radial basis, and sine activation functions. Eleven input combinations were considered to investigate the impact of each dimensionless variable on the abutment scour depth. It was found that ds/L = f (Fe, h/L, d50/L, Ks) was the best ELM model, indicating that the dimensional analysis of the original data properly reflected the underlying physics of the problem. Also, the absence of one variable from this input combination resulted in a significant accuracy reduction. The results also demonstrated that the proposed ELM model significantly outperformed the regression-based equations derived from the literature. The ELM model presented a fundamental equation for abutment scours depth prediction. Based on the simulation results, it appeared the ELM model could be used effectively in practical engineering applications of predicting abutment scour depth. The estimated uncertainty of the developed ELM model was calculated and compared with the conventional and artificial intelligence-based models. The lowest uncertainty with a value of ±0.026 was found in the proposed model in comparison with ±0.50 as the best uncertainty of the other models.

show abstract

Section: The Goodness Of Fit Statisticsmentioning

confidence: 99%

A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

Bonakdari

Moradi

Ebtehaj

et al. 2020

Water

View full text Add to dashboard Cite

show abstract

“…In recent years, numerous methods and experimental relationships have been developed for indirectly simulating evaporation [9,10]. Over the years, various studies have sought to identify linear experimental relationships [11][12][13] and non-linear experimental relationships [14][15][16][17][18][19][20] as indirect methods to simulate evaporation from free water surfaces. Some of these experimental relationships are listed in Table S1 in Supplementary Material (SM).…”

Section: Introductionmentioning

confidence: 99%

Two Comprehensive and Practical Methods for Simulating Pan Evaporation under Different Climatic Conditions in Iran

Dehghanipour

Karami

Ghazvinian

et al. 2021

Water

View full text Add to dashboard Cite

Evaporation from surface water plays a crucial role in water accounting of basins, water resource management, and irrigation systems management. As such, the simulation of evaporation with high accuracy is very important. In this study, two methods for simulating pan evaporation under different climatic conditions in Iran were developed. In the first method, six experimental relationships (linear, quadratic, and cubic, with two input combinations) were determined for Iran’s six climate types, inspired by a multilayer perceptron neural network (MLP-NN) neuron and optimized with the genetic algorithm. The best relationship of the six was selected for each climate type, and the results were presented in a three-dimensional graph. The best overall relationship obtained in the first method was used as the basic relationship in the second method, and climatic correction coefficients were determined for other climate types using the genetic algorithm optimization model. Finally, the accuracy of the two methods was validated using data from 32 synoptic weather stations throughout Iran. For the first method, error tolerance diagrams and statistical coefficients showed that a quadratic experimental relationship performed best under all climatic conditions. To simplify the method, two graphs were created based on the quadratic relationship for the different climate types, with the axes of the graphs showing relative humidity and temperature, and with pan evaporation, were drawn as contours. For the second method, the quadratic relationship for semi-dry conditions was selected as the basic relationship. The estimated climatic correction coefficients for other climate types lay between 0.8 and 1 for dry, semi-dry, semi-humid, Mediterranean climates, and between 0.4 and 0.6 for humid and very humid climates, indicating that one single relationship cannot be used to simulate pan evaporation for all climatic conditions in Iran. The validation results confirmed the accuracy of the two methods in simulating pan evaporation under different climatic conditions in Iran.

show abstract

“…Model performance in estimating the regional GR2M model parameters was compared using four statistical indices, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Pearson Correlation Coefficient (r), and Combined Accuracy (CA) 47 .…”

Section: Introductionmentioning

confidence: 99%

Using machine learning methods for supporting GR2M model in runoff estimation in an ungauged basin

Ditthakit

Pinthong

Salaeh

et al. 2021

Sci Rep

View full text Add to dashboard Cite

Estimating monthly runoff variation, especially in ungauged basins, is inevitable for water resource planning and management. The present study aimed to evaluate the regionalization methods for determining regional parameters of the rainfall-runoff model (i.e., GR2M model). Two regionalization methods (i.e., regression-based methods and distance-based methods) were investigated in this study. Three regression-based methods were selected including Multiple Linear Regression (MLR), Random Forest (RF), and M5 Model Tree (M5), and two distance-based methods included Spatial Proximity Approach and Physical Similarity Approach (PSA). Hydrological data and the basin's physical attributes were analyzed from 37 runoff stations in Thailand's southern basin. The results showed that using hydrological data for estimating the GR2M model parameters is better than using the basin's physical attributes. RF had the most accuracy in estimating regional GR2M model’s parameters by giving the lowest error, followed by M5, MLR, SPA, and PSA. Such regional parameters were then applied in estimating monthly runoff using the GR2M model. Then, their performance was evaluated using three performance criteria, i.e., Nash–Sutcliffe Efficiency (NSE), Correlation Coefficient (r), and Overall Index (OI). The regionalized monthly runoff with RF performed the best, followed by SPA, M5, MLR, and PSA. The Taylor diagram was also used to graphically evaluate the obtained results, which indicated that RF provided the products closest to GR2M's results, followed by SPA, M5, PSA, and MLR. Our finding revealed the applicability of machine learning for estimating monthly runoff in the ungauged basins. However, the SPA would be recommended in areas where lacking the basin's physical attributes and hydrological information.

show abstract

Comparison of multi-gene genetic programming and dynamic evolving neural-fuzzy inference system in modeling pan evaporation

Cited by 47 publications

References 25 publications

A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

Two Comprehensive and Practical Methods for Simulating Pan Evaporation under Different Climatic Conditions in Iran

Using machine learning methods for supporting GR2M model in runoff estimation in an ungauged basin

Contact Info

Product

Resources

About