scholarly journals Flood Classification Based on a Fuzzy Clustering Iteration Model with Combined Weight and an Immune Grey Wolf Optimizer Algorithm

Water ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 80 ◽  
Author(s):  
Qiang Zou ◽  
Li Liao ◽  
Yi Ding ◽  
Hui Qin

Flood classification is an important basis for flood forecasting, flood risk identification, flood real-time scheduling, and flood resource utilization. However, flood classification results may be not reasonable due to uncertainty, the fuzziness of evaluation indices, and the demerit of not comprehensively considering the index weight. In this paper, based on the fuzzy clustering iterative model, a sensitivity coefficient was applied to combine the subjective and objective weights into a combined weight, then the fuzzy clustering iterative model with combined weight (FCI-CW) was proposed for flood classification. Moreover, an immune grey wolf optimizer algorithm (IGWO) based on the standard grey wolf optimizer algorithm and an immune clone selection operator was proposed for the global search of the optimal fuzzy clustering center and the sensitivity coefficient of FCI-CW. Finally, simulation results at Nanjing station and Yichang station demonstrate that the proposed methodology, i.e., FCI-CW combined with IGWO, is reasonable and reliable, can effectively deal with flood classification problems with better fitness and a comprehensive consideration of the subjective and objective aspects, and has great application potential in sorting, evaluation, and decision-making problems without evaluation criteria.

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1192
Author(s):  
Randall Claywell ◽  
Laszlo Nadai ◽  
Imre Felde ◽  
Sina Ardabili ◽  
Amirhosein Mosavi

The accurate prediction of the solar diffuse fraction (DF), sometimes called the diffuse ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse irradiance research is discussed and then three robust, machine learning (ML) models are examined using a large dataset (almost eight years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid adaptive network-based fuzzy inference system (ANFIS), a single multi-layer perceptron (MLP) and a hybrid multi-layer perceptron grey wolf optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various solar and DF irradiance data, from Spain. The results were then evaluated using frequently used evaluation criteria, the mean absolute error (MAE), mean error (ME) and the root mean square error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance in both the training and the testing procedures.


Author(s):  
Randall Claywell ◽  
Nadai Laszlo ◽  
Felde Imre ◽  
Amir Mosavi

The accurate prediction of the solar Diffuse Fraction (DF), sometimes called the Diffuse Ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse Irradiance research is discussed and then three robust, Machine Learning (ML) models, are examined using a large dataset (almost 8 years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid Adaptive Network-based Fuzzy Inference System (ANFIS), a single Multi-Layer Perceptron (MLP) and a hybrid Multi-Layer Perceptron-Grey Wolf Optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various Solar and Diffuse Fraction (DF) irradiance data, from Spain. The results were then evaluated using two frequently used evaluation criteria, the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance, in both the training and the testing procedures.


2020 ◽  
Author(s):  
Randall Claywell ◽  
Laszlo Nadai ◽  
Imre Felde ◽  
Amir Mosavi

Abstract The accurate prediction of the solar Diffuse Fraction (DF), sometimes called the Diffuse Ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse Irradiance research is discussed and then three robust, Machine Learning (ML) models, are examined using a large dataset (almost 8 years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid Adaptive Network-based Fuzzy Inference System (ANFIS), a single Multi-Layer Perceptron (MLP) and a hybrid Multi-Layer Perceptron-Grey Wolf Optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various Solar and Diffuse Fraction (DF) irradiance data, from Spain. The results were then evaluated using two frequently used evaluation criteria, the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance, in both the training and the testing procedures.


2020 ◽  
Author(s):  
Randall Claywell ◽  
Laszlo Nadai ◽  
Felde Imre ◽  
Amir Mosavi

The accurate prediction of the solar Diffuse Fraction (DF), sometimes called the Diffuse Ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse Irradiance research is discussed and then three robust, Machine Learning (ML) models, are examined using a large dataset (almost 8 years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid Adaptive Network-based Fuzzy Inference System (ANFIS), a single Multi-Layer Perceptron (MLP) and a hybrid Multi-Layer Perceptron-Grey Wolf Optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various Solar and Diffuse Fraction (DF) irradiance data, from Spain. The results were then evaluated using two frequently used evaluation criteria, the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance, in both the training and the testing procedures.


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