scholarly journals Evaluation of different data management scenarios for estimating daily reference evapotranspiration

2013 ◽  
Vol 44 (6) ◽  
pp. 1058-1070 ◽  
Author(s):  
Jalal Shiri ◽  
Ali Ashraf Sadraddini ◽  
Amir Hossein Nazemi ◽  
Ozgur Kisi ◽  
Pau Marti ◽  
...  
Keyword(s):  
Data Management ◽  
Fuzzy Inference ◽  
Reference Evapotranspiration ◽  
Gene Expression Programming ◽  
Local Data ◽  
Management Scenarios ◽  
Inference System ◽  
Pooled Data ◽  
Temperature Radiation ◽  
Better Than

Temperature and solar radiation-based modeling procedures are reported in this study for estimating daily reference evapotranspiration (ET0) by using gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS). A comparison is also made among these techniques and the corresponding traditional temperature/radiation-based ET0 estimation equations. Two data management scenarios were evaluated for estimating ET0: (1) the models were trained and tested using the local data of each studied weather station; and (2) the models were trained using the pooled data from all the stations and tested in each individual station. The GEP and ANFIS models were found to be better than the Hargreaves–Samani, Makkink and Turc ET0 equations in the first scenario. Comparison of GEP and ANFIS models trained with pooled data and tested for each station showed that the ANFIS models generally performed better than the GEP models. However, the comparison of GEP and ANFIS models trained and tested with pooled data revealed that the GEP models performed better than the ANFIS models in the second scenario.

scholarly journals A comparison of artificial intelligence models for the estimation of daily suspended sediment load: a case study on the Telar and Kasilian rivers in Iran

2018 ◽  
Vol 19 (1) ◽  
pp. 165-178 ◽  
Author(s):  
Samad Emamgholizadeh ◽  
Razieh Karimi Demneh
Keyword(s):  
Suspended Sediment ◽  
Sediment Load ◽  
Fuzzy Inference ◽  
Gene Expression Programming ◽  
Coefficient Of Determination ◽  
Suspended Sediment Load ◽  
Inference System ◽  
Sediment Rating Curve ◽  
Intelligence Models ◽  
Better Than

Abstract The estimation of the suspended sediment load in rivers is one of the main issues in hydraulic engineering. Different traditional methods such as the sediment rating curve (SRC) can be used to estimate the suspended sediment load of rivers. The main problem with this method is its low accuracy and uncertainty. In this study, the ability of three intelligence models namely: gene expression programming (GEP), artificial neural networks (ANN) and adaptive neuro fuzzy inference system (ANFIS) were compared with the SRC method. The daily flow discharge and sediment discharge at two hydrometric stations of the Kasilian and Telar rivers in the period of 1964–2014 were used to develop intelligence models. The performance of these methods indicated that all intelligence models give reliable results in the estimation of the suspended sediment load and their performance was better than the SRC method. Moreover, results showed that the GEP model with a high coefficient of determination (R2) and a low mean absolute error (MAE) was better than both the ANN and ANFIS models for the estimation of daily suspended sediment load of the two sub-basins of the Kasilian and Telar rivers.

scholarly journals Novel Hybrid Data-Intelligence Model for Forecasting Monthly Rainfall with Uncertainty Analysis

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 502 ◽  
Author(s):  
Zaher Yaseen ◽  
Isa Ebtehaj ◽  
Sungwon Kim ◽  
Hadi Sanikhani ◽  
H. Asadi ◽  
...  
Keyword(s):  
Uncertainty Analysis ◽  
Fuzzy Inference ◽  
Monthly Rainfall ◽  
Rainfall Time Series ◽  
Anfis Model ◽  
Inference System ◽  
Forecasting Models ◽  
Neuro Fuzzy ◽  
Hybrid Data ◽  
Better Than

In this research, three different evolutionary algorithms (EAs), namely, particle swarm optimization (PSO), genetic algorithm (GA) and differential evolution (DE), are integrated with the adaptive neuro-fuzzy inference system (ANFIS) model. The developed hybrid models are proposed to forecast rainfall time series. The capability of the proposed evolutionary hybrid ANFIS was compared with the conventional ANFIS in forecasting monthly rainfall for the Pahang watershed, Malaysia. To select the optimal model, sixteen different combinations of six different lag attributes taking into account the effect of monthly, seasonal, and annual history were considered. The performances of the forecasting models were assessed using various forecasting skill indicators. Moreover, an uncertainty analysis of the developed forecasting models was performed to evaluate the ability of the hybrid ANFIS models. The bound width of 95% confidence interval (d-factor) and the percentage of observed samples which was enveloped by 95% forecasted uncertainties (95PPU) were used for this purpose. The results indicated that all the hybrid ANFIS models performed better than the conventional ANFIS and for all input combinations. The obtained results showed that the models with best input combinations had the (95PPU and d-factor) values of (91.67 and 1.41), (91.03 and 1.41), (89.74 and 1.42), and (88.46 and 1.43) for ANFIS-PSO, ANFIS-GA, ANFIS-DE, and the conventional ANFIS, respectively. Based on the 95PPU and d-factor, it is concluded that all hybrid ANFIS models have an acceptable degree of uncertainty in forecasting monthly rainfall. The results of this study proved that the hybrid ANFIS with an evolutionary algorithm is a reliable modeling technique for forecasting monthly rainfall.

scholarly journals Ranking of hybrid wavelet-AI models by TOPSIS method for estimation of daily flow discharge

2020 ◽  
Vol 20 (8) ◽  
pp. 3156-3171
Author(s):  
Hiwa Farajpanah ◽  
Morteza Lotfirad ◽  
Arash Adib ◽  
Hassan Esmaeili-Gisavandani ◽  
Özgur Kisi ◽  
...  
Keyword(s):  
Fuzzy Inference ◽  
Gene Expression Programming ◽  
Least Square ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Topsis Method ◽  
M5 Model Tree ◽  
Inference System ◽  
Flow Discharge ◽  
Discharge Temperature

Abstract This research uses the multi-layer perceptron–artificial neural network (MLP-ANN), radial basis function–ANN (RBF-ANN), least square support vector machine (LSSVM), adaptive neuro-fuzzy inference system (ANFIS), M5 model tree (M5T), gene expression programming (GEP), genetic programming (GP) and Bayesian network (BN) with five types of mother wavelet functions (MWFs: coif4, db10, dmey, fk6 and sym7) and selects the best model by the TOPSIS method. The case study is the Navrood watershed in the north of Iran and the considered parameters are daily flow discharge, temperature and precipitation during 1991 to 2018. The derived results show that the best method is the hybrid of the M5T model with sym7 wavelet function. The MWFs were decomposed by discrete wavelet transform (DWT). The combination of AI models and MWFs improves the correlation coefficient of MLP, RBF, LSSVM, ANFIS, GP, GEP, M5T and BN by 8.05%, 4.6%, 8.14%, 8.14%, 22.97%, 7.5%, 5.75% and 10% respectively.

scholarly journals Assessment of global hydro-social indicators in water resources management

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Omid Bozorg-Haddad ◽  
Sahar Baghban ◽  
Hugo A. Loáiciga
Keyword(s):  
Water Resources ◽  
Fuzzy Inference ◽  
Human Life ◽  
Gene Expression Programming ◽  
Social Indicators ◽  
Absolute Error ◽  
Computing Methods ◽  
Climatic Conditions ◽  
Inference System ◽  
Spatial And Temporal Scales

AbstractWater is a vital element that plays a central role in human life. This study assesses the status of indicators based on water resources availability relying on hydro-social analysis. The assessment involves countries exhibiting decreasing trends in per capita renewable water during 2005–2017. Africa, America, Asia, Europe, and Oceania encompass respectively 48, 35, 43, 20, and 5 countries with distinct climatic conditions. Four hydro-social indicators associated with rural society, urban society, technology and communication, and knowledge were estimated with soft-computing methods [i.e., artificial neural networks, adaptive neuro-fuzzy inference system, and gene expression programming (GEP)] for the world’s continents. The GEP model’s performance was the best among the computing methods in estimating hydro-social indicators for all the world’s continents based on statistical criteria [correlation coefficient (R), root mean square error (RMSE), and mean absolute error]. The values of RMSE for GEP models for the ratio of rural to urban population (PRUP), population density, number of internet users and education index parameters equaled (0.084, 0.029, 0.178, 0.135), (0.197, 0.056, 0.152, 0.163), (0.151, 0.036, 0.123, 0.210), (0.182, 0.039, 0.148, 0.204) and (0.141, 0.030, 0.226, 0.082) for Africa, America, Asia, Europe and Oceania, respectively. Scalable equations for hydro-social indicators are developed with applicability at variable spatial and temporal scales worldwide. This paper’s results show the patterns of association between social parameters and water resources vary across continents. This study’s findings contribute to improving water-resources planning and management considering hydro-social indicators.

scholarly journals Comparison of wavelet-based hybrid models for the estimation of daily reference evapotranspiration in different climates

2018 ◽  
Vol 11 (1) ◽  
pp. 39-53 ◽  
Author(s):  
Alireza Araghi ◽  
Jan Adamowski ◽  
Christopher J. Martinez
Keyword(s):  
Good Accuracy ◽  
Fuzzy Inference ◽  
Reference Evapotranspiration ◽  
Hybrid Models ◽  
Detectable Effect ◽  
Negative Effects ◽  
Inference System ◽  
Large Numbers ◽  
Input Variables ◽  
Different Climates

Abstract Reference evapotranspiration (ETo) is one of the most important factors in the hydrologic cycle and water balance studies. In this study, the performance of three simple and three wavelet hybrid models were compared to estimate ETo in three different climates in Iran, based on different combinations of input variables. It was found that the wavelet-artificial neural network was the best model, and multiple linear regression (MLR) was the worst model in most cases, although the performance of the models was related to the climate and the input variables used for modeling. Overall, it was found that all models had good accuracy in terms of estimating daily ETo. Also, it was found in this study that large numbers of decomposition levels via the wavelet transform had noticeable negative effects on the performance of the wavelet-based models, especially for the wavelet-adaptive network-based fuzzy inference system and wavelet-MLR, but in contrast, the type of db wavelet function did not have a detectable effect on the performance of the wavelet-based models.

Identification and Equalization of Nonlinear Channel Based on ANFIS

2012 ◽  
Vol 195-196 ◽  
pp. 240-244
Author(s):  
Qiang Qu ◽  
Xiao Li Wang ◽  
Xue Bo Chen
Keyword(s):  
High Speed ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Least Square ◽  
Identification Accuracy ◽  
Bp Network ◽  
Inference System ◽  
Cluster Partition ◽  
Better Than

The demand of high-speed communication leads that the application for resource of channel exceeds the range of linear model. The channel should be described as a nonlinear model. So, the paper uses the adaptive neuron-fuzzy inference system (ANFIS) to identify and equalize the nonlinear channel of the high-speed communication system. Meanwhile, the subtract cluster is applied to identify the construction of the ANFIS and the hybrid learning algorithm based on the least square and back-propagation is used to train network. The simulation results show that the convergence rate and identification accuracy of the ANFIS are better than BP network and the efficiency of the ANFIS based on subtract cluster partition algorithm is higher than that of the ANFIS based on the grid partition algorithm.

scholarly journals Comparison of neural networks and neuro-fuzzy computing techniques for prediction of peak breach outflow

2016 ◽  
Vol 18 (4) ◽  
pp. 724-740 ◽  
Author(s):  
Hasan G. Elmazoghi ◽  
Vail Karakale (Waiel Mowrtage) ◽  
Lubna S. Bentaher
Keyword(s):  
Fuzzy Inference ◽  
Gene Expression Programming ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Regression Equations ◽  
Data Set ◽  
Inference System ◽  
Embankment Dams ◽  
Small Dams ◽  
Neuro Fuzzy

Accurate prediction of peak outflows from breached embankment dams is a key parameter in dam risk assessment. In this study, efficient models were developed to predict peak breach outflows utilizing artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Historical data from 93 embankment dam failures were used to train and evaluate the applicability of these models. Two scenarios were applied with each model by either considering the whole data set without classification or classifying the set into small dams (48 dams) and large dams (45 dams). In this way, nine models were developed and their results were compared to each other and to the results of the best available regression equations and recent gene expression programming. Among the different models, the ANFIS model of the first scenario exhibited better performance based on its higher efficiency (E = 0.98), higher coefficient of determination (R2 = 0.98) and lower mean absolute error (MAE = 840.9). Moreover, models based on classified data enhanced the prediction of peak outflows particularly for small dams. Finally, this study indicated the potential of the developed ANFIS and ANN models to be used as predictive tools of peak outflow rates of embankment dams.

Yarn Strength Modelling Using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Gene Expression Programming (GEP)

2013 ◽  
Vol 8 (4) ◽  
pp. 155892501300800 ◽  
Author(s):  
A.R. Fallahpour ◽  
A.R. Moghassem
Keyword(s):  
Gene Expression ◽  
Root Mean Square Error ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Breaking Strength ◽  
Gene Expression Programming ◽  
Mean Square ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy

This study compares capabilities of two different modelling methodologies for predicting breaking strength of rotor spun yarns. Forty eight yarn samples were produced considering variations in three drawing frame parameters namely break draft, delivery speed, and distance between back and middle rolls. Several topologies with different architectures were trained to get the best adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP) models. Prediction performance of the GEP model was compared with that of ANFIS using root mean square error (RMSE) and correlation coefficient (R2-Value) parameters on the test data. Results show that, the GEP model has a significant priority over the ANFIS model in term of prediction accuracy. The correlation coefficient (R2-value) and root mean square error for the GEP model were 0.87 and 0.35 respectively, while these parameters were 0.48 and 0.53 for the ANFIS model. Also, a mathematical formula was developed with high degree of accuracy using GEP algorithm to predict the breaking strength of the yarns. This advantage is not accessible in the ANFIS model.

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