Predicting river daily flow using wavelet-artificial neural networks based on regression analyses in comparison with artificial neural networks and support vector machine models

This study investigates the ability of wavelet-artificial neural networks (WANN) for the prediction of short-term daily river flow. The WANN model is improved by conjunction of two methods, discrete wavelet transform and artificial neural networks (ANN) based on regression analyses, respectively. The proposed WANN models are applied to the daily flow data of Vanyar station, on the Ajichai River in the northwest region of Iran, and compared with the ANN and support vector machine (SVM) techniques. Mean square error (MSE), mean absolute error (MAE) and correlation coefficient (R) statistics are used for evaluating precision of the WANN, ANN and SVM models. Comparison results demonstrate that the WANN model performs better than the ANN and SVM models in short-term (1-, 2- and 3-day ahead) daily river flow prediction.

     

Non-tuned machine learning approach for hydrological time series forecasting

Stream-flow forecasting is a crucial task for hydrological science. Throughout the literature, traditional and artificial intelligence models have been applied to this task. An attempt to explore and develop better expert models is an ongoing endeavor for this hydrological application. In addition, the accuracy of modeling, confidence and practicality of the model are the other significant problems that need to be considered. Accordingly, this study investigates modern non-tuned machine learning data-driven approach, namely extreme learning machine (ELM). This data-driven approach is containing single layer feedforward neural network that selects the input variables randomly and determine the output weights systematically. To demonstrate the reliability and the effectiveness, one-step-ahead stream-flow forecasting based on three time-scale pattern (daily, mean weekly and mean monthly) for Johor river, Malaysia, were implemented. Artificial neural network (ANN) model is used for comparison and evaluation. The results indicated ELM approach superior the ANN model level accuracies and time consuming in addition to precision forecasting in tropical zone. In measureable terms, the dominance of ELM model over ANN model was indicated in accordance with coefficient determination (R ²) root-mean-square error (RMSE) and mean absolute error (MAE). The results were obtained for example the daily time scale R ² = 0.94 and 0.90, RMSE = 2.78 and 11.63, and MAE = 0.10 and 0.43, for ELM and ANN models respectively.

     

Performance evaluation of hybrid FFA-ANFIS and GA-ANFIS models to predict particle size distribution of a muck-pile after blasting

Accurately predicting the particle size distribution of a muck-pile after blasting is always an important subject for mining industry. Adaptive neuro-fuzzy inference system (ANFIS) has emerged as a synergic intelligent system. The main contribution of this paper is to optimize the premise and consequent parameters of ANFIS by firefly algorithm (FFA) and genetic algorithm (GA). To the best of our knowledge, no research has been published that assesses FFA and GA with ANFIS for fragmentation prediction and no research has tested the efficiency of these models to predict the fragmentation in different time scales as of yet. To show the effectiveness of the proposed ANFIS-FFA and ANFIS-GA models, their modelling accuracy has been compared with ANFIS, support vector regression (SVR) and artificial neural network (ANN). Intelligence predictions of fragmentation by ANFIS-FFA, ANFIS-GA, ANFIS, SVR and ANN are compared with observed values of fragmentation available in 88 blasting event of two quarry mines, Iran. According to the results, both ANFIS-FFA and ANFIS-GA prediction models performed satisfactorily; however, the lowest root mean square error (RMSE) and the highest correlation of determination (R²) values were obtained from ANFIS-GA model. The values of R² and RMSE obtained from ANFIS-GA, ANFIS-FFA, ANFIS, SVR and ANN models were equal to (0.989, 0.974), (0.981, 1.249), (0.956, 1.591), (0.924, 2.016) and (0.948, 2.554), respectively. Consequently, the proposed ANFIS-GA model has the potential to be used for predicting aims on other fields.

     

Precipitation forecasting by using wavelet-support vector machine conjunction model

A new wavelet-support vector machine conjunction model for daily precipitation forecast is proposed in this study. The conjunction method combining two methods, discrete wavelet transform and support vector machine, is compared with the single support vector machine for one-day-ahead precipitation forecasting. Daily precipitation data from Izmir and Afyon stations in Turkey are used in the study. The root mean square errors (RMSE), mean absolute errors (MAE), and correlation coefficient (R) statistics are used for the comparing criteria. The comparison results indicate that the conjunction method could increase the forecast accuracy and perform better than the single support vector machine. For the Izmir and Afyon stations, it is found that the conjunction models with RMSE=46.5 mm, MAE=13.6 mm, R=0.782 and RMSE=21.4 mm, MAE=9.0 mm, R=0.815 in test period is superior in forecasting daily precipitations than the best accurate support vector regression models with RMSE=71.6 mm, MAE=19.6 mm, R=0.276 and RMSE=38.7 mm, MAE=14.2 mm, R=0.103, respectively. The ANN method was also employed for the same data set and found that there is a slight difference between ANN and SVR methods.     

Customer churn prediction by hybrid neural networks

As churn management is a major task for companies to retain valuable customers, the ability to predict customer churn is necessary. In literature, neural networks have shown their applicability to churn prediction. On the other hand, hybrid data mining techniques by combining two or more techniques have been proved to provide better performances than many single techniques over a number of different domain problems. This paper considers two hybrid models by combining two different neural network techniques for churn prediction, which are back-propagation artificial neural networks (ANN) and self-organizing maps (SOM). The hybrid models are ANN combined with ANN (ANN + ANN) and SOM combined with ANN (SOM + ANN). In particular, the first technique of the two hybrid models performs the data reduction task by filtering out unrepresentative training data. Then, the outputs as representative data are used to create the prediction model based on the second technique. To evaluate the performance of these models, three different kinds of testing sets are considered. They are the general testing set and two fuzzy testing sets based on the filtered out data by the first technique of the two hybrid models, i.e. ANN and SOM, respectively. The experimental results show that the two hybrid models outperform the single neural network baseline model in terms of prediction accuracy and Types I and II errors over the three kinds of testing sets. In addition, the ANN + ANN hybrid model significantly performs better than the SOM + ANN hybrid model and the ANN baseline model.     

Remote estimation of regional evapotranspiration

Three models are applied to estimating evapotranspiration in central Australia, using limited routine meteorological data and the NOAA-14 AVHRR overpass. By minimizing the difference between model predicted surface temperature and satellite derived temperature to adjust the estimated soil moisture, both an instantaneous physically based model and a one dimensional boundary layer simulation yielded consistent results. This highlights the sensitivity of surface temperature to soil moisture and suggests that radiometric surface temperature can be used to adjust simple water balance estimates of soil moisture providing a simple and effective means of estimating large scale evapotranspiration in remote arid regions.     

A novel approach for estimating heat transfer coefficients of ethylene glycol–water mixtures

Ethylene glycol–water mixtures (EGWM) are vital for cooling engines in automotive industry. Scarce information is available in the literature for estimating the heat transfer coefficients (HTC) of EGWM using knowledge-based estimation techniques such as adaptive neuro-fuzzy inference systems (ANFIS) and artificial neural networks (ANN) which offer nonlinear input–output mapping. In this paper, the supervised learning methods of ANFIS and ANN are exploited for estimating the experimentally determined HTC. This original research fulfills the preceding modeling efforts on thermal properties of EGWM and HTC applications in the literature. An experimental test setup is designed to compute HTC of mixture over a small circular aluminum heater surface, 9.5 mm in diameter, placed at the bottom 40-mm-wide wall of a rectangular channel 3 mm × 40 mm in cross section. Measurement data are utilized as the train and test data sets of the estimation process. Prediction results have shown that ANFIS provide more accurate and reliable approximations compared to ANN. ANFIS present correlation factor of 98.81 %, whereas ANN estimate 87.83 % accuracy for test samples.     

Modeling of daily pan evaporation in sub tropical climates using ANN,LS-SVR,Fuzzy Logic,and ANFIS

This paper investigates the abilities of Artificial Neural Networks (ANN), Least Squares – Support Vector Regression (LS-SVR), Fuzzy Logic, and Adaptive Neuro-Fuzzy Inference System (ANFIS) techniques to improve the accuracy of daily pan evaporation estimation in sub-tropical climates. Meteorological data from the Karso watershed in India (consisting of 3801 daily records from the year 2000 to 2010) were used to develop and test the models for daily pan evaporation estimation. The measured meteorological variables include daily observations of rainfall, minimum and maximum air temperatures, minimum and maximum humidity, and sunshine hours. Prior to model development, the Gamma Test (GT) was used to derive estimates of the noise variance for each input–output set in order to identify the most useful predictors for use in the machine learning approaches used in this study. The ANN models consisted of feed forward backpropagation (FFBP) models with Bayesian Regularization (BR), along with the Levenberg–Marquardt (LM) algorithm. A comparison was made between the estimates provided by the ANN, LS-SVR, Fuzzy Logic, and ANFIS models. The empirical Hargreaves and Samani method (HGS), as well as the Stephens–Stewart (SS) method, were also considered for comparison with the newer machine learning methods. The Root Mean Square Error (RMSE) and Correlation Coefficient (CORR) were the statistical performance indices that were used to evaluate the accuracy of the various models. Based on the comparison, it was found that the Fuzzy Logic and LS-SVR approaches can be employed successfully in modeling the daily evaporation process from the available climatic data. In addition, results showed that the machine learning models outperform the traditional HGS and SS empirical methods.     

Development of a hybrid PSO–ANN model for estimating glucose and xylose yields for microwave-assisted pretreatment and the enzymatic hydrolysis of lignocellulosic biomass

In this paper, two artificial intelligent systems, the artificial neural network (ANN) and particle swarm optimization (PSO), were combined to form a hybrid PSO–ANN model that was used to improve estimates of glucose and xylose yields from the microwave–acid pretreatment and enzymatic hydrolysis of lignocellulosic biomass based on pretreatment parameters. ANN is a powerful tool capable of determining the relationship between the desired input and output data while PSO was used as a robust population-based search algorithm to optimize the performance of the ANN model. Specifically, it was used to determine the optimum number of neurons in the hidden layer and the best value of the learning rate of the ANN model. The optimization method includes minimizing the fitness function mean absolute error that was found to be 0.0176. The PSO algorithm suggested an optimum number of neurons in the hidden layer as 15 and a learning rate of 0.761 these consequently used to construct the ANN model. After constructing the hybrid PSO–ANN model, the performance of the intelligent system was examined by determining the regression coefficient (R ²) for estimating the experimental values of glucose and xylose and compared to the results from a response surface methodology (RSM) model. The results of R ² of the hybrid PSO–ANN model for glucose and xylose were 0.9939 and 0.9479, respectively, while the RSM model results for the same sugars were 0.8901 and 0.8439. This analysis reveals that the hybrid PSO–ANN model offers a higher degree of accuracy in comparison with the more commonly used RSM model.

     

Design of decision model for sensitive crop irrigation system

Agriculture Industry is highly dependent on environmental and weather conditions. Many times, crops are spoiled because of sudden changes in weather. Therefore, we need a decision model to take care the water requirement of sensitive crops of agriculture industry. The proposed work presents a novel and proficient hybrid model for sensitive crop irrigation system (SCIS). For implementation of the model, brassica crop is taken. The duration and amount of water to be supplied is based upon the weather prediction and soil condition information. The decision model is developed using adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) for brassica crops. In this model, if the input data values are available in range, then ANFIS model would be preferred and if the data sets are available for training, testing and validation then ANN model would be the best choice. The soil moisture, soil status in terms of temperature and leaf wetness are the input and flow control of sprinklers is the out for SCIS. The predicted outputs are analysed to assert the suitability of the proposed approach in the brassica crops. The proposed SCIS achieved an accuracy of 91% and 99% for ANFIS and ANN models respectively.     

Generalized neural network and wavelet transform based approach for fault location estimation of a transmission line

To maintain the efficient and reliable operation of power systems, it is extremely important that the transmission line faults need to be detected and located in a reliable and accurate manner. A number of mathematical and intelligent techniques are available in the literature for estimating the fault location. However, the results are not satisfactory due to the wide variation in operating conditions such as system loading level, fault inception instance, fault resistance and dc offset and harmonics contents in the transient signal of the faulted transmission line. Keeping in view of aforesaid, a new approach based on generalized neural network (GNN) with wavelet transform is presented for fault location estimation. Wavelet transform is used to extract the features of faulty current signals in terms of standard deviation. Obtained features are used as an input to the GNN model for estimating the location of fault in a given transmission systems. Results obtained from GNN model are compared with ANN and well established mathematical models and found more accurate.     

ANN- and ANFIS-based multi-staged decision algorithm for the detection and diagnosis of bearing faults

Condition monitoring of roller element bearing defects has been one of the biggest problems in predictive maintenance since bearing failures may give catastrophic results on the machining operations and may cause downtime. Two of the well-established and widely used methods for bearing fault detection and diagnosis are the artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFIS). For this aim, a multi-staged decision algorithm was developed in this study based on ANN and ANFIS models. Both time and frequency domain parameters extracted from the vibration and current signals were used to train the ANN and ANFIS models, which were then used to detect and diagnose the severity of the bearing fault. Experimental data collected from a shaft-bearing mechanism were used to test the performances of the two schemes. The system was operated under four different speeds, for a brand-new bearing and bearings with artificially introduced local defects with various sizes. The experimental results showed that the proposed scheme is an effective means for detecting and diagnosing bearing faults. Furthermore, the results revealed that ANFIS-based scheme was superior to the ANN-based one especially in diagnosing fault severity.

     

Modelling of evaporation from the reservoir of Yuvacik dam using adaptive neuro-fuzzy inference systems

Adaptive neuro-fuzzy inference system (ANFIS) models are proposed as an alternative approach of evaporation estimation for Yuvacik Dam. This study has three objectives: (1) to develop ANFIS models to estimate daily pan evaporation from measured meteorological data; (2) to compare the ANFIS model to the multiple linear regression (MLR) model; and (3) to evaluate the potential of ANFIS model. Various combinations of daily meteorological data, namely air temperature, relative humidity, solar radiation and wind speed, are used as inputs to the ANFIS so as to evaluate the degree of effect of each of these variables on daily pan evaporation. The results of the ANFIS model are compared with MLR model. Mean square error, average absolute relative error and coefficient of determination statistics are used as comparison criteria for the evaluation of the model performances. The ANFIS technique whose inputs are solar radiation, air temperature, relative humidity and wind speed, gives mean square errors of 0.181 mm, average absolute relative errors of 9.590% mm, and determination coefficient of 0.958 for Yuvacik Dam station, respectively. Based on the comparisons, it was found that the ANFIS technique could be employed successfully in modelling evaporation process from the available climatic data.     

Fault gear identification and classification using discrete wavelet transform and adaptive neuro-fuzzy inference

In this paper, an intelligent diagnosis for fault gear identification and classification based on vibration signal using discrete wavelet transform and adaptive neuro-fuzzy inference system (ANFIS) is presented. The discrete wavelet transform (DWT) technique plays one of the important roles for signal feature extraction in the proposed system. The abnormal transient signals will show in different decomposition levels and can be used to recognize the various faults by the DWT figure. However, many fault conditions are hard to inspect accurately by the naked eye. In the present study, the feature extraction method based on discrete wavelet transform with energy spectrum is proposed. The different order wavelets are considered to identify fault features accurately. The database is established by feature vectors of energy spectrum which are used as input pattern in the training and identification process. Furthermore, the ANFIS is proposed to identify and classify the fault gear positions and the gear fault conditions in the fault diagnosis system. The proposed ANFIS includes both the fuzzy logic qualitative approximation and the adaptive neural network capability. The experimental results verified that the proposed ANFIS has more possibilities in fault gear identification. The ANFIS achieved an accuracy identification rate which was more satisfactory than traditional vision inspection in the proposed system.     

Prediction of local scour around bridge piers using the ANFIS method

Local scour around bridge piers is a complicated physical process and involves highly three-dimensional flows. Thus, the scour depth, which is directly related to the safety of a bridge, cannot be given in the form of the exact relationship of dependent variables via an analytical method. This paper proposes the use of the adaptive neuro-fuzzy inference system (ANFIS) method for predicting the scour depth around a bridge pier. Five variables including mean velocity, flow depth, size of sediment particles, critical velocity for particles’ initiation of motion, and pier width were used for the scour depth. For comparison, predictions by the artificial neural network (ANN) model were also provided. Both the ANN model and ANFIS method were trained and validated. The findings indicate that the modeling with dimensional variables yields better predictions than when normalized variables are used. The ANN model was applied to a field-scale dataset. Prediction results indicated that the errors are much larger compared to the case of a laboratory-scale dataset. The MAPE by the ANN model trained with part of the field data was not seriously different from that by the model trained with the laboratory data. However, the application of the ANFIS method improved the predictions significantly, reducing the MAPE to the half of that by the ANN model. Five selected empirical formulas were also applied to the same dataset, and Sheppard and Melville’s formula was found to provide the best prediction. However, the MAPEs for the scour depths predicted by empirical formulas are much larger than MAPEs by either the ANN or the ANFIS method. The ANFIS method predicts much better if the range of the training dataset is sufficiently wide to cover the range of the application dataset.

     

Artificial neural network–genetic algorithm for estimation of crop evapotranspiration in a semi-arid region of Iran

This study compares the daily potato crop evapotranspiration (ET_C) estimated by artificial neural network (ANN), neural network–genetic algorithm (NNGA) and multivariate nonlinear regression (MNLR) methods. Using a 6-year (2000–2005) daily meteorological data recorded at Tabriz synoptic station and the Penman–Monteith FAO 56 standard approach (PMF-56), the daily ET_C was determined during the growing season (April–September). Air temperature, wind speed at 2 m height, net solar radiation, air pressure, relative humidity and crop coefficient for every day of the growing season were selected as the input of ANN models. In this study, the genetic algorithm was applied for optimization of the parameters used in ANN approach. It was found that the optimization of the ANN parameters did not improve the performance of ANN method. The results indicated that MNLR, ANN and NNGA methods were able to predict potato ET_C at desirable level of accuracy. However, the MNLR method with highest coefficient of determination (R ² > 0.96, P value < 0.05) and minimum errors provided superior performance among the other methods.     

Performance Evaluation of an Adaptive-Network-Based Fuzzy Inference System Approach for Location of Faults on Transmission Lines Using Monte Carlo Simulation

This paper employs a wavelet multiresolution analysis (MRA) along with the adaptive-network-based fuzzy inference system to overcome the difficulties associated with conventional voltage- and current-based measurements for transmission-line fault location algorithms, due to the effect of factors such as fault inception angle, fault impedance, and fault distance. This proposed approach is different from conventional algorithms that are based on deterministic computations on a well-defined model to be protected, employing wavelet transform together with intelligent computational techniques, such as the fuzzy inference system (FIS), adaptive neurofuzzy inference system (ANFIS), and artificial neural network (ANN) in order to incorporate expert evaluation so as to extract important features from wavelet MRA coefficients for obtaining coherent conclusions regarding fault location. A comparative study establishes that the ANFIS approach has superiority over ANN- and FIS-based approaches for the location of line faults. In addition, the efficacy of the ANFIS is validated through the Monte Carlo simulation for incorporating the stochastic nature of fault occurrence in practical systems. Thus, this ANFIS-based digital relay can be used as an effective tool for real-time digital relaying purposes.      

Evaluation of peak and residual conditions of actively confined concrete using neuro-fuzzy and neural computing techniques

This paper investigates the ability of four artificial intelligence techniques, including artificial neural network (ANN), radial basis neural network (RBNN), adaptive neuro-fuzzy inference system (ANFIS) with grid partitioning, and ANFIS with fuzzy c-means clustering, to predict the peak and residual conditions of actively confined concrete. A large experimental test database that consists of 377 axial compression test results of actively confined concrete specimens was assembled from the published literature, and it was used to train, test, and validate the four models proposed in this paper using the mentioned artificial intelligence techniques. The results show that all of the neural network and ANFIS models fit well with the experimental results, and they outperform the conventional models. Among the artificial intelligence models investigated, RBNN model is found to be the most accurate to predict the peak and residual conditions of actively confined concrete. The predictions of each proposed model are subsequently used to study the interdependence of critical parameters and their influence on the behavior of actively confined concrete.

     

Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting

Desired rock fragmentation is the main goal of the blasting operation in surface mines, civil and tunneling works. Therefore, precise prediction of rock fragmentation is very important to achieve an economically successful outcome. The primary objective of this article is to propose a new model for forecasting the rock fragmentation using adaptive neuro-fuzzy inference system (ANFIS) in combination with particle swarm optimization (PSO). The proposed PSO–ANFIS model has been compared with support vector machines (SVM), ANFIS and nonlinear multiple regression (MR) models. To construct the predictive models, 72 blasting events were investigated, and the values of rock fragmentation as well as five effective parameters on rock fragmentation, i.e., specific charge, stemming, spacing, burden and maximum charge used per delay were measured. Based on several statistical functions [e.g., coefficient of correlation (R ²) and root-mean-square error (RMSE)], it was found that the PSO–ANFIS (with R ² = 0.89 and RMSE = 1.31) performs better than the SVM (with R ² = 0.83 and RMSE = 1.66), ANFIS (with R ² = 0.81 and RMSE = 1.78) and nonlinear MR (with R ² = 0.57 and RMSE = 3.93) models. Finally, the sensitivity analysis shows that the burden and maximum charge used per delay have the least and the most effects on the rock fragmentation, respectively.