scholarly journals Application of MGGP, ANN, MHBMO, GRG, and Linear Regression for Developing Daily Sediment Rating Curves

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Majid Niazkar ◽  
Mohammad Zakwan
Keyword(s):  
Nonlinear Regression ◽  
Empirical Model ◽  
Absolute Error ◽  
Data Driven ◽  
Discharge Data ◽  
Nonlinear Regression Models ◽  
Sediment Loads ◽  
Multigene Genetic Programming ◽  
Rating Curves ◽  
Artificial Neural Network Ann

A data-driven relationship between sediment and discharge of a river is among the most erratic relationships in river engineering due to the existence of an inevitable scatter in sediment rating curves. Recently, Multigene Genetic Programming (MGGP), as a machine learning (ML) method, has been proposed to develop data-driven models for various phenomena in the field of hydrology and water resource engineering. The present study explores the capability of MGGP-based models to develop daily sediment ratings of two gauging sites with 30-year sediment-discharge data, which was utilized previously in the literature. The results obtained by MGGP were compared with those achieved by an empirical model and Artificial Neural Network (ANN). The coefficients of the empirical model were calibrated using linear and nonlinear regression models (Generalized Reduced Gradient (GRG) and the Modified Honey Bee Mating Optimization (MHBMO) algorithm). According to the comparative analysis, the mean absolute error (MAE) at the two gauging stations reduced from 516.54 to 519.23 obtained by nonlinear regression to 447.26 and 504.23 achieved by MGGP, respectively. Similarly, all other performance indices indicated the suitability and accuracy of MGGP in developing sediment ratings. Therefore, it was demonstrated that ML-based models, particularly MGGP-based models, outperformed the empirical models for estimating sediment loads.

An Effective Hybrid Semi-Parametric Regression Strategy for Rainfall Forecasting Combining Linear and Nonlinear Regression

2013 ◽  
pp. 273-289
Author(s):  
Jiansheng Wu
Keyword(s):  
Nonlinear Regression ◽  
Regression Models ◽  
Principal Component ◽  
Linear Regression Models ◽  
Rainfall Forecasting ◽  
Forecasting Accuracy ◽  
Parametric Regression ◽  
Nonlinear Regression Models ◽  
Linear And Nonlinear ◽  
Artificial Neural Network Ann

Rainfall forecasting is an important research topic in disaster prevention and reduction. The characteristic of rainfall involves a rather complex systematic dynamics under the influence of different meteorological factors, including linear and nonlinear pattern. Recently, many approaches to improve forecasting accuracy have been introduced. Artificial neural network (ANN), which performs a nonlinear mapping between inputs and outputs, has played a crucial role in forecasting rainfall data. In this paper, an effective hybrid semi-parametric regression ensemble (SRE) model is presented for rainfall forecasting. In this model, three linear regression models are used to capture rainfall linear characteristics and three nonlinear regression models based on ANN are able to capture rainfall nonlinear characteristics. The semi-parametric regression is used for ensemble model based on the principal component analysis technique. Empirical results reveal that the prediction using the SRE model is generally better than those obtained using other models in terms of the same evaluation measurements. The SRE model proposed in this paper can be used as a promising alternative forecasting tool for rainfall to achieve greater forecasting accuracy and improve prediction quality.

scholarly journals Using artificial neural network (ANN) for prediction of sediment loads, application to the Mellah catchment, northeast Algeria

2017 ◽  
Vol 33 (1) ◽  
pp. 47-55 ◽  
Author(s):  
Housseyn Bouzeria ◽  
Abderrahmane N. Ghenim ◽  
Kamel Khanchoul
Keyword(s):  
Suspended Sediment ◽  
Time Series Data ◽  
Water Discharge ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Sediment Discharge ◽  
Statistical Parameters ◽  
Discharge Data ◽  
Sediment Loads

AbstractIn this study, we present the performances of the best training algorithm in Multilayer Perceptron (MLP) neural networks for prediction of suspended sediment discharges in Mellah catchment. Time series data of daily suspended sediment discharge and water discharge from the gauging station of Bouchegouf were used for training and testing the networks. A number of statistical parameters, i.e. root mean square error (RMSE), mean absolute error (MAE), coefficient of efficiency (CE) and coefficient of determination (R2) were used for performance evaluation of the model. The model produced satisfactory results and showed a very good agreement between the predicted and observed data. The results also showed that the performance of the MLP model was capable to capture the exact pattern of the sediment discharge data in the Mellah catchment.

scholarly journals Assessment of Artificial Intelligence Models for Developing Single-Value and Loop Rating Curves

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Majid Niazkar ◽  
Mohammad Zakwan
Keyword(s):  
Artificial Intelligence ◽  
Conventional Method ◽  
Evaluation Criteria ◽  
Resource Planning ◽  
Optimization Techniques ◽  
Computational Techniques ◽  
Multigene Genetic Programming ◽  
Artificial Intelligence Models ◽  
Rating Curves ◽  
Artificial Neural Network Ann

Estimation of discharge flowing through rivers is an important aspect of water resource planning and management. The most common way to address this concern is to develop stage-discharge relationships at various river sections. Various computational techniques have been applied to develop discharge ratings and improve the accuracy of estimated discharges. In this regard, the present study explores the application of the novel hybrid multigene genetic programming-generalized reduced gradient (MGGP-GRG) technique for estimating river discharges for steady as well as unsteady flows. It also compares the MGGP-GRG performance with those of the commonly used optimization techniques. As a result, the rating curves of eight different rivers were developed using the conventional method, evolutionary algorithm (EA), the modified honey bee mating optimization (MHBMO) algorithm, artificial neural network (ANN), MGGP, and the hybrid MGGP-GRG technique. The comparison was conducted on the basis of several widely used performance evaluation criteria. It was observed that no model outperformed others for all datasets and metrics considered, which demonstrates that the best method may be different from one case to another one. Nevertheless, the ranking analysis indicates that the hybrid MGGP-GRG model overall performs the best in developing stage-discharge relationships for both single-value and loop rating curves. For instance, the hybrid MGGP-GRG technique improved sum of square of errors obtained by the conventional method between 4.5% and 99% for six out of eight datasets. Furthermore, EA, the MHBMO algorithm, and artificial intelligence (AI) models (ANN and MGGP) performed satisfactorily in some of the cases, while the idea of combining MGGP with GRG reveals that this hybrid method improved the performance of MGGP in this specific application. Unlike the black box nature of ANN, MGGP offers explicit equations for stream rating curves, which may be counted as one of the advantages of this AI model.

scholarly journals Attribute Selection Impact on Linear and Nonlinear Regression Models for Crop Yield Prediction

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Alberto Gonzalez-Sanchez ◽  
Juan Frausto-Solis ◽  
Waldo Ojeda-Bustamante
Keyword(s):  
Linear Regression ◽  
Real Data ◽  
Absolute Error ◽  
Correlation Factor ◽  
Attribute Selection ◽  
Data Driven ◽  
Yield Prediction ◽  
Nonlinear Regression Models ◽  
Modeling Techniques ◽  
Data Driven Modeling

Efficient cropping requires yield estimation for each involved crop, where data-driven models are commonly applied. In recent years, some data-driven modeling technique comparisons have been made, looking for the best model to yield prediction. However, attributes are usually selected based on expertise assessment or in dimensionality reduction algorithms. A fairer comparison should include the best subset of features for each regression technique; an evaluation including several crops is preferred. This paper evaluates the most common data-driven modeling techniques applied to yield prediction, using a complete method to define the best attribute subset for each model. Multiple linear regression, stepwise linear regression, M5′ regression trees, and artificial neural networks (ANN) were ranked. The models were built using real data of eight crops sowed in an irrigation module of Mexico. To validate the models, three accuracy metrics were used: the root relative square error (RRSE), relative mean absolute error (RMAE), and correlation factor (R). The results show that ANNs are more consistent in the best attribute subset composition between the learning and the training stages, obtaining the lowest average RRSE (86.04%), lowest average RMAE (8.75%), and the highest average correlation factor (0.63).

A comparative study of data-driven and physics-based gas turbine fault recognition approaches

2021 ◽  
pp. 1748006X2198964
Author(s):  
Juan Luis Pérez-Ruiz ◽  
Igor Loboda ◽  
Iván González-Castillo ◽  
Víctor Manuel Pineda-Molina ◽  
Karen Anaid Rendón-Cortés ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Gas Turbine ◽  
Recognition Accuracy ◽  
Data Driven ◽  
Fault Identification ◽  
Fault Recognition ◽  
Data Driven Approach ◽  
Diagnostic Approaches ◽  
Artificial Neural Network Ann ◽  
Two Stages

The present paper compares the fault recognition capabilities of two gas turbine diagnostic approaches: data-driven and physics-based (a.k.a. gas path analysis, GPA). The comparison takes into consideration two differences between the approaches, the type of diagnostic space and diagnostic decision rule. To that end, two stages are proposed. In the first one, a data-driven approach with an artificial neural network (ANN) that recognizes faults in the space of measurement deviations is compared with a hybrid GPA approach that employs the same type of ANN to recognize faults in the space of estimated fault parameter. Different case studies for both anomaly detection and fault identification are proposed to evaluate the diagnostic spaces. They are formed by varying the classification, type of diagnostic analysis, and deviation noise scheme. In the second stage, the original GPA is reconstructed replacing the ANN with a tolerance-based rule to make diagnostic decisions. Here, two aspects are under analysis: the comparison of GPA classification rules and whole approaches. The results reveal that for simple classifications both spaces are equally accurate for anomaly detection and fault identification. However, for complex scenarios, the data-driven approach provides on average slightly better results for fault identification. The use of a hybrid GPA with ANN for a full classification instead of an original GPA with tolerance-based rule causes an increase of 12.49% in recognition accuracy for fault identification and up to 54.39% for anomaly detection. As for the whole approach comparison, the application of a data-driven approach instead of the original GPA can lead to an improvement of 12.14% and 53.26% in recognition accuracy for fault identification and anomaly detection, respectively.

scholarly journals Empirical Model With Excellent Statistical Properties for Describing Temperature-Dependent Developmental Rates of Insects and Mites

2017 ◽  
Vol 110 (3) ◽  
pp. 302-309 ◽  
Author(s):  
David A. Ratkowsky ◽  
Gadi V. P. Reddy
Keyword(s):  
Nonlinear Regression ◽  
Empirical Model ◽  
Minimum Variance ◽  
Statistical Properties ◽  
Parameter Estimates ◽  
Regression Equations ◽  
Temperature Dependent ◽  
Temperature Range ◽  
Least Squares Estimators ◽  
Development Rates

Abstract Previous empirical models for describing the temperature-dependent development rates for insects include the Briére, Lactin, Beta, and Ratkowsky models. Another nonlinear regression model, not previously considered in population entomology, is the Lobry–Rosso–Flandrois model, the shape of which is very close to that of the Ratkowsky model in the suboptimal temperature range, but which has the added advantage that all four of its parameters have biological meaning. A consequence of this is that initial parameter estimates, needed for solving the nonlinear regression equations, are very easy to obtain. In addition, the model has excellent statistical properties, with the estimators of the parameters being “close-to-linear,” which means that the least squares estimators are close to being unbiased, normally distributed, minimum variance estimators. The model describes the pooled development rates very well throughout the entire biokinetic temperature range and deserves to become the empirical model of general use in this area.

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