scholarly journals The Superiority of Data-Driven Techniques for Estimation of Daily Pan Evaporation

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 701
Author(s):  
Manish Kumar ◽  
Anuradha Kumari ◽  
Deepak Kumar ◽  
Nadhir Al-Ansari ◽  
Rawshan Ali ◽  
...  
Keyword(s):  
Graphical Representation ◽  
Support Vector ◽  
Pan Evaporation ◽  
Testing Dataset ◽  
Sunshine Hours ◽  
Taylor Diagram ◽  
Data Points ◽  
Daily Pan Evaporation ◽  
Multi Linear Regression ◽  
Artificial Neural Network Ann

In the present study, estimating pan evaporation (Epan) was evaluated based on different input parameters: maximum and minimum temperatures, relative humidity, wind speed, and bright sunshine hours. The techniques used for estimating Epan were the artificial neural network (ANN), wavelet-based ANN (WANN), radial function-based support vector machine (SVM-RF), linear function-based SVM (SVM-LF), and multi-linear regression (MLR) models. The proposed models were trained and tested in three different scenarios (Scenario 1, Scenario 2, and Scenario 3) utilizing different percentages of data points. Scenario 1 includes 60%: 40%, Scenario 2 includes 70%: 30%, and Scenario 3 includes 80%: 20% accounting for the training and testing dataset, respectively. The various statistical tools such as Pearson’s correlation coefficient (PCC), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and Willmott Index (WI) were used to evaluate the performance of the models. The graphical representation, such as a line diagram, scatter plot, and the Taylor diagram, were also used to evaluate the proposed model’s performance. The model results showed that the SVM-RF model’s performance is superior to other proposed models in all three scenarios. The most accurate values of PCC, RMSE, NSE, and WI were found to be 0.607, 1.349, 0.183, and 0.749, respectively, for the SVM-RF model during Scenario 1 (60%: 40% training: testing) among all scenarios. This showed that with an increase in the sample set for training, the testing data would show a less accurate modeled result. Thus, the evolved models produce comparatively better outcomes and foster decision-making for water managers and planners.

scholarly journals On the Evaluation of Rhamnolipid Biosurfactant Adsorption Performance on Amberlite XAD-2 Using Machine Learning Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fengqin Chen ◽  
Jinbo Huang ◽  
Xianjun Wu ◽  
Xiaoli Wu ◽  
Arash Arabmarkadeh
Keyword(s):  
Least Squares ◽  
Machine Learning Techniques ◽  
Lower Percentage ◽  
Support Vector ◽  
Mean Deviation ◽  
Group Management ◽  
Learning Techniques ◽  
Data Points ◽  
Artificial Neural Network Ann ◽  
Rhamnolipid Biosurfactant

Biosurfactants are a series of organic compounds that are composed of two parts, hydrophobic and hydrophilic, and since they have properties such as less toxicity and biodegradation, they are widely used in the food industry. Important applications include healthy products, oil recycling, and biological refining. In this research, to calculate the curves of rhamnolipid adsorption compared to Amberlite XAD-2, the least-squares vector machine algorithm has been used. Then, the obtained model is formed by 204 adsorption data points. Various graphical and statistical approaches are applied to ensure the correctness of the model output. The findings of this study are compared with studies that have used artificial neural network (ANN) and data group management method (GMDH) models. The model used in this study has a lower percentage of absolute mean deviation than ANN and GMDH models, which is estimated to be 1.71%.The least-squares support vector machine (LSSVM) is very valuable for investigating the breakthrough curve of rhamnolipid, and it can also be used to help chemists working on biosurfactants. Moreover, our graphical interface program can assist everyone to determine easily the curves of rhamnolipid adsorption on Amberlite XAD-2.

scholarly journals Investigation on Online DGA Monitoring to Determine Transformer Health Index Using Machine Learning

2021 ◽  
Vol 2128 (1) ◽  
pp. 012024
Author(s):  
M Solehin Shamsudin ◽  
Fitri Yakub ◽  
M Ibrahim Shapiai ◽  
Azlan Mohmad ◽  
N Amirah Abd Hamid
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Gas Analysis ◽  
Support Vector ◽  
Health Index ◽  
Testing Dataset ◽  
Random Neural Network ◽  
Periodic Maintenance ◽  
Artificial Neural Network Ann ◽  
Commercial Device

Abstract The Dissolve Gas Analysis (DGA) to determine the ageing and degradation of the transformer is standard and routine periodic maintenance. In general, there are two DGA analysis methods which are conventional (lab-based) and online monitoring. DGA monitoring will be able to access to detect incipient fault and transformer failure. Several techniques are available to analyse, interpret and diagnose the DGA result, such as IEEE standard, IEC 60599 standard, Key Gas Method, and Duval methods. There are several Machine Learning (ML) techniques has been explored such as Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Neural Neighbours (KNN), Random Neural Network (RNN), and Fuzzy Logic for determining the transformer condition, including fault diagnostic and fault detection. However, there are unexplored studies to combine the commercial device to determine the Health Index (HI) of Transformer. In this study, an ML method with the available input feature from the commercial device to the network is trained to determine the HI. In general, the benchmark dataset from the existing work is employed to validate the proposed investigation. There are 730 datasets comprising five different classes; 1) Very Good, 2) Good, 3) Fair, 4) Poor, 5) Very Poor in determining the HI of a transformer. Conventional rule to partition the train and testing dataset with a 70:30 ratio is employed in this study. The maximum accuracy results and method for 1) M1 is 66.67% for ANN, 2) M2 is 68.49% for ANN, 3) M3 is 76.71% for KNN, 4) M5 is 76.26% for ANN, 5) M6 is 79.00% for ANN and 6) M7 is 86.30% for ANN. In conclusion, the multi-gas device will have a good accuracy performance and provide a good HI indicator to classify the condition of the transformer, which can be used for preventive maintenance.

scholarly journals Prediction of mean wave overtopping at simple sloped breakwaters using kernel-based methods

2021 ◽  
Author(s):  
Shabnam Hosseinzadeh ◽  
Amir Etemad-Shahidi ◽  
Ali Koosheh
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Gaussian Process Regression ◽  
Structural Features ◽  
Support Vector ◽  
Ann Model ◽  
Wave Overtopping ◽  
Taylor Diagram ◽  
The Mean ◽  
Artificial Neural Network Ann

Abstract The accurate prediction of the mean wave overtopping rate at breakwaters is vital to have a safe design. Hence, providing a robust tool as a preliminary estimator can be useful for practitioners. Recently, soft computing tools such as artificial neural network (ANN) have been developed as alternatives to traditional overtopping formulae. The goal of this paper is to assess the capabilities of two kernel-based methods namely Gaussian process regression (GPR) and support vector regression for the prediction of mean wave overtopping rate at sloped breakwaters. An extensive dataset taken from EurOtop (2018) database, including rubble mound structures with permeable core, straight slopes, without berm, and crown wall, was employed to develop the models. Different combinations of the important dimensionless parameters representing structural features and wave conditions were tested based on the sensitivity analysis for developing the models. The obtained results were compared with those of the ANN model and the existing empirical formulae. The modified Taylor diagram was used to compare the models graphically. The results showed the superiority of kernel-based models, especially the GPR model over the ANN model and empirical formulae. In addition, the optimal input combination was introduced based on accuracy and the number of input parameters criteria. Finally, the physical consistencies of developed models were investigated the results, of which demonstrated the reliability of kernel-based models in terms of delivering physics of overtopping phenomenon.

scholarly journals A Survey on Predicting Advanced Liver Fibrosis Using Different Machine Learning Algorithms

2020 ◽  
pp. 177-183
Author(s):  
Krishnendu K B ◽  
Deepa S S
Keyword(s):  
Machine Learning ◽  
Liver Fibrosis ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Swarm Optimization ◽  
Advanced Liver Fibrosis ◽  
Forest Genetic ◽  
Multi Linear Regression ◽  
Artificial Neural Network Ann

Machine learning (ML) is a subsection of AI. The goal of ML is to understand the structure of data and fit that data into models that can be used for prediction, classification etc. Although machine learning is an area within computer science, it differs from traditional computational approaches. In recent years, different machine learning algorithms are used for disease prediction. Algorithms like Decision Tree (DT), Support Vector Machine (SVM), Particle Swarm Optimization (PSO), Multi- Linear Regression, Random Forest, Genetic Algorithm (GA), Artificial Neural Network (ANN), Naive Bayes, etc. are used for classification. Using these algorithms liver fibrosis stages can be predicted. This paper discusses different machine learning algorithms for the prediction of liver fibrosis stage and the performance analysis of these algorithms in various studies.

Estimating daily pan evaporation from climatic data of the State of Illinois, USA using adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN)

2011 ◽  
Vol 42 (6) ◽  
pp. 491-502 ◽  
Author(s):  
J. Shiri ◽  
W. Dierickx ◽  
A. Pour-Ali Baba ◽  
S. Neamati ◽  
M. A. Ghorbani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Pan Evaporation ◽  
Climatic Parameters ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Daily Pan Evaporation ◽  
Artificial Neural Network Ann

Evaporation is a major component of the hydrological cycle. It is an important aspect of water resource engineering and management, and in estimating the water budget of irrigation schemes. The current work presents the application of adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) approaches for modeling daily pan evaporation using daily climatic parameters. The neuro-fuzzy and neural network models are trained and tested using the data of three weather stations from different geographical positions in the U.S. State of Illinois. Daily meteorological variables such as air temperature, solar radiation, wind speed, relative humidity, surface soil temperature and total rainfall for three years (August 2005 to September 2008) were used for training and testing the employed models. Statistic parameters such as the coefficient of determination (R2), the root mean squared error (RMSE), the variance accounted for (VAF), the adjusted coefficient of efficiency (E1) and the adjusted index of agreement (d1) are used to evaluate the performance of the applied techniques. The results obtained show the feasibility of the ANFIS and ANN evaporation modeling from the available climatic parameters, especially when limited climatic parameters are used.

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