Modeling of Wind Speed Profile using Soft Computing Techniques

2015 ◽  
pp. 252-273
Author(s):  
Pijush Samui ◽  
Yıldırım Dalkiliç
Keyword(s):  
Wind Speed ◽  
Soft Computing ◽  
Kernel Functions ◽  
Training Dataset ◽  
Support Vector ◽  
Wind Speed Profile ◽  
Testing Dataset ◽  
Soft Computing Techniques ◽  
Adaptive Regression ◽  
Regression Techniques

This chapter examines the capability of three soft computing techniques (Genetic Programming [GP], Support Vector Machine [SVM], and Multivariate Adaptive Regression Spline [MARS]) for prediction of wind speed in Nigeria. Latitude, longitude, altitude, and the month of the year have been used as inputs of GP, RVM, and MARS models. The output of GP, SVM, and MARS is wind speed. GP, SVM, and MARS have been used as regression techniques. To develop GP, MARS, and SVM, the datasets have been divided into the following two groups: 1) Training Dataset – this is required to develop GP, MPMR, and RVM models. This study uses 18 stations' data as a training dataset. 2) Testing Dataset – this is required to verify the developed GP, MPMR, and RVM models. The remaining 10 stations data have been used as testing dataset. Radial basis function has been used as kernel functions for SVM. A detailed comparative study between the developed GP, SVM, and MARS models is performed in this chapter.

Estimation of discharge correction factor of modified Parshall flume using ANFIS and ANN

2020 ◽  
Vol 1 (105) ◽  
pp. 17-30
Author(s):  
D. Saran ◽  
N.K. Tiwari
Keyword(s):  
Sensitivity Analysis ◽  
Soft Computing ◽  
Correction Factor ◽  
Prediction Models ◽  
Training Dataset ◽  
Inference System ◽  
Testing Dataset ◽  
Soft Computing Techniques ◽  
Regression Techniques ◽  
Prediction Capability

Purpose: To evaluate and compare the capability of ANFIS (Adaptive Neuro-Fuzzy-Inference System), ANN (Artificial Neural Network), and MNLR (Multiple Non-linear Regression) techniques in the estimation and formulation of Discharge Correction Factor (Cd) of modified Parshall flumes as based on linear relations and errors between input and output data. Design/methodology/approach: Acknowledging the necessity of further research in this field, experiments were conducted in the Hydraulics Laboratory of Civil Engineering Department, National Institute of Technology, Kurukshetra, India. The Parshall flume characteristics, associated longitudinal slopes and the discharge passing through the flume were varied. Consequent water depths at specific points in Parshall flumes were noted and the values of Cd were computed. In this manner, a data set of 128 observations was acquired. This was bifurcated arbitrarily into a training dataset consisting of 88 observations and a testing dataset consisting of 40 observations. Models developed using the training dataset were checked on the testing dataset for comparison of the performance of each predictive model. Further, an empirical relationship was formulated establishing Cd as a function of flume characteristics, longitudinal slope, and water depth at specific points using the MNLR technique. Moreover, Cd was estimated using soft computing tools; ANFIS and ANN. Finally, a sensitivity analysis was done to find out the flume variable having the greatest influence on the estimation of Cd. Findings: The predictive accuracy of the ANN-based model was found to be better than the model developed using ANFIS, followed by the model developed using the MNLR technique. Further, sensitivity analysis results indicated that primary depth reading (Ha) as input parameter has the greatest influence on the prediction capability of the developed model. Research limitations/implications: Since the soft computing models are data based learning, hence the prediction capability of these models may dwindle if data is selected beyond the current data range, which is based on the experiments conducted under specific conditions. Further, since the present study has faced time and facility constraints, hence there is still a huge scope of research in this field. Different lateral slopes, combined laterallongitudinal slopes, and more modified Parshall flume models of larger sizes can be added to increase the versatility of the current research. Practical implications: Cd of modified Parshall flumes can be predicted using the ANNbased prediction model more accurately as compared to other considered techniques. Originality/value: The comparative analysis of prediction models, as well as the formulation of relation, has been conducted in this study. In all the previous works, little to no soft computing techniques have been applied for the analysis of Parshall flumes. Even the regression techniques have been applied only on Parshall flumes of standard sizes. However, this paper includes not only Parshall flume of standard size but also a modified Parshall flume in its pursuit of predicting Cd with the help of ANN and ANFIS based prediction models along with MNLR technique

scholarly journals Systematic Framework to Predict Early-Stage Liver Carcinoma Using Hybrid of Feature Selection Techniques and Regression Techniques

Complexity ◽  
2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Marium Mehmood ◽  
Nasser Alshammari ◽  
Saad Awadh Alanazi ◽  
Fahad Ahmad
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Liver Diseases ◽  
Early Stage ◽  
Support Vector ◽  
Liver Carcinoma ◽  
Random Forest Regression ◽  
Soft Computing Techniques ◽  
Regression Algorithms ◽  
Regression Techniques

The liver is the human body’s mandatory organ, but detecting liver disease at an early stage is very difficult due to the hiddenness of symptoms. Liver diseases may cause loss of energy or weakness when some irregularities in the working of the liver get visible. Cancer is one of the most common diseases of the liver and also the most fatal of all. Uncontrolled growth of harmful cells is developed inside the liver. If diagnosed late, it may cause death. Treatment of liver diseases at an early stage is, therefore, an important issue as is designing a model to diagnose early disease. Firstly, an appropriate feature should be identified which plays a more significant part in the detection of liver cancer at an early stage. Therefore, it is essential to extract some essential features from thousands of unwanted features. So, these features will be mined using data mining and soft computing techniques. These techniques give optimized results that will be helpful in disease diagnosis at an early stage. In these techniques, we use feature selection methods to reduce the dataset’s feature, which include Filter, Wrapper, and Embedded methods. Different Regression algorithms are then applied to these methods individually to evaluate the result. Regression algorithms include Linear Regression, Ridge Regression, LASSO Regression, Support Vector Regression, Decision Tree Regression, Multilayer Perceptron Regression, and Random Forest Regression. Based on the accuracy and error rates generated by these Regression algorithms, we have evaluated our results. The result shows that Random Forest Regression with the Wrapper Method from all the deployed Regression techniques is the best and gives the highest R2-Score of 0.8923 and lowest MSE of 0.0618.

A Two-Layer Learning Architecture for Multi-Class Protein Folds Classification

2013 ◽  
pp. 786-797
Author(s):  
Ruofei Wang ◽  
Xieping Gao
Keyword(s):  
Query Protein ◽  
Training Dataset ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Protein Folds ◽  
Testing Dataset ◽  
Ensemble Strategy ◽  
Component Classifier ◽  
Independent Testing Dataset

Classification of protein folds plays a very important role in the protein structure discovery process, especially when traditional sequence alignment methods fail to yield convincing structural homologies. In this chapter, we have developed a two-layer learning architecture, named TLLA, for multi-class protein folds classification. In the first layer, OET-KNN (Optimized Evidence-Theoretic K Nearest Neighbors) is used as the component classifier to find the most probable K-folds of the query protein. In the second layer, we use support vector machine (SVM) to build the multi-class classifier just on the K-folds, generated in the first layer, rather than on all the 27 folds. For multi-feature combination, ensemble strategy based on voting is selected to give the final classification result. The standard percentage accuracy of our method at ~63% is achieved on the independent testing dataset, where most of the proteins have <25% sequence identity with those in the training dataset. The experimental evaluation based on a widely used benchmark dataset has shown that our approach outperforms the competing methods, implying our approach might become a useful vehicle in the literature.

Prediction of The Uniaxial Compressive Strength of Rocks Materials

2018 ◽  
pp. 31-96 ◽  
Author(s):  
Nurcihan Ceryan ◽  
Nuray Korkmaz Can
Keyword(s):  
Soft Computing ◽  
Computing Methods ◽  
P Wave ◽  
Support Vector ◽  
Advantages And Disadvantages ◽  
Artifical Neural Network ◽  
P Wave Velocity ◽  
Soft Computing Techniques ◽  
Soft Computing Methods ◽  
Direct And Indirect Methods

This study briefly will review determining UCS including direct and indirect methods including regression model soft computing techniques such as fuzzy interface system (FIS), artifical neural network (ANN) and least sqeares support vector machine (LS-SVM). These has advantages and disadvantages of these methods were discussed in term predicting UCS of rock material. In addition, the applicability and capability of non-linear regression, FIS, ANN and LS-SVM SVM models for predicting the UCS of the magnatic rocks from east Pondite, NE Turkey were examined. In these soft computing methods, porosity and P-durability secon index defined based on P-wave velocity and slake durability were used as input parameters. According to results of the study, the performanc of LS-SVM models is the best among these soft computing methods suggested in this study.

scholarly journals A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer

2020 ◽  
Vol 10 (2) ◽  
pp. 635 ◽  
Author(s):  
Yingli LV ◽  
Qui-Thao Le ◽  
Hoang-Bac Bui ◽  
Xuan-Nam Bui ◽  
Hoang Nguyen ◽  
...  
Keyword(s):  
Soft Computing ◽  
Mean Squared Error ◽  
Machine Learning Algorithms ◽  
Classification And Regression Tree ◽  
Gradient Boosting ◽  
Support Vector ◽  
Stochastic Gradient Boosting ◽  
Soft Computing Techniques ◽  
Residuals Analysis ◽  
Computing Models

In this study, the ilmenite content in beach placer sand was estimated using seven soft computing techniques, namely random forest (RF), artificial neural network (ANN), k-nearest neighbors (kNN), cubist, support vector machine (SVM), stochastic gradient boosting (SGB), and classification and regression tree (CART). The 405 beach placer borehole samples were collected from Southern Suoi Nhum deposit, Binh Thuan province, Vietnam, to test the feasibility of these soft computing techniques in estimating ilmenite content. Heavy mineral analysis indicated that valuable minerals in the placer sand are zircon, ilmenite, leucoxene, rutile, anatase, and monazite. In this study, five materials, namely rutile, anatase, leucoxene, zircon, and monazite, were used as the input variables to estimate ilmenite content based on the above mentioned soft computing models. Of the whole dataset, 325 samples were used to build the regarded soft computing models; 80 remaining samples were used for the models’ verification. Root-mean-squared error (RMSE), determination coefficient (R2), a simple ranking method, and residuals analysis technique were used as the statistical criteria for assessing the model performances. The numerical experiments revealed that soft computing techniques are capable of estimating the content of ilmenite with high accuracy. The residuals analysis also indicated that the SGB model was the most suitable for determining the ilmenite content in the context of this research.

scholarly journals Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction

2017 ◽  
Vol 32 (1) ◽  
pp. 103-112 ◽  
Author(s):  
Basant Yadav ◽  
Sudheer Ch ◽  
Shashi Mathur ◽  
Jan Adamowski
Keyword(s):  
Soft Computing ◽  
Groundwater Level ◽  
Groundwater Resources ◽  
Support Vector ◽  
Complex Nature ◽  
Data Set ◽  
Groundwater Levels ◽  
Soft Computing Techniques ◽  
Learning Machine ◽  
The Comparative Study

Abstract Fluctuation of groundwater levels around the world is an important theme in hydrological research. Rising water demand, faulty irrigation practices, mismanagement of soil and uncontrolled exploitation of aquifers are some of the reasons why groundwater levels are fluctuating. In order to effectively manage groundwater resources, it is important to have accurate readings and forecasts of groundwater levels. Due to the uncertain and complex nature of groundwater systems, the development of soft computing techniques (data-driven models) in the field of hydrology has significant potential. This study employs two soft computing techniques, namely, extreme learning machine (ELM) and support vector machine (SVM) to forecast groundwater levels at two observation wells located in Canada. A monthly data set of eight years from 2006 to 2014 consisting of both hydrological and meteorological parameters (rainfall, temperature, evapotranspiration and groundwater level) was used for the comparative study of the models. These variables were used in various combinations for univariate and multivariate analysis of the models. The study demonstrates that the proposed ELM model has better forecasting ability compared to the SVM model for monthly groundwater level forecasting.

scholarly journals A Comparison of Three Soft Computing Techniques, Bayesian Regression, Support Vector Regression, and Wavelet Regression, for Monthly Rainfall Forecast

2017 ◽  
Vol 26 (4) ◽  
pp. 641-655 ◽  
Author(s):  
Ashutosh Sharma ◽  
Manish Kumar Goyal
Keyword(s):  
Support Vector Regression ◽  
Soft Computing ◽  
Bayesian Regression ◽  
Monthly Rainfall ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Efficiency Coefficient ◽  
Wavelet Regression ◽  
Rainfall Forecast ◽  
Soft Computing Techniques

AbstractRainfall, being one of the most important components of the hydrological cycle, plays an extremely important role in agriculture-based economies like India. This paper presents a comparison between three soft computing techniques, namely Bayesian regression (BR), support vector regression (SVR), and wavelet regression (WR), for monthly rainfall forecast in Assam, India. A WR model is a combination of discrete wavelet transform and linear regression. Monthly rainfall data for 102 years from 1901 to 2002 at 21 stations were used for this study. The performances of different models were evaluated based on the mean absolute error, root mean square error, correlation coefficient, and Nash-Sutcliffe efficiency coefficient. Based on model statistics, WR was found to be the most accurate followed by SVR and BR. The efficiencies for the BR, SVR, and WR models were found to be 32.8%, 52.9%, and 64.03%, respectively. From the spatial analysis of model performances, it was found that the models performed best for the upper Assam region followed by lower, southern, and middle regions, respectively.

scholarly journals Comparison of six different soft computing methods in modeling evaporation in different climates

2016 ◽  
Author(s):  
Lunche Wang ◽  
Ozgur Kisi ◽  
Mohammad Zounemat-Kermani ◽  
Yiqun Gan
Keyword(s):  
Soft Computing ◽  
Computing Methods ◽  
Least Square ◽  
Support Vector ◽  
Climatic Data ◽  
Regression Methods ◽  
Soft Computing Techniques ◽  
Soft Computing Methods ◽  
Fuzzy Genetic ◽  
Different Climates

Abstract. Evaporation plays important roles in regional water resources management,terrestrial ecological process and regional climate change. This study investigated the abilities of six different soft computing methods, Multi-layer perceptron (MLP), generalized regression neural network (GRNN), fuzzy genetic (FG), least square support vector machine (LSSVM), multivariate adaptive regression spline (MARS), adaptive neuro-fuzzy inference systems with grid partition (ANFIS-GP), and two regression methods, multiple linear regression (MLR) and Stephens and Stewart model (SS) in predicting monthly Ep. Long-term climatic data at eight stations in different climates, air temperature (Ta), solar radiation (Rg), sunshine hours (Hs), relative humidity (RH) and wind speed (Ws) during 1961–2000 are used for model development and validation. The first part of applications focused on testing and comparing the model accuracies using different local input combinations. The results showed that the models have different accuracies in different climates and the MLP model performed superior to the other models in predicting monthly Ep at most stations, while GRNN model performed better in Tibetan Plateau. The accuracies of above models ranked as: MLP, GRNN, LSSVM, FG, ANFIS-GP, MARS and MLR. Generalized models were also developed and tested with data of eight stations. The overall results indicated that the soft computing techniques generally performed better than the regression methods, but MLR and SS models can be more preferred at some climatic zones instead of complex nonlinear models, for example, the BJ, CQ and HK stations.

scholarly journals Laboratory Testing Implications of Risk-Stratification and Management of COVID-19 Patients

2021 ◽  
Author(s):  
Caidong Liu ◽  
Ziyu Wang ◽  
Wei Wu ◽  
Changgang Xiang ◽  
Lingxiang Wu ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Stratification ◽  
Early Stage ◽  
Case Series ◽  
Low Risk ◽  
Training Dataset ◽  
Support Vector ◽  
Machine Model ◽  
Testing Dataset ◽  
Probability Of Death

Abstract Objectives: To classify COVID-19 patients into low-risk and high-risk at admission by laboratory indicators.Design, Setting, and Patients: This is a case series of patients from a China healthcare system in Wuhan. In this retrospective cohort, 3563 patients confirmed COVID-19 pneumonia, including 548 patients in the training dataset, and 3015 patients in the testing dataset.Interventions: NoneMeasurements and Main Results:We first identified the significant laboratory indicators related to the severity of COVID-19 in the training dataset. Neutrophils percentage, lymphocytes percentage, creatinine, and blood urea nitrogen with AUC greater than 0.7 were included in the model. These indicators were further used to build a support vector machine model to classify patients into low-risk and high-risk at admission. Results showed that this model could stratify the patients in the testing dataset effectively (AUC=0.89). Moreover, laboratory indicators detected in the first week after admission were able to estimate the probability of death (AUC=0.95). Besides, we could diagnose COVID-19 and differentiated it from other kinds of viral pneumonia based on laboratory indicators (accuracy=0.97).Conclusions:Our risk-stratification model based on laboratory indicators could help to diagnose, monitor, and predict severity at an early stage of COVID-19.

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