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.

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 Probabilistic Wavelet–Support Vector Regression Model for Streamflow Forecasting with Rainfall and Climate Information Input*

2015 ◽  
Vol 16 (5) ◽  
pp. 2209-2229 ◽  
Author(s):  
Zhiyong Liu ◽  
Ping Zhou ◽  
Yinqin Zhang
Keyword(s):  
Regression Model ◽  
Support Vector Regression ◽  
Support Vector ◽  
Climate Indices ◽  
Lead Times ◽  
Discrete Wavelet ◽  
Streamflow Forecasting ◽  
Support Vector Regression Model ◽  
Inference System ◽  
Predictive Uncertainty

Abstract It is essential to explore reliable streamflow forecasting techniques for water resources management. In this study, a Bayesian wavelet–support vector regression model (BWS model) is developed for one- and multistep-ahead streamflow forecasting using local meteohydrological observations and climate indices including El Niño–Southern Oscillation (ENSO) and the Indian Ocean dipole (IOD) as potential predictors. To accomplish this, a two-step strategy is applied. In the first step, the discrete wavelet transform is coupled with a support vector regression model for streamflow prediction. The three key factors of mother wavelets, decomposition levels, and edge effects are considered in the wavelet decomposition phase when using the hybrid wavelet–support vector regression model (WS model). Different combinations of these factors form a variety of WS models with corresponding forecasts. The second step combines multiple candidate WS models with “good” performance via Bayesian model averaging. This integrates the predictive strengths of different candidate WS models, giving a realistic assessment of the predictive uncertainty. The new ensemble model is used to forecast daily and monthly streamflows at two sites in Dongjiang basin, southern China. The results show that the proposed BWS model consistently generates more reliable predictions for daily (lead times of 1–7 days) and monthly (lead times of 1–3 months) forecasts as compared with the best single-member WS models and the adaptive neuro-fuzzy inference system (ANFIS). Furthermore, the proposed BWS model provides detailed information about the predictive uncertainty.

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.

Deep Learning Based Sentiment Classification on User-Generated Big Data

2020 ◽  
Vol 13 (5) ◽  
pp. 1047-1056
Author(s):  
Akshi Kumar ◽  
Arunima Jaiswal
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Deep Learning ◽  
Soft Computing ◽  
Sentiment Classification ◽  
Convolution Neural Network ◽  
Support Vector ◽  
Computing Technique ◽  
Proposed Model ◽  
Soft Computing Techniques

Background: Sentiment analysis of big data such as Twitter primarily aids the organizations with the potential of surveying public opinions or emotions for the products and events associated with them. Objective: In this paper, we propose the application of a deep learning architecture namely the Convolution Neural Network. The proposed model is implemented on benchmark Twitter corpus (SemEval 2016 and SemEval 2017) and empirically analyzed with other baseline supervised soft computing techniques. The pragmatics of the work includes modelling the behavior of trained Convolution Neural Network on wellknown Twitter datasets for sentiment classification. The performance efficacy of the proposed model has been compared and contrasted with the existing soft computing techniques like Naïve Bayesian, Support Vector Machines, k-Nearest Neighbor, Multilayer Perceptron and Decision Tree using precision, accuracy, recall, and F-measure as key performance indicators. Methods: Majority of the studies emphasize on the utilization of feature mining using lexical or syntactic feature extraction that are often unequivocally articulated through words, emoticons and exclamation marks. Subsequently, CNN, a deep learning based soft computing technique is used to improve the sentiment classifier’s performance. Results: The empirical analysis validates that the proposed implementation of the CNN model outperforms the baseline supervised learning algorithms with an accuracy of around 87% to 88%. Conclusion: Statistical analysis validates that the proposed CNN model outperforms the existing techniques and thus can enhance the performance of sentiment classification viability and coherency.

Robust and sparse Dual Tree Complex Wavelet Transform-based Twin Support Vector regression for dense 5G InH  Communications

2021 ◽  
Author(s):  
anis charrada ◽  
Abdelaziz Samet
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Support Vector Regression ◽  
Path Loss ◽  
Series Data ◽  
Support Vector ◽  
Sensitivity Threshold ◽  
Discrete Wavelet ◽  
Twin Support Vector Regression ◽  
Receiver Sensitivity

Abstract A robust and sparse Twin Support Vector Regression based on Dual Tree Discrete Wavelet Transform algorithm is conceived in this paper and applied to 28, 38, 60 and 73-GHz LOS (Line-of-Sight) wireless multipath transmission system in 5G Indoor Hotspot (InH) settings (simple, semi-complex and complex conference rooms) under small receiver sensitivity threshold. The algorithm establishes a denoising process in the learning phase based on Dual Tree Discrete Wavelet Transform (DT-CWT) which is suitable for time-series data. Additionally, the Close-In (CI) free space reference distance path loss model is analyzed and the large-scale propagation and probability distribution functions are investigated by determining the PLE (Path Loss Exponent) and the standard deviation of Shadow Factor (SF) for each InH scenario under consideration. Performance are evaluated under twelve (12) configuration scenarios, according to three criteria: mobility (0/3mps), receiver sensitivity threshold (-80/-120 dBm) and type of the InH area (simple, semi-complex and complex conference room). Experimental results confirm the effectiveness of the proposed approach compared to other standard techniques.

COVID-19 Spread Pattern Using Support Vector Regression

2020 ◽  
Vol 8 (1) ◽  
pp. 67-74 ◽  
Author(s):  
Herlawati Herlawati
Keyword(s):  
Radial Basis Function ◽  
Support Vector Regression ◽  
Soft Computing ◽  
Basis Function ◽  
Rapid Development ◽  
Support Vector ◽  
Growth Simulation ◽  
Real Condition ◽  
Radial Basis ◽  
Spread Pattern

Pandemics are rare and happen in about 100 years period. Current pandemic, COVID-19, occurs in the industrial 4.0 era where there is a rapid development computation. Yet, the scientists in every country face difficulty in predicting the growth simulation of this pandemic. The paper tries to use a soft computing algorithm to predict the pattern of the COVID-19 pandemic in Indonesia. Support Vector Regression was used in Google Interactive Notebook with some kernels for comparison, i.e. radial basis function, linear and polynomial. The testing results showed that radial basis function outperformed other kernels as a regressor with some parameters should follows the real condition, i.e. gamma, c, and epsilon.

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