scholarly journals Assessing accuracy of barley yield forecasting with integration of climate variables and support vector regression

2019 ◽  
Vol 73 (1) ◽  
pp. 19
Author(s):  
Laleh Parviz
Keyword(s):  
Support Vector Regression ◽  
Correlation Coefficient ◽  
Crop Yield ◽  
High Efficiency ◽  
Evaluation Criteria ◽  
Accurate Estimation ◽  
Support Vector ◽  
Climate Variables ◽  
Climate Effect ◽  
The Impact

<p>Investigations of the relation between crop yield and climate variables are crucial for agricultural studies and decision making related to crop monitoring. Multiple linear regression (MLR) and support vector regression (SVR) are used to identify and model the impact of climate variables on barley yield. The climate variables of 36 years (1982–2017) are gathered from three provinces of Iran with different climate: Yazd (arid), Zanjan (semi-arid), Gilan (very humid). Air temperature by high correlation coefficient with barley yield was introduced as the dominant climate variable. According to evaluation criteria, SVR provided accurate estimation of crop yield in comparison with MLR. The diversity of climate impressed the estimated yield in which UI, decreasing from Gilan to Yazd provinces, was 47.77%. Support vector machine (SVM) with capturing the nonlinearity of time series, could improve barley yield estimation, with the minimum UI for Yazd province. Also, the minimum correlation coefficient between the observed and simulated yield was found in Gilan province. Based on GMER calculations, SVM forecasts were underestimated in three provinces. All findings show that SVM is able to have high efficiency to model the climate effect on crop yield.</p>

scholarly journals Robust Speed Control of a Three Phase Induction Motor Using Support Vector Regression

2021 ◽  
Vol 11 (6) ◽  
pp. 7861-7866
Author(s):  
N. H. Mugheri ◽  
M. U. Keerio ◽  
S. Chandio ◽  
R. H. Memon
Keyword(s):  
Support Vector Regression ◽  
Induction Motor ◽  
High Efficiency ◽  
Speed Control ◽  
Pi Controller ◽  
Learning Ability ◽  
Maintenance Cost ◽  
Support Vector ◽  
Three Phase ◽  
Speed Response

The Three Phase Induction Motor (TIM) is one of the most widely used motors due to its low price, robustness, low maintenance cost, and high efficiency. In this paper, a Support Vector Regression (SVR) based controller for TIM speed control using Indirect Vector Control (IVC) is presented. The IVC method is more frequently used because it enables better speed control of the TIM with higher dynamic performance. Artificial Neural Network (ANN) controllers have been widely used for TIM speed control for several reasons such as their ability to successfully train without prior knowledge of the mathematical model, their learning ability, and their fast implementation speed. The SVR-based controller overcomes the drawbacks of the ANN-based controller, i.e. its low accuracy, overfitting, and poor generalization ability. The speed response under the proposed controller is faster in terms of rising and settling time. The dynamic speed response of the proposed controller is also superior to that of the ANN-PI controller. The performance of the proposed controller was compared for TIM speed control with an ANN-PI controller via simulations in SIMULINK.

scholarly journals Comparative Study for Classification Algorithms Performance in Crop Yields Prediction Systems

2021 ◽  
Vol 1 (2) ◽  
pp. 19-24
Author(s):  
Halbast Rashid Ismael ◽  
Adnan Mohsin Abdulazeez ◽  
Dathar A. Hasan
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Yields ◽  
Effective Field ◽  
Research Field ◽  
Support Vector ◽  
Classification Algorithms ◽  
Yield Quality ◽  
Prediction Systems ◽  
The Impact

The agriculture importance is not restricted to our daily life; it is also an effective field that enhances the economic growth in any country. Therefore, developing the quality of the crop yields using recent technologies is a crucial procedure to obtain competitive crops. Nowadays, data mining is an emerging research field in agriculture especially in the predicting and analysis of crop yield. This paper focuses on utilizing various data mining classification algorithms to predict the impact of various parameters such as area, season and production on the crop yield quality. The performance of the decision tree, naive Bayes, random forest, support vector machine and K-nearest neighbour is measured and compared to each other. The comparison involves measuring the error values and accuracy. The SVM algorithm achieved the highest accuracy value with 76.82%. while the lowest is achieved by the KNN algorithm with 35.76%. The highest error value was 111.8855 for KNN. Also, the prediction help farmer to increased and improved the income level.  

scholarly journals Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5763 ◽  
Author(s):  
Shahab S. Band ◽  
Saeid Janizadeh ◽  
Subodh Chandra Pal ◽  
Indrajit Chowdhuri ◽  
Zhaleh Siabi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Water Resource Management ◽  
Nitrate Concentration ◽  
Evaluation Criteria ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Accurate Estimation ◽  
Support Vector ◽  
Topographic Wetness Index ◽  
Agricultural Areas

Prediction of the groundwater nitrate concentration is of utmost importance for pollution control and water resource management. This research aims to model the spatial groundwater nitrate concentration in the Marvdasht watershed, Iran, based on several artificial intelligence methods of support vector machine (SVM), Cubist, random forest (RF), and Bayesian artificial neural network (Baysia-ANN) machine learning models. For this purpose, 11 independent variables affecting groundwater nitrate changes include elevation, slope, plan curvature, profile curvature, rainfall, piezometric depth, distance from the river, distance from residential, Sodium (Na), Potassium (K), and topographic wetness index (TWI) in the study area were prepared. Nitrate levels were also measured in 67 wells and used as a dependent variable for modeling. Data were divided into two categories of training (70%) and testing (30%) for modeling. The evaluation criteria coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE) were used to evaluate the performance of the models used. The results of modeling the susceptibility of groundwater nitrate concentration showed that the RF (R2 = 0.89, RMSE = 4.24, NSE = 0.87) model is better than the other Cubist (R2 = 0.87, RMSE = 5.18, NSE = 0.81), SVM (R2 = 0.74, RMSE = 6.07, NSE = 0.74), Bayesian-ANN (R2 = 0.79, RMSE = 5.91, NSE = 0.75) models. The results of groundwater nitrate concentration zoning in the study area showed that the northern parts of the case study have the highest amount of nitrate, which is higher in these agricultural areas than in other areas. The most important cause of nitrate pollution in these areas is agriculture activities and the use of groundwater to irrigate these crops and the wells close to agricultural areas, which has led to the indiscriminate use of chemical fertilizers by irrigation or rainwater of these fertilizers is washed and penetrates groundwater and pollutes the aquifer.

scholarly journals Multitask Support Vector Regression for Solar and Wind Energy Prediction

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6308
Author(s):  
Carlos Ruiz ◽  
Carlos M. Alaíz ◽  
José R. Dorronsoro
Keyword(s):  
Machine Learning ◽  
Wind Energy ◽  
Support Vector Regression ◽  
Energy Production ◽  
Multitask Learning ◽  
Support Vector ◽  
Prediction Problem ◽  
Energy Prediction ◽  
The Common ◽  
The Impact

Given the impact of renewable sources in the overall energy production, accurate predictions are becoming essential, with machine learning becoming a very important tool in this context. In many situations, the prediction problem can be divided into several tasks, more or less related between them but each with its own particularities. Multitask learning (MTL) aims to exploit this structure, training several models at the same time to improve on the results achievable either by a common model or by task-specific models. In this paper, we show how an MTL approach based on support vector regression can be applied to the prediction of photovoltaic and wind energy, problems where tasks can be defined according to different criteria. As shown experimentally with three different datasets, the MTL approach clearly outperforms the results of the common and specific models for photovoltaic energy, and are at the very least quite competitive for wind energy.

scholarly journals Support vector regression-based QSAR models for prediction of antioxidant activity of phenolic compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ying Shi
Keyword(s):  
Phenolic Compounds ◽  
Support Vector Regression ◽  
Correlation Coefficient ◽  
Pearson Correlation ◽  
Qsar Model ◽  
Heat Of Formation ◽  
Trolox Equivalent Antioxidant Capacity ◽  
Support Vector ◽  
Prediction Ability ◽  
Qsar Models

AbstractThe Support vector regression (SVR) was used to investigate quantitative structure–activity relationships (QSAR) of 75 phenolic compounds with Trolox-equivalent antioxidant capacity (TEAC). Geometric structures were optimized at the EF level of the MOPAC software program. Using Pearson correlation coefficient analysis, four molecular descriptors [n(OH), Cosmo Area (CA), Core-Core Repulsion (CCR) and Final Heat of Formation (FHF)] were selected as independent variables. The QSAR model was developed from the training set consisting of 57 compounds and then used the leave-one-out cross-validation (LOOCV) correlation coefficient to evaluate the prediction ability of the QSAR model. Used Artificial neural network (ANN) and multiple linear regression (MLR) for comparing. The RMSE (root mean square error) values of LOOCV in SVR, ANN and MLR models were 0.44, 0.46 and 0.54. The RMSE values of prediction of external 18 compounds were 0.41, 0.39 and 0.54 for SVR, ANN and MLR models, respectively. The obtained result indicated that the SVR models exhibited excellent predicting performance and competent for predicting the TEAC of phenolic compounds.

A Fuzzy-Based Support Vector Regression Framework for Crop Yield Prediction

2021 ◽  
pp. 173-185
Author(s):  
Uduak Umoh ◽  
Daniel Asuquo ◽  
Imoh Eyoh ◽  
Abdultaofeek Abayomi ◽  
Emmanuel Nyoho ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Crop Yield ◽  
Support Vector ◽  
Yield Prediction ◽  
Regression Framework

scholarly journals MPPT of Permanent Magnet Synchronous Generator in Tidal Energy Systems Using Support Vector Regression

2021 ◽  
Vol 13 (4) ◽  
pp. 2223
Author(s):  
Ahmed G. Abo-Khalil ◽  
Ali S. Alghamdi
Keyword(s):  
Permanent Magnet ◽  
Support Vector Regression ◽  
Tidal Current ◽  
High Efficiency ◽  
Synchronous Generator ◽  
Current Speed ◽  
Maximum Power ◽  
Support Vector ◽  
Generation System ◽  
Permanent Magnet Synchronous Generator

In this paper, an improved Maximum Power Point Tracking (MPPT) algorithm for a tidal power generation system using a Support Vector Regression (SVR) is proposed. To perform this MPPT, a tidal current speed sensor is needed to track the maximum power. The use of these sensors has a lack of reliability, requires maintenance, and has a disadvantage in terms of price. Therefore, there is a need for a sensorless MPPT control algorithm that does not require information on tidal current speed and rotation speed that improves these shortcomings. Sensorless MPPT control methods, such as SVR, enables the maximum power to be output by comparing the relationship between the output power and the rotational speed of the generator. The performance of the SVR is influenced by the selection of its parameters which is optimized during the offline training stage. SVR has a strength and better response than the neural network since it ensures the global minimum and avoids being stuck at local minima. This paper proposes a high-efficiency grid-connected tidal current generation system with a permanent magnet synchronous generator back-to-back converter. The proposed algorithm is verified experimentally and the results confirm the excellent control characteristics of the proposed algorithm.

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