scholarly journals Estimating Yield and Water Productivity of Tomato Using a Novel Hybrid Approach

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3615
Author(s):  
Hossein Dehghanisanij ◽  
Somayeh Emami ◽  
Mohammed Achite ◽  
Nguyen Thi Thuy Linh ◽  
Quoc Bao Pham
Keyword(s):  
Hybrid Method ◽  
Drip Irrigation ◽  
High Efficiency ◽  
Hybrid Methods ◽  
Climatic Factors ◽  
Water Productivity ◽  
Hybrid Approach ◽  
Support Vector ◽  
Plastic Mulch ◽  
Crop Variety

Water productivity (WP) of crops is affected by water–fertilizer management in interaction with climatic factors. This study aimed to evaluate the efficiency of a hybrid method of season optimization algorithm (SO) and support vector regression (SVR) in estimating the yield and WP of tomato crops based on climatic factors, irrigation–fertilizer under the drip irrigation, and plastic mulch. To approve the proposed method, 160 field data including water consumption during the growing season, fertilizers, climatic variables, and crop variety were applied. Two types of treatments, namely drip irrigation (DI) and drip irrigation with plastic mulch (PMDI), were considered. Seven different input combinations were used to estimate yield and WP. R2, RMSE, NSE, SI, and σ criteria were utilized to assess the proposed hybrid method. A good agreement was presented between the observed (field monitoring data) and estimated (calculated with SO–SVR method) values (R2 = 0.982). The irrigation–-fertilizer parameters (PMDI, F) and crop variety (V) are the most effective in estimating the yield and WP of tomato crops. Statistical analysis of the obtained results showed that the SO–SVR hybrid method has high efficiency in estimating WP and yield. In general, intelligent hybrid methods can enable the optimal and economical use of water and fertilizer resources.

scholarly journals A New Hybrid Approach for Short-Term Electric Load Forecasting Applying Support Vector Machine with Ensemble Empirical Mode Decomposition and Whale Optimization

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1520 ◽  
Author(s):  
Liu ◽  
Jin ◽  
Gao
Keyword(s):  
Parameter Optimization ◽  
Hybrid Method ◽  
Hybrid Approach ◽  
Load Forecasting ◽  
Data Preprocessing ◽  
Support Vector ◽  
Electric Load ◽  
Short Term ◽  
Whale Optimization ◽  
Electric Load Forecasting

Electrical power system forecasting has been a main focus for researchers who want to improve the effectiveness of a power station. Although some traditional models have been proved suitable for short-term electric load forecasting, its nature of ignoring the significance of parameter optimization and data preprocessing usually results in low forecasting accuracy. This paper proposes a short-term hybrid forecasting approach which consists of the three following modules: Data preprocessing, parameter optimization algorithm, and forecasting. This hybrid model overcomes the disadvantages of the conventional model and achieves high forecasting performance. To verify the forecasting effectiveness of the hybrid method, 30-minutes of electric load data from power stations in New South Wales and Queensland are used for conducting experiments. A comprehensive evaluation, including a Diebold-Mariano (DM) test and forecasting effectiveness, is applied to verify the ability of the hybrid approach. Experimental results indicated that the new hybrid method can perform accurate electric load forecasting, which can be regarded as a powerful assist in managing smart grids.

scholarly journals A Hybrid Approach for Short-Term Forecasting of Wind Speed

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Sivanagaraja Tatinati ◽  
Kalyana C. Veluvolu
Keyword(s):  
Wind Speed ◽  
Hybrid Method ◽  
Hybrid Approach ◽  
Adaptive Methods ◽  
Correlation Factor ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Wind Speed Data ◽  
Mode Decomposition ◽  
The Individual

We propose a hybrid method for forecasting the wind speed. The wind speed data is first decomposed into intrinsic mode functions (IMFs) with empirical mode decomposition. Based on the partial autocorrelation factor of the individual IMFs, adaptive methods are then employed for the prediction of IMFs. Least squares-support vector machines are employed for IMFs with weak correlation factor, and autoregressive model with Kalman filter is employed for IMFs with high correlation factor. Multistep prediction with the proposed hybrid method resulted in improved forecasting. Results with wind speed data show that the proposed method provides better forecasting compared to the existing methods.

scholarly journals PARAMETRIC STUDY OF PARALLEL BLOCK JACOBI / SOURCE ITERATION HYBRID METHODS IN 2-D CARTESIAN GEOMETRY AND CONSTRUCTION OF THE INTEGRAL TRANSPORT MATRIX METHOD MATRICES VIA GREEN’S FUNCTIONS

2021 ◽  
Vol 247 ◽  
pp. 03017
Author(s):  
Dylan S. Hoagland ◽  
Yousry Y. Azmy
Keyword(s):  
Hybrid Method ◽  
Parametric Study ◽  
Matrix Method ◽  
Unstructured Grids ◽  
Hybrid Methods ◽  
Neutron Transport ◽  
Hybrid Approach ◽  
Massively Parallel ◽  
Neutron Transport Equation ◽  
Integral Transport

Parallel Block Jacobi (PBJ) [1] is an asynchronous spatial domain decomposition with application in solving the neutron transport equation due to its extendibility to massively parallel solution in unstructured spatial meshes (grids) without the use of the computationally complex and expensive sweeps required by the Source Iteration (SI) method in these applications. [2] However, PBJ iterative methods suffer a lack of iterative robustness in problems with optically thin cells, [1] which we have previously demonstrated to be a consequence of PBJ’s asynchronicity. To mitigate this effect, we have developed multiple PBJ / SI hybrid methods which employ a PBJ method (Parallel Block Jacobi - Integral Transport Matrix Method (PBJ-ITMM) or Inexact Parallel Block Jacobi (IPBJ)) along with SI. [3,4] In this work, we perform a parametric study to determine performance of numerous PBJ / SI hybrid methods as a function of multiple problem parameters. This parametric study reached 5 main conclusions: 1) our hybrid approach is more effective with PBJ-ITMM than with IPBJ, 2) for PBJ-ITMM, there is a hybrid method that mitigates the aforementioned iterative slowdown in optically thin cells without diminishing the method’s potential parallelism in unstructured grids, 3) this hybrid method is most effective in problems with large, continuous regions of very thin cells, 4) the best performing hybrid method consistently executes within a factor of ten slower than current state-of-the-art acceleration methods that are not efficiently extendable to the massively parallel regime, and 5) both PBJ-ITMM and IPBJ are observed to be viable approaches for our desired applications. In the pursuit of implementing PBJ-ITMM in unstructured grids, we conclude with a description of the Green’s Function ITMM Construction (GFIC) algorithm, which allows for the ITMM matrices to be constructed using the pre-existing SI sweep algorithm already present in unstructured grid SN transport codes.

Water use efficiency and productivity of habanero pepper (Capsicum chinense Jacq.) based on two transplanting dates

2015 ◽  
Vol 71 (6) ◽  
pp. 885-891 ◽  
Author(s):  
Rutilo López-López ◽  
Marco Antonio Inzunza-Ibarra ◽  
Ignacio Sánchez-Cohen ◽  
Andrés Fierro-Álvarez ◽  
Ernesto Sifuentes-Ibarra
Keyword(s):  
Water Use Efficiency ◽  
Water Use ◽  
Drip Irrigation ◽  
Water Productivity ◽  
Fruit Yield ◽  
Cost Ratio ◽  
Plastic Mulch ◽  
Benefit Cost Ratio ◽  
Habanero Pepper ◽  
Use Efficiency

Habanero pepper production was assessed with drip irrigation and plastic mulch, based on two transplanting dates. The objectives of the study were: (i) to evaluate the effect of two transplanting dates and the use of plastic mulch on water productivity and habanero pepper fruit yield under drip irrigation conditions; and (ii) to determine the profitability and economic viability of the product in the regional market. The work was conducted in the municipality of Huimanguillo, state of Tabasco, Mexico, in loam soils classified as Eutric Fluvisol. The Jaguar variety of habanero pepper, developed by INIFAP and possessing better genetic and productive characteristics, was used. Two transplanting dates were studied, (i) 30 January 2013 and (ii) 15 February 2013, with and without plastic mulch. The conclusions were: (i) application of irrigation depths based on crop evapotranspiration (ETc) and plastic mulch transplanted on 30 January increased the fruit yield of the crop and improved the benefit–cost ratio of the production system; and (ii) water use efficiency based on the 30 January transplanting date was 8.68 kg m−3 of water applied with plastic mulch, 6.51 kg m−3 without plastic mulch, and 3.65 kg m−3 for the 15 February transplanting date with plastic mulch.

scholarly journals A Potential Flow Theory and Boundary Layer Theory Based Hybrid Method for Waterjet Propulsion

2019 ◽  
Vol 7 (4) ◽  
pp. 113 ◽  
Author(s):  
Zhang ◽  
Zhang ◽  
Shang
Keyword(s):  
Boundary Layer ◽  
Hybrid Method ◽  
High Efficiency ◽  
Hybrid Approach ◽  
Iteration Process ◽  
Empirical Method ◽  
Boundary Layer Theory ◽  
Physical Parameters ◽  
Propulsion Performance ◽  
Local Advantage

A hybrid method—coupled with the boundary element method (BEM) for wave-making resistance, the empirical method (EM) for viscous resistance, and the boundary layer theory (BLT) for capture of an area’s physical parameters—was proposed to predict waterjet propulsion performance. The waterjet propulsion iteration process was established from the force-balanced waterjet–hull system by applying the hybrid approach. Numerical validation of the present method was carried out using the 1/8.556 scale waterjet-propelled ITTC (International Towing Tank Conference) Athena ship model. Resistance, attitudes, wave cut profiles, waterjet thrust, and thrust deduction showed similar tendencies to the experimental curves and were in good agreement with the data. The application of the present hybrid method to the side-hull configuration research of a trimaran indicates that the side-hull arranged at the rear of the main hull contributed to energy-saving and high-efficiency propulsion. In addition, at high Froude numbers, the “fore-body trimaran” showed a local advantage in resistance and thrust deduction.

IRRIGATION SCHEDULE OF LONG-FRUIT CUCUMBERS AND HYDROAMELIORATIVE REQUIREMENTS FOR DRIP IRRIGATION IN PLASTIC GREENHOUSE

2019 ◽  
pp. 307-313
Author(s):  
Rumiana Kireva ◽  
Roumen Gadjev
Keyword(s):  
Irrigation Water ◽  
Drip Irrigation ◽  
Water Productivity ◽  
Irrigation Schedule ◽  
Water Source ◽  
Climate Conditions ◽  
Irrigation Technology ◽  
Irrigation Technologies ◽  
Pressure Flows ◽  
Efficient Water Use

The deficit of the irrigation water requires irrigation technologies with more efficient water use. For cucumbers, the most suitable is the drip irrigation technology. For establishing of the appropriate irrigation schedule of cucumbers under the soil and climate conditions in the village of Chelopechene, near Sofia city, the researchеs was conducted with drip irrigation technology, adopting varying irrigation schedules and hydraulic regimes - from fully meeting the daily crops water requirements cucumbers to reduced depths with 20% and 40%. It have been established irrigation schedule with adequate pressure flows in the water source, irrigation water productivity and yields of in plastic unheated greenhouses of the Sofia plant.

Effect of Irrigation Methods and Mulching on Growth and Seed Yield of Semialata

2020 ◽  
Vol 7 (01) ◽  
Author(s):  
SK SRIVASTAVA ◽  
PAWAN JEET
Keyword(s):  
Seed Yield ◽  
Drip Irrigation ◽  
Growth Parameters ◽  
Block Design ◽  
Water Requirement ◽  
Furrow Irrigation ◽  
Plastic Mulch ◽  
Randomized Block Design ◽  
Daily Water ◽  
Black Plastic

A study was conducted to assess the effect of drip irrigation and plastic mulch on growth and seed yield of Semialata. Two types of plastic mulch (green and silver/black) were tested at three levels of irrigation (120%, 100% and 80%) by drip irrigation and one level (100%) by furrow irrigation. The daily water requirement of Semialata was estimated by the equation ETcrop= ETox crop factor. ETcrop is crop water requirement mm/day. ETo (reference evapotranspiration, mm/day) was calculated by FAO calculator which uses temperature and humidity data. In this experiments there were twelve treatments were considered. The treatments were replicated thrice. The experiment was laid in randomized block design. It was observed that drip irrigation with or without plastic mulch is yielding better results in terms of growth parameters and seed yield as compared to furrow irrigation without plastic mulch. It was also observed that maximum suppression (67.58%) of weeds resulted with drip irrigation and silver/black plastic mulch at 80% level of irrigation.

A visual terrain classification method for mobile robots’ navigation based on convolutional neural network and support vector machine

2021 ◽  
pp. 014233122098791
Author(s):  
Wanli Wang ◽  
Botao Zhang ◽  
Kaiqi Wu ◽  
Sergey A Chepinskiy ◽  
Anton A Zhilenkov ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Mobile Robots ◽  
Convolutional Neural Network ◽  
Hybrid Method ◽  
Classification Accuracy ◽  
Support Vector ◽  
High Classification Accuracy ◽  
Enhance Efficiency ◽  
Multi Class Classification

In this paper, a hybrid method based on deep learning is proposed to visually classify terrains encountered by mobile robots. Considering the limited computing resource on mobile robots and the requirement for high classification accuracy, the proposed hybrid method combines a convolutional neural network with a support vector machine to keep a high classification accuracy while improve work efficiency. The key idea is that the convolutional neural network is used to finish a multi-class classification and simultaneously the support vector machine is used to make a two-class classification. The two-class classification performed by the support vector machine is aimed at one kind of terrain that users are mostly concerned with. Results of the two classifications will be consolidated to get the final classification result. The convolutional neural network used in this method is modified for the on-board usage of mobile robots. In order to enhance efficiency, the convolutional neural network has a simple architecture. The convolutional neural network and the support vector machine are trained and tested by using RGB images of six kinds of common terrains. Experimental results demonstrate that this method can help robots classify terrains accurately and efficiently. Therefore, the proposed method has a significant potential for being applied to the on-board usage of mobile robots.

scholarly journals Regional Zenith Tropospheric Delay Modeling Based on Least Squares Support Vector Machine Using GNSS and ERA5 Data

2021 ◽  
Vol 13 (5) ◽  
pp. 1004
Author(s):  
Song Li ◽  
Tianhe Xu ◽  
Nan Jiang ◽  
Honglei Yang ◽  
Shuaimin Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
High Efficiency ◽  
Rapid Development ◽  
Support Vector ◽  
Tropospheric Delay ◽  
Improvement Rate ◽  
Zenith Tropospheric Delay ◽  
Background Data ◽  
Mean Square Errors

The meteorological reanalysis data has been widely applied to derive zenith tropospheric delay (ZTD) with a high spatial and temporal resolution. With the rapid development of artificial intelligence, machine learning also begins as a high-efficiency tool to be employed in modeling and predicting ZTD. In this paper, we develop three new regional ZTD models based on the least squares support vector machine (LSSVM), using both the International GNSS Service (IGS)-ZTD products and European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) data over Europe throughout 2018. Among them, the ERA5 data is extended to ERA5S-ZTD and ERA5P-ZTD as the background data by the model method and integral method, respectively. Depending on different background data, three schemes are designed to construct ZTD models based on the LSSVM algorithm, including the without background data, with the ERA5S-ZTD, and with the ERA5P-ZTD. To investigate the advantage and feasibility of the proposed ZTD models, we evaluate the accuracy of two background data and three schemes by segmental comparison with the IGS-ZTD of 85 IGS stations in Europe. The results show that the overall average Root Mean Square Errors (RMSE) value of all sites is 30.1 mm for the ERA5S-ZTD, and 10.7 mm for the ERA5P-ZTD. The overall average RMSE is 25.8 mm, 22.9 mm, and 9 mm for the three schemes, respectively. Moreover, the overall improvement rate is 19.1% and 1.6% for the ZTD model with ERA5S-ZTD and ERA5P-ZTD, respectively. In order to explore the reason of the lower improvement for the ZTD model with ERA5P-ZTD, the loop verification is performed by estimating the ZTD values of each available IGS station. In actuality, the monthly improvement rate of estimated ZTD is positive for most stations, and the biggest improvement rate can even reach about 40%. The negative rate mainly comes from specific stations, these stations are located on the edge of the region, near the coast, as well as the lower similarity between the individual verified station and training stations.

scholarly journals Discriminative Learning Approach Based on Flexible Mixture Model for Medical Data Categorization and Recognition

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2450
Author(s):  
Fahd Alharithi ◽  
Ahmed Almulihi ◽  
Sami Bourouis ◽  
Roobaea Alroobaea ◽  
Nizar Bouguila
Keyword(s):  
Mixture Model ◽  
Hybrid Approach ◽  
Medical Data ◽  
Support Vector ◽  
Discriminative Learning ◽  
Learning Approach ◽  
X Rays ◽  
Intrinsic Nature ◽  
Discriminative Models ◽  
Dirichlet Mixture

In this paper, we propose a novel hybrid discriminative learning approach based on shifted-scaled Dirichlet mixture model (SSDMM) and Support Vector Machines (SVMs) to address some challenging problems of medical data categorization and recognition. The main goal is to capture accurately the intrinsic nature of biomedical images by considering the desirable properties of both generative and discriminative models. To achieve this objective, we propose to derive new data-based SVM kernels generated from the developed mixture model SSDMM. The proposed approach includes the following steps: the extraction of robust local descriptors, the learning of the developed mixture model via the expectation–maximization (EM) algorithm, and finally the building of three SVM kernels for data categorization and classification. The potential of the implemented framework is illustrated through two challenging problems that concern the categorization of retinal images into normal or diabetic cases and the recognition of lung diseases in chest X-rays (CXR) images. The obtained results demonstrate the merits of our hybrid approach as compared to other methods.

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