scholarly journals A Method for Prediction of Waterlogging Economic Losses in a Subway Station Project

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1421
Author(s):  
Han Wu ◽  
Junwu Wang
Keyword(s):  
Search Algorithm ◽  
Economic Loss ◽  
Economic Losses ◽  
Subway Station ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Engineering Project ◽  
Disaster Loss ◽  
Penalty Factor ◽  
Input Variables

In order to effectively solve the problems of low prediction accuracy and calculation efficiency of existing methods for estimating economic loss in a subway station engineering project due to rainstorm flooding, a new intelligent prediction model is developed using the sparrow search algorithm (SSA), the least-squares support vector machine (LSSVM) and the mean impact value (MIV) method. First, in this study, 11 input variables are determined from the disaster loss rate and asset value, and a complete method is provided for acquiring and processing data of all variables. Then, the SSA method, with strong optimization ability, fast convergence and few parameters, is used to optimize the kernel function and the penalty factor parameters of the LSSVM. Finally, the MIV is used to identify the important input variables, so as to reduce the predicted input variables and achieve higher calculation accuracy. In addition, 45 station projects in China were selected for empirical analysis. The empirical results revealed that the linear correlation between the 11 input variables and output variables was weak, which demonstrated the necessity of adopting nonlinear analysis methods such as the LSSVM. Compared with other forecasting methods, such as the multiple regression analysis, the backpropagation neural network (BPNN), the BPNN optimized by the particle swarm optimization, the BPNN optimized by the SSA, the LSSVM, the LSSVM optimized by the genetic algorithm, the PSO-LSSVM and the LSSVM optimized by the Grey Wolf Optimizer, the model proposed in this paper had higher accuracy and stability and was effectively used for forecasting economic loss in subway station engineering projects due to rainstorms.

Ship Manoeuvring Model Parameter Identification Using Intelligent Machine Learning Method and the Beetle Antennae Search Algorithm

2019 ◽  
Author(s):  
Changyuan Chen ◽  
Manases Tello Ruiz ◽  
Evert Lataire ◽  
Guillaume Delefortrie ◽  
Marc Mansuy ◽  
...  
Keyword(s):  
Search Algorithm ◽  
Nonlinear Least Squares ◽  
Training Sample ◽  
Support Vector ◽  
Ship Motions ◽  
Unknown Parameters ◽  
Training Samples ◽  
Penalty Factor ◽  
Rbf Kernel ◽  
Nomoto Model

Abstract In order to identify more accurately and efficiently the unknown parameters of a ship motions model, a novel Nonlinear Least Squares Support Vector Machine (NLSSVM) algorithm, whose penalty factor and Radial Basis Function (RBF) kernel parameters are optimised by the Beetle Antennae Search algorithm (BAS), is proposed and investigated. Aiming at validating the accuracy and applicability of the proposed method, the method is employed to identify the linear and nonlinear parameters of the first-order nonlinear Nomoto model with training samples from numerical simulation and experimental data. Subsequently, the identified parameters are applied in predicting the ship motion. The predicted results illustrate that the new NLSSVM-BAS algorithm can be applied in identifying ship motion’s model, and the effectiveness is verified. Compared among traditional identification approaches with the proposed method, the results display that the accuracy is improved. Moreover, the robust and stability of the NLSSVM-BAS are verified by adding noise in the training sample data.

scholarly journals Simulation of Titicaca Lake Water Level Fluctuations Using Hybrid Machine Learning Technique Integrated with Grey Wolf Optimizer Algorithm

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3015 ◽  
Author(s):  
Babak Mohammadi ◽  
Yiqing Guan ◽  
Pouya Aghelpour ◽  
Samad Emamgholizadeh ◽  
Ramiro Pillco Zolá ◽  
...  
Keyword(s):  
Random Forest ◽  
Water Level ◽  
Lake Water ◽  
Water Level Fluctuations ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Time Step ◽  
Lake Water Level ◽  
Input Variables

Lakes have an important role in storing water for drinking, producing hydroelectric power, and environmental, agricultural, and industrial uses. In order to optimize the use of lakes, precise prediction of the lake water level (LWL) is a main issue in water resources management. Due to the existence of nonlinear relations, uncertainty, and characteristics of the time series variables, the exact prediction of the lake water level is difficult. In this study the hybrid support vector regression (SVR) and the grey wolf algorithm (GWO) are used to predict lake water level fluctuations. Also, three types of data preprocessing methods, namely Principal component analysis, Random forest, and Relief algorithm were used for finding the best input variables for prediction LWL by the SVR and SVR-GWO models. Before the LWL simulation on monthly time step using the hybrid model, an evolutionary approach based on different monthly lags was conducted for determining the best mask of the input variables. Results showed that based on the random forest method, the best scenario of the inputs was Xt−1, Xt−2, Xt−3, Xt−4 for the SVR-GWO model. Also, the performance of the SVR-GWO model indicated that it could simulate the LWL with acceptable accuracy (with RMSE = 0.08 m, MAE = 0.06 m, and R2 = 0.96).

scholarly journals Inspection method of combine assembly quality based on optimized VMD

2021 ◽  
Vol 2125 (1) ◽  
pp. 012021
Author(s):  
Menghui Xuan ◽  
Sixia Zhao ◽  
Mengnan Liu ◽  
Liyou Xu ◽  
Xiaoliang Chen
Keyword(s):  
Search Algorithm ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Modal Parameter ◽  
Assembly Quality ◽  
Inspection Method ◽  
Feature Extraction Method ◽  
Mode Decomposition ◽  
Penalty Factor

Abstract Aiming at the problems of low assembly accuracy and difficult to detect assembly quality of combine, a method of combine assembly quality detection based on sparrow search algorithm (SSA) optimized variational mode decomposition (VMD) and particle swarm optimization (PSO) optimized least squares support vector machine (LSSVM) was proposed, Firstly, the sparrow search algorithm is used to obtain the optimal VMD decomposition modal parameter K and penalty factor α, then the combined vibration signal of combine harvester is decomposed into intrinsic modal components of different center frequencies by using the best parameter combination [K, α]. Finally, the feature vector is used as the input of LSSVM classifier to classify different fault features. The analysis results show that the classification accuracy of SSA-VMD joint feature extraction method is 99.5%, which is 17.5% and 9.5% higher than ensemble empirical mode decomposition (EEMD) and fixed parameter VMD, which verifies the superiority of this method in the detection of combine assembly quality.

Fertiliser evenness - losses and costs: A study on the economic benefits of uniform applications of fertiliser

1999 ◽  
pp. 215-220 ◽  
Author(s):  
R. Horrell ◽  
A.K. Metherell ◽  
S. Ford ◽  
C. Doscher
Keyword(s):  
Crop Yields ◽  
Cumulative Effect ◽  
Economic Loss ◽  
Economic Benefits ◽  
Significant Loss ◽  
Economic Losses ◽  
Financial Return ◽  
Spread Pattern ◽  
Fertiliser Application ◽  
Seed Crops

Over two million tonnes of fertiliser are applied to New Zealand pastures and crops annually and there is an increasing desire by farmers to ensure that the best possible economic return is gained from this investment. Spreading distribution measurements undertaken by Lincoln Ventures Ltd (LVL) have identified large variations in the evenness of fertiliser application by spreading machines which could lead to a failure to achieve optimum potential in some crop yields and to significant associated economic losses. To quantify these losses, a study was undertaken to calculate the effect of uneven fertiliser application on crop yield. From LVL's spreader database, spread patterns from many machines were categorised by spread pattern type and by coefficient of variation (CV). These patterns were then used to calculate yield losses when they were combined with the response data from five representative cropping and pastoral situations. Nitrogen fertiliser on ryegrass seed crops shows significant production losses at a spread pattern CV between 30% and 40%. For P and S on pasture, the cumulative effect of uneven spreading accrues, until there is significant economic loss occurring by year 3 for both the Waikato dairy and Southland sheep and beef systems at CV values between 30% and 40%. For nitrogen on pasture, significant loss in a dairy system occurs at a CV of approximately 40% whereas for a sheep and beef system it is at a CV of 50%, where the financial return from nitrogen application has been calculated at the average gross revenue of the farming system. The conclusion of this study is that the current Spreadmark standards are a satisfactory basis for defining the evenness requirements of fertiliser applications in most circumstances. On the basis of Spreadmark testing to date, more than 50% of the national commercial spreading fleet fails to meet the standard for nitrogenous fertilisers and 40% fails to meet the standard for phosphatic fertilisers.Keywords: aerial spreading, crop response, economic loss, fertiliser, ground spreading, striping, uneven application, uneven spreading, yield loss

scholarly journals Quantification of PV Power and Economic Losses Due to Soiling in Qatar

2021 ◽  
Vol 13 (6) ◽  
pp. 3364
Author(s):  
Amr Zeedan ◽  
Abdulaziz Barakeh ◽  
Khaled Al-Fakhroo ◽  
Farid Touati ◽  
Antonio S. P. Gonzales
Keyword(s):  
Output Power ◽  
Power Plants ◽  
Economic Loss ◽  
Economic Losses ◽  
Energy Yield ◽  
Solar Pv ◽  
Financial Loss ◽  
Grid Systems ◽  
Dust Density

Soiling losses of photovoltaic (PV) panels due to dust lead to a significant decrease in solar energy yield and result in economic losses; this hence poses critical challenges to the viability of PV in smart grid systems. In this paper, these losses are quantified under Qatar’s harsh environment. This quantification is based on experimental data from long-term measurements of various climatic parameters and the output power of PV panels located in Qatar University’s Solar facility in Doha, Qatar, using a customized measurement and monitoring setup. A data processing algorithm was deliberately developed and applied, which aimed to correlate output power to ambient dust density in the vicinity of PV panels. It was found that, without cleaning, soiling reduced the output power by 43% after six months of exposure to an average ambient dust density of 0.7 mg/m3. The power and economic loss that would result from this power reduction for Qatar’s ongoing solar PV projects has also been estimated. For example, for the Al-Kharasaah project power plant, similar soiling loss would result in about a 10% power decrease after six months for typical ranges of dust density in Qatar’s environment; this, in turn, would result in an 11,000 QAR/h financial loss. This would pose a pressing need to mitigate soiling effects in PV power plants.

scholarly journals A FOD Detection Approach on Millimeter-Wave Radar Sensors Based on Optimal VMD and SVDD

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 997
Author(s):  
Jun Zhong ◽  
Xin Gou ◽  
Qin Shu ◽  
Xing Liu ◽  
Qi Zeng
Keyword(s):  
Millimeter Wave ◽  
Detection Methods ◽  
Economic Losses ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Range Resolution ◽  
Radar Sensors ◽  
Detection Approach ◽  
Wave Radar ◽  
Whale Optimization

Foreign object debris (FOD) on airport runways can cause serious accidents and huge economic losses. FOD detection systems based on millimeter-wave (MMW) radar sensors have the advantages of higher range resolution and lower power consumption. However, it is difficult for traditional FOD detection methods to detect and distinguish weak signals of targets from strong ground clutter. To solve this problem, this paper proposes a new FOD detection approach based on optimized variational mode decomposition (VMD) and support vector data description (SVDD). This approach utilizes SVDD as a classifier to distinguish FOD signals from clutter signals. More importantly, the VMD optimized by whale optimization algorithm (WOA) is used to improve the accuracy and stability of the classifier. The results from both the simulation and field case show the excellent FOD detection performance of the proposed VMD-SVDD method.

scholarly journals Prediction of Healing Performance of Autogenous Healing Concrete Using Machine Learning

Materials ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4068
Author(s):  
Xu Huang ◽  
Mirna Wasouf ◽  
Jessada Sresakoolchai ◽  
Sakdirat Kaewunruen
Keyword(s):  
Machine Learning ◽  
Search Algorithm ◽  
Weather Conditions ◽  
Prediction Performance ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Support Vector ◽  
Self Healing ◽  
Artificial Neural Network Ann

Cracks typically develop in concrete due to shrinkage, loading actions, and weather conditions; and may occur anytime in its life span. Autogenous healing concrete is a type of self-healing concrete that can automatically heal cracks based on physical or chemical reactions in concrete matrix. It is imperative to investigate the healing performance that autogenous healing concrete possesses, to assess the extent of the cracking and to predict the extent of healing. In the research of self-healing concrete, testing the healing performance of concrete in a laboratory is costly, and a mass of instances may be needed to explore reliable concrete design. This study is thus the world’s first to establish six types of machine learning algorithms, which are capable of predicting the healing performance (HP) of self-healing concrete. These algorithms involve an artificial neural network (ANN), a k-nearest neighbours (kNN), a gradient boosting regression (GBR), a decision tree regression (DTR), a support vector regression (SVR) and a random forest (RF). Parameters of these algorithms are tuned utilising grid search algorithm (GSA) and genetic algorithm (GA). The prediction performance indicated by coefficient of determination (R2) and root mean square error (RMSE) measures of these algorithms are evaluated on the basis of 1417 data sets from the open literature. The results show that GSA-GBR performs higher prediction performance (R2GSA-GBR = 0.958) and stronger robustness (RMSEGSA-GBR = 0.202) than the other five types of algorithms employed to predict the healing performance of autogenous healing concrete. Therefore, reliable prediction accuracy of the healing performance and efficient assistance on the design of autogenous healing concrete can be achieved.

scholarly journals GMBO: Group Mean-Based Optimizer for Solving Various Optimization Problems

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1190
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Štěpán Hubálovský
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Random Search ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Main Idea ◽  
Population Based ◽  
Grey Wolf Optimizer ◽  
Whale Optimization

There are many optimization problems in the different disciplines of science that must be solved using the appropriate method. Population-based optimization algorithms are one of the most efficient ways to solve various optimization problems. Population-based optimization algorithms are able to provide appropriate solutions to optimization problems based on a random search of the problem-solving space without the need for gradient and derivative information. In this paper, a new optimization algorithm called the Group Mean-Based Optimizer (GMBO) is presented; it can be applied to solve optimization problems in various fields of science. The main idea in designing the GMBO is to use more effectively the information of different members of the algorithm population based on two selected groups, with the titles of the good group and the bad group. Two new composite members are obtained by averaging each of these groups, which are used to update the population members. The various stages of the GMBO are described and mathematically modeled with the aim of being used to solve optimization problems. The performance of the GMBO in providing a suitable quasi-optimal solution on a set of 23 standard objective functions of different types of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal is evaluated. In addition, the optimization results obtained from the proposed GMBO were compared with eight other widely used optimization algorithms, including the Marine Predators Algorithm (MPA), the Tunicate Swarm Algorithm (TSA), the Whale Optimization Algorithm (WOA), the Grey Wolf Optimizer (GWO), Teaching–Learning-Based Optimization (TLBO), the Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA). The optimization results indicated the acceptable performance of the proposed GMBO, and, based on the analysis and comparison of the results, it was determined that the GMBO is superior and much more competitive than the other eight algorithms.

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