This paper introduces a methodology for predicting and mapping surface motion beneath road pavement structures caused by environmental factors. Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) ...
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This paper introduces a methodology for predicting and mapping surface motion beneath road pavement structures caused by environmental factors. Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) measurements, geospatial analyses, and Machine Learning algorithms (MLAs) are employed for achieving the purpose. Two single learners, i.e., Regression Tree (RT) and Support Vector Machine (SVM), and two ensemble learners, i.e., Boosted Regression Trees (BRT) and Random Forest (RF) are utilized for estimating the surface motion ratio in terms of mm/year over the Province of Pistoia (Tuscany Region, central Italy, 964 km(2)), in which strong subsidence phenomena have occurred. The interferometric process of 210 Sentinel-1 images from 2014 to 2019 allows exploiting the average displacements of 52,257 Persistent Scatterers as output targets to predict. A set of 29 environmental-related factors are preprocessed by SAGA-GIS, version 2.3.2, and ESRI ArcGIS, version 10.5, and employed as input features. Once the dataset has been prepared, three wrapper feature selection approaches (backward, forward, and bi-directional) are used for recognizing the set of most relevant features to be used in the modeling. A random splitting of the dataset in 70% and 30% is implemented to identify the training and test set. Through a bayesian optimization algorithm (BOA) and a 10-Fold Cross-Validation (CV), the algorithms are trained and validated. Therefore, the Predictive Performance of MLAs is evaluated and compared by plotting the Taylor Diagram. Outcomes show that SVM and BRT are the most suitable algorithms;in the test phase, BRT has the highest Correlation Coefficient (0.96) and the lowest Root Mean Square Error (0.44 mm/year), while the SVM has the lowest difference between the standard deviation of its predictions (2.05 mm/year) and that of the reference samples (2.09 mm/year). Finally, algorithms are used for mapping surface motion over the study area. We propose thre
Dynamic environments are still a big challenge for optimizationalgorithms. In this paper, a Genetic algorithm using both Multiploid representation and the bayesian Decision method is proposed. By Multiploid represent...
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Dynamic environments are still a big challenge for optimizationalgorithms. In this paper, a Genetic algorithm using both Multiploid representation and the bayesian Decision method is proposed. By Multiploid representation, an implicit memory scheme is introduced to transfer useful information to the next generations. In this representation, there are more than one genotypes and only one phenotype. The phenotype values are determined based on the corresponding genotypes values. To determine phe-notype values, the well-known bayesian optimization algorithm (BOA) has been injected into our algo-rithm to create a Bayes Network by using the previous population to exploit interactions between variables. With this algorithm, we have solved the well-known Dynamic Knapsack Problem (DKP) with 100, 250, and 500 items. Also, we have compared our algorithm with the most recent algorithm in the literature by using the DKP with 100 items. Experiments have shown that the proposed algorithm is effi-cient and faster than the peer algorithms in the manner of tracking moving optima without using an explicit memory scheme. In conclusion, using relationships between variables within the optimizationalgorithms is useful when concerning dynamic environments.(c) 2022 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).
According to the requirements of temperature compensation for real-time and accuracy,a method of using bayesianalgorithm to optimize the gradient boosting tree regression is proposed to establish the temperature erro...
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According to the requirements of temperature compensation for real-time and accuracy,a method of using bayesianalgorithm to optimize the gradient boosting tree regression is proposed to establish the temperature error compensation model of fiber optic gyroscope,and it adopts the method of real-time acquisition of temperature change rate with multiple data windows to meet the requirements of online compensation and model *** fiber optic gyroscope is placed in a temperature box to perform a temperature change test of -40-60℃ to obtain measured *** temperature and temperature change rate are used as input,and bayesianalgorithmoptimization gradient lifting tree regression modeling and temperature rising and falling segment modeling are performed *** comparative experiment results show that the proposed model achieves the best compensation *** the compensation comparison test,it is verified that the proposed model has good compensation ability and generalization ability for non-training data.
The hybridization of genetic algorithms(GAs) and Tabu Search(TS) is one of the traditional problems in function optimization in the GA literature. However,most proposed methods so far have utilized GAs to explore glob...
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The hybridization of genetic algorithms(GAs) and Tabu Search(TS) is one of the traditional problems in function optimization in the GA literature. However,most proposed methods so far have utilized GAs to explore global candidates and TS to exploit local *** such methods,this paper discusses new algorithms to directly store individuals into multiple tabu lists during GA-iterations. The paper describes the basic idea,algorithms,experimental results, and their practical applications for social simulation and electric equipments design.
Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems. Genetic algorithms, the m...
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Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems. Genetic algorithms, the most well-known and representative evolutionary search technique, have been the subject of the major part of such applications. Estimation of distribution algorithms (EDAs) offer a novel evolutionary paradigm that constitutes a natural and attractive alternative to genetic algorithms. They make use of a probabilistic model, learnt from the promising solutions, to guide the search process. In this paper, we set out a basic taxonomy of EDA techniques, underlining the nature and complexity of the probabilistic model of each EDA variant. We review a set of innovative works that make use of EDA techniques to solve challenging bioinformatics problems, emphasizing the EDA paradigm's potential for further research in this domain.
This article proposes a competent hierarchical optimization method called the hierarchical bayesian optimization algorithm (hBOA). hBOA extends the bayesian optimization algorithm (BOA) by incorporating three importan...
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