Land subsidence poses one of the major natural hazards around the globe that cause damage to life and property. Although several advanced models have been applied to model land subsidence susceptibility, no consensus ...
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Land subsidence poses one of the major natural hazards around the globe that cause damage to life and property. Although several advanced models have been applied to model land subsidence susceptibility, no consensus has been reached on the most accurate models to study this phenomenon. In this work, we propose the use of the following five state-of-the-art models to calculate the susceptibility to land subsidence across a region in Iran: artificial neural network - satin bowerbird optimization (ANN-SBO), artificial neural network-water cycle algorithm (ANN-WCA), artificial neural network-chimp optimization algorithm (ANN-ChoA) and artificial neural network-crow search algorithm (ANN-CSA). We used 12 land subsidence predictors and 93 land subsidence locations as input data in the algorithms. The land subsidence locations were divided into training (65 locations or 70%) and validating (28 locations or 30%) samples. As per the importance factor analysis, the Groundwater Withdraw variable was found the most important factor among all input factors and the slope was found the least important factor among all. According to the validation procedure the most performing model, in terms of Success Rate, was WCA-ANN (AUC = 0.953), followed by ChOA-ANN (AUC = 0.944), SBO-ANN (AUC = 0.924), CSA-ANN (AUC = 0.915) and ANN (AUC = 0.913). For the Prediction Rate, the highest performance was achieved by WCA-ANN (AUC = 0.974), followed by ChOA-ANN (AUC = 0.958), SBO-ANN (AUC = 0.942), CSA-ANN (AUC = 0.931) and ANN (AUC = 0.927). The present work of such higher accuracy can be useful for the policymakers of govt. of Iran during operation work of any mega projects and implementation.
In this work we develop theoretical techniques for analysing the performance of the quantum approximate optimization algorithm (QAOA) when applied to random boolean constraint satisfaction problems (CSPs), and use the...
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In this paper, we propose control-theoretic methods as tools for the design of online optimization algorithms that are able to address dynamic, noisy, and partially uncertain time-varying quadratic objective functions...
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Bilevel optimization methods are increasingly relevant within machine learning, especially for tasks such as hyperparameter optimization and meta-learning. Compared to the offline setting, online bilevel optimization ...
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In this work, we address a class of nonconvex nonsmooth optimization problems where the objective function is the sum of two smooth functions (one of which is proximable) and two nonsmooth functions (one proper, close...
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作者：
Henke, DorotheeLefebvre, HenriSchmidt, MartinThürauf, JohannesUniversity of Passau
School of Business Economics and Information Systems Chair of Business Decisions and Data Science Dr.-Hans-Kapfinger-Str. 30 Passau94032 Germany Trier University
Department of Mathematics Universitätsring 15 Trier54296 Germany
Department Liberal Arts and Social Sciences Discrete Optimization Lab Dr.-Luise-Herzberg-Str. 4 Nuremberg90461 Germany
The literature on pessimistic bilevel optimization with coupling constraints is rather scarce and it has been common sense that these problems are harder to tackle than pessimistic bilevel problems without coupling co...
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In next basket recommendation (NBR) a set of items is recommended to users based on their historical basket sequences. In many domains, the recommended baskets consist of both repeat items and explore items. Some stat...
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In this article, we extend our previous work (Applicable Analysis, 2024, pp. 1-25) on the steepest descent method for uncertain multiobjective optimization problems. While that study established local convergence, it ...
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Population co-evolution strategies are widely used to handle constrained multi-objective optimization problems (CMOPs). However, existing coevolutionary algorithms oversimplify population collaboration and are rigid i...
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The paper deals with the implicit programming approach to a class of Mathematical Programs with Equilibrium Constraints (MPECs) and bilevel programs in the case when the corresponding reduced problems are solved using...
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