Classification issues within ensemble-based simulation: application to surge floods forecasting

Abstract

Contemporary tasks of complex system simulation are often related to the issue of uncertainty management. It comes from the lack of information or knowledge about the simulated system as well as from restrictions of the model set being used. One of the powerful tools for the uncertainty management is ensemble-based simulation, which uses variation in input or output data, model parameters, or available versions of models to improve the simulation performance. Furthermore, the system of models for complex system simulation (especially in case of hiring ensemble-based approach) can be considered as a complex system. As a result, the identification of the complex model's structure and parameters provide additional sources of uncertainty to be managed. Within the presented work, we are developing a conceptual and technological approach to manage the ensemblebased simulation taking into account changing states of both simulated system and system of models within the ensemble-based approach. The states of these systems are considered as a subject of classification with consequent inference of better strategies for ensemble evolution over the simulation time and ensemble aggregation. Here the ensemble evolution enables implementation of dynamic reactive solutions that can automatically conform to the changing states of both systems. The ensemble aggregation can be considered within a scope of averaging (regression way) or selection (classification way, which complement the classification mentioned earlier) approach. The technological basis for such approach includes ensemble-based simulation techniques using domain-specific software combined within a composite application; data science approaches for analysis of available datasets (simulation data, observations, situation assessment, etc.); and machine learning algorithms for classes identification, ensemble management and knowledge acquisition. Within the work, a set of case studies is addressed to examine the opportunities provided by the developed approach: metocean events' forecasting simulation, urban traffic environment, multi-agent crowd simulation, etc.

Flood Forecasting by Using Machine Learning: A Study Leveraging Historic Climatic Records of Bangladesh

“…In the present research, a flood vulnerability map for Iran is produced by utilising the concept of the convolutional neural networks (CNN) method [42], one of the more current and effective techniques in enormous datasets. In their discussion of several case studies, Sergey et al [43] use the example of ensemble-based storm surge simulation for forecasting floods in St. Petersburg, Russia, to look at the opportunities presented by the established methodology. Mosavi et al's [31] main contribution is to show the current state of ML models for flood prediction and to provide insight into the most appropriate models.…”

Emerging Complexity in Distributed Intelligent Systems

“…One of the widespread implementation classes within this approach is ensemble learning, which implements an interaction between learning algorithms concerning multiple models and datasets to forecast an outcome by aggregating several predictions. Aggregation can be performed as a combination of predictor results for regression [ 52 ] or selecting a sole outcome for classification [ 53 ].…”

A Conceptual Approach to Complex Model Management with Generalized Modelling Patterns and Evolutionary Identification

“…1) for consideration of key processes and operations during modeling of the complex system. The framework may be considered as a generalization and extension of a framework [11,12] involved in the operation. Transitions between concepts and between layers are denoted with 1 → 2 and 1 → 2 respectively, e.g., operator Γ → Ξ reflects observation of quantitative parameters, operator Γ → Ξ stays for basic data assimilation.…”

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