Optimising Urban Flood Resilience

• URL: http://arxiv.org/abs/2604.18620v1
• Date: Fri, 17 Apr 2026 21:19:41 GMT
• Title: Optimising Urban Flood Resilience
• Abstract summary: Blue-Green Infrastructure (BGI) offers a sustainable solution for managing flood risk.<n>This study presents a novel multi-objective optimisation tool that couples a state-of-the-art hydrodynamic model with a bespoke evolutionary algorithm.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to the increasing frequency and severity of storm events, driven by the escalation of anthropogenic climate change and urban expansion, there is a requirement for increasingly efficient flood risk management strategies. While Blue-Green Infrastructure (BGI) offers a sustainable solution for managing flood risk, optimal implementation is challenging. To help overcome this challenge, this study presents a novel multi-objective optimisation tool that couples a state-of-the-art hydrodynamic model with a bespoke evolutionary algorithm. The use of a fully dynamic hydrodynamic model enables the tool to accurately evaluate the effectiveness of proposed BGI features with respect to property scale flood vulnerability and hazard analysis. This contrasts with alternative approaches which utilise simplified models, which can only reliably predict inundation extents, thus the proposed optimisation tool provides greater certainty regarding the optimality of the solutions. As a hydrodynamic simulation is required to evaluate each candidate solution, the bespoke evolutionary algorithm is specifically designed to minimise the number of simulations required, ensuring the tool is computationally practical. The effectiveness of the tool in this regard is validated via the derivation of exact convergence measures, for a tractable search space, and via comparisons with benchmark algorithms, for an intractable search space. Compared with traditional design practices, the proposed tool offers an automated approach capable of efficiently exploring a wide range of solutions, providing decision-makers with a set of optimal solutions from which they can make informed investment decisions. The presented methods provide a robust framework for optimising a variety of BGI features in complex urban environments.

Related papers

• Optimal Control of Microswimmers for Trajectory Tracking Using Bayesian Optimization [0.10705399532413612]
  Trajectory tracking for microswimmers remains a key challenge in microrobotics.<n>We formulate the trajectory tracking problem as an optimal control problem using a combination of B-spline parametrization and Bayesian optimization.
  arXiv  Detail & Related papers  (2026-02-10T09:14:32Z)
• TCPO: Thought-Centric Preference Optimization for Effective Embodied Decision-making [75.29820290660065]
  This paper proposes Thought-Centric Preference Optimization ( TCPO) for effective embodied decision-making.<n>It emphasizes the alignment of the model's intermediate reasoning process, mitigating the problem of model degradation.<n>Experiments in the ALFWorld environment demonstrate an average success rate of 26.67%, achieving a 6% improvement over RL4VLM.
  arXiv  Detail & Related papers  (2025-09-10T11:16:21Z)
• Preference Optimization for Combinatorial Optimization Problems [54.87466279363487]
  Reinforcement Learning (RL) has emerged as a powerful tool for neural optimization, enabling models learns that solve complex problems without requiring expert knowledge.<n>Despite significant progress, existing RL approaches face challenges such as diminishing reward signals and inefficient exploration in vast action spaces.<n>We propose Preference Optimization, a novel method that transforms quantitative reward signals into qualitative preference signals via statistical comparison modeling.
  arXiv  Detail & Related papers  (2025-05-13T16:47:00Z)
• A Hybrid Tabu Scatter Search Algorithm for Simulation-Based Optimization of Multi-Objective Runway Operations Scheduling [0.0]
  dissertation addresses the challenge of air traffic flow management by proposing a simulation-based optimization (SbO) approach for runway operations scheduling.<n>The goal is to optimize airport capacity utilization while minimizing delays, fuel consumption, and environmental impacts.<n>The proposed SbO framework integrates a discrete-event simulation model to handle runway conditions and a hybrid Tabu-Scatter Search algorithm to identify optimal solutions.
  arXiv  Detail & Related papers  (2025-02-08T14:42:05Z)
• A Survey on Inference Optimization Techniques for Mixture of Experts Models [50.40325411764262]
  Large-scale Mixture of Experts (MoE) models offer enhanced model capacity and computational efficiency through conditional computation.<n> deploying and running inference on these models presents significant challenges in computational resources, latency, and energy efficiency.<n>This survey analyzes optimization techniques for MoE models across the entire system stack.
  arXiv  Detail & Related papers  (2024-12-18T14:11:15Z)
• Deep Reinforcement Learning for Online Optimal Execution Strategies [49.1574468325115]
  This paper tackles the challenge of learning non-Markovian optimal execution strategies in dynamic financial markets. We introduce a novel actor-critic algorithm based on Deep Deterministic Policy Gradient (DDPG) We show that our algorithm successfully approximates the optimal execution strategy.
  arXiv  Detail & Related papers  (2024-10-17T12:38:08Z)
• An Improved Artificial Fish Swarm Algorithm for Solving the Problem of Investigation Path Planning [8.725702964289479]
  We propose a chaotic artificial fish swarm algorithm based on multiple population differential evolution (DE-CAFSA) We incorporate adaptive field of view and step size adjustments, replace random behavior with the 2-opt operation, and introduce chaos theory and sub-optimal solutions. Experimental results demonstrate that DE-CAFSA outperforms other algorithms on various public datasets of different sizes.
  arXiv  Detail & Related papers  (2023-10-20T09:35:51Z)
• DADO -- Low-Cost Query Strategies for Deep Active Design Optimization [1.6298921134113031]
  We present two selection strategies for self-optimization to reduce the computational cost in multi-objective design optimization problems. We evaluate our strategies on a large dataset from the domain of fluid dynamics and introduce two new evaluation metrics to determine the model's performance.
  arXiv  Detail & Related papers  (2023-07-10T13:01:27Z)
• Surrogate-Assisted Evolutionary Generative Design Of Breakwaters Using Deep Convolutional Networks [0.0]
  In the paper, a multi-objective evolutionary surrogate-assisted approach for the fast and effective generative design of coastal breakwaters is proposed. To approximate the computationally expensive objective functions, the deep convolutional neural network is used as a surrogate model. The experimental results confirm that the proposed approach allows obtaining more effective (less expensive with better protective properties) solutions than non-surrogate approaches for the same time.
  arXiv  Detail & Related papers  (2022-04-07T12:37:40Z)
• The multi-objective optimisation of breakwaters using evolutionary approach [62.997667081978825]
  In engineering practice, it is often necessary to increase the effectiveness of existing protective constructions for ports and coasts. In the paper, the multi-objective evolutionary approach for the breakwaters optimisation is proposed.
  arXiv  Detail & Related papers  (2020-04-06T21:48:01Z)
• Optimizing Wireless Systems Using Unsupervised and Reinforced-Unsupervised Deep Learning [96.01176486957226]
  Resource allocation and transceivers in wireless networks are usually designed by solving optimization problems. In this article, we introduce unsupervised and reinforced-unsupervised learning frameworks for solving both variable and functional optimization problems.
  arXiv  Detail & Related papers  (2020-01-03T11:01:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.