Xianke He¹, Yuansheng Li¹, Hengjie Liao¹, Zhehao Jiang¹, Meixue Shi¹, Zhe Hu^2,3, Yaowei Huang^2,3, Keliu Wu^2,3,*

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.1, 2026, DOI:10.32604/fdmp.2026.074990 - 06 February 2026

Abstract Weak water-drive offshore reservoirs with complex pore architecture and strong permeability heterogeneity present major challenges, including rapid depletion of formation energy, low waterflood efficiency, and significant lateral and vertical variability in crude oil properties, all of which contribute to limited recovery. To support more effective field development, alternative strategies and a deeper understanding of pore-scale flow behavior are urgently needed. In this work, CT imaging and digital image processing were used to construct a digital rock model representative of the target reservoir. A pore-scale flow model was then developed, and the Volume of Fluid (VOF)… More > Graphic Abstract

Cyreneo Dofitas¹, Yong-Woon Kim², Yung-Cheol Byun^3,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.069374 - 09 December 2025

Abstract Recent advances in deep learning have significantly improved flood detection and segmentation from aerial and satellite imagery. However, conventional convolutional neural networks (CNNs) often struggle in complex flood scenarios involving reflections, occlusions, or indistinct boundaries due to limited contextual modeling. To address these challenges, we propose a hybrid flood segmentation framework that integrates a Vision Transformer (ViT) encoder with a U-Net decoder, enhanced by a novel Flood-Aware Refinement Block (FARB). The FARB module improves boundary delineation and suppresses noise by combining residual smoothing with spatial-channel attention mechanisms. We evaluate our model on a UAV-acquired flood More >

Nader Karmous¹, Wadii Jlassi¹, Mohamed Ould-Elhassen Aoueileyine¹, Imen Filali^2,*, Ridha Bouallegue¹

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4363-4393, 2025, DOI:10.32604/cmes.2025.074178 - 23 December 2025

Abstract This paper introduces a robust Distributed Denial-of-Service attack detection framework tailored for Software-Defined Networking based Internet of Things environments, built upon a novel, synthetic multi-vector dataset generated in a Mininet-Ryu testbed using real-time flow-based labeling. The proposed model is based on the XGBoost algorithm, optimized with Principal Component Analysis for dimensionality reduction, utilizing lightweight flow-level features extracted from OpenFlow statistics to classify attacks across critical IoT protocols including TCP, UDP, HTTP, MQTT, and CoAP. The model employs lightweight flow-level features extracted from OpenFlow statistics to ensure low computational overhead and fast processing. Performance was rigorously… More >

Ihsan Qadir¹, Usama Naeem², Ahmed Nouman³, Aamir Raza⁴, Jun Wu^1,*

Revue Internationale de Géomatique, Vol.34, pp. 831-853, 2025, DOI:10.32604/rig.2025.069020 - 10 November 2025

Abstract The Jhelum River Basin in Pakistan has experienced recurrent and severe flooding over the past several decades, leading to substantial economic losses, infrastructure damage, and socio-environmental disruptions. This study uses multi-temporal satellite remote sensing data with historical hydrological records to map the spatial and temporal dynamics of major flood events occurring between 1988 and 2019. By utilizing satellite imagery from Landsat 5, Landsat 8, and Sentinel-2, key flood events were analyzed through the application of water indices such as the Normalized Difference Water Index (NDWI) and the Modified NDWI (MNDWI) to delineate flood extents. Historical… More >

Chih-Yu Liu^1,2, Cheng-Yu Ku^1,2,*, Ming-Han Tsai¹, Jia-Yi You³

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2969-2990, 2025, DOI:10.32604/cmes.2025.070663 - 30 September 2025

Abstract Amid growing typhoon risks driven by climate change with projected shifts in precipitation intensity and temperature patterns, Taiwan faces increasing challenges in flood risk. In response, this study proposes a geographic information system (GIS)-based artificial intelligence (AI) model to assess flood susceptibility in Keelung City, integrating geospatial and hydrometeorological data collected during Typhoon Krathon (2024). The model employs the random forest (RF) algorithm, using seven environmental variables excluding average elevation, slope, topographic wetness index (TWI), frequency of cumulative rainfall threshold exceedance, normalized difference vegetation index (NDVI), flow accumulation, and drainage density, with the number of… More >

Xiao Li^*, Junxiang Liu, Weinan Fan, Shiying Xu

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.1, pp. 1-2, 2025, DOI:10.32604/icces.2025.012284

Abstract Flooding is one of the most common types of natural hazards leading to wide-spread disturbances and damages to human communities and natural environment across the world. Flood forecasting is an effective means to provide timely hazard information to relevant government decision-makers and practitioners as well as those residents at risk, which plays an important role in flood risk reduction. A complete flood forecasting system normally includes at least two components, that is precipitation predictions and a hydrological or hydraulic model for flooding processes simulation. However, the current flood forecasting especially for urban floods face obvious… More >

Pengfei Wang^1,2,3, Jiwu Sun², Lu Lu^1,4, Hongchen Li¹, Hongzhe Liu², Cheng Xu², Yongqiang Liu^1,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2883-2903, 2025, DOI:10.32604/cmc.2025.065267 - 03 July 2025

Abstract Flooding and heavy rainfall under extreme weather conditions pose significant challenges to target detection algorithms. Traditional methods often struggle to address issues such as image blurring, dynamic noise interference, and variations in target scale. Conventional neural network (CNN)-based target detection approaches face notable limitations in such adverse weather scenarios, primarily due to the fixed geometric sampling structures that hinder adaptability to complex backgrounds and dynamically changing object appearances. To address these challenges, this paper proposes an optimized YOLOv9 model incorporating an improved deformable convolutional network (DCN) enhanced with a multi-scale dilated attention (MSDA) mechanism. Specifically,… More >

Chun Xiao^1,2,*

Energy Engineering, Vol.122, No.7, pp. 2719-2750, 2025, DOI:10.32604/ee.2025.063782 - 27 June 2025

Abstract In contemporary power systems, delving into the flexible regulation potential of demand-side resources is of paramount significance for the efficient operation of power grids. This research puts forward an innovative multivariate flexible load aggregation control approach that takes dynamic demand response into full consideration. In the initial stage, using generalized time-domain aggregation modelling for a wide array of heterogeneous flexible loads, including temperature-controlled loads, electric vehicles, and energy storage devices, a novel calculation method for their maximum adjustable capacities is devised. Distinct from conventional methods, this newly developed approach enables more precise and adaptable quantification… More >

Chinedu Nwaigwe^1,2,*, Abdon Atangana²

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.5, pp. 1239-1259, 2025, DOI:10.32604/fdmp.2025.059925 - 30 May 2025

Abstract In the early stages of oil exploration, oil is produced through processes such as well drilling. Later, hot water may be injected into the well to improve production. A key challenge is understanding how the temperature and velocity of the injected hot water affect the production rate. This is the focus of the current study. It proposes variable-viscosity mathematical models for heat and water saturation in a reservoir containing Bonny-light crude oil, with the aim of investigating the effects of water temperature and velocity on the recovery rate. First, two sets of experimental data are… More >

Tao Wang^1,2, Haiyang Yu^1,*, Jie Gao², Fei Wang², Xinlong Zhang^3,*, Hao Yang², Guirong Di², Pengrun Wang²

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.5, pp. 1191-1200, 2025, DOI:10.32604/fdmp.2025.058865 - 30 May 2025

Abstract Oilfields worldwide are increasingly grappling with challenges such as early water breakthrough and high water production, yet direct, targeted solutions remain elusive. In recent years, chemical flooding techniques designed for tertiary oil recovery have garnered significant attention, with microgel flooding emerging as a particularly prominent area of research. Despite its promise, the complex mechanisms underlying microgel flooding have been rarely investigated numerically. This study aims to address these gaps by characterizing the distribution of microgel concentration and viscosity within different pore structures. To enhance the accuracy of these characterizations, the viscosity of microgels is adjusted More >