Suchergebnisse - spatial algorithm complexity vulnerability

Analyzing and Discovering Spatial Algorithm Complexity Vulnerabilities in Recursion

Autoren: Ziqi Wang Debao Bu Weihan Tian et al.

Quelle: Applied Sciences, Vol 14, Iss 5, p 1855 (2024)

Schlagwörter: spatial algorithm complexity vulnerability, recursion structure, call chain, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2076-3417/14/5/1855; https://doaj.org/toc/2076-3417

Zugangs-URL: https://doaj.org/article/3656a30b4486482385949d777fa88d6e

Analyzing and Discovering Spatial Algorithm Complexity Vulnerabilities in Recursion.

Autoren: Wang, Ziqi Bu, Debao Tian, Weihan et al.

Quelle: Applied Sciences (2076-3417); Mar2024, Vol. 14 Issue 5, p1855, 19p

Schlagwörter: DENIAL of service attacks, ALGORITHMS

Low-Carbon Water Ecological POI Logistics Route Planning Based on Improved Water Network Space AGNES Clustering Model and Symmetrical Simulated Huffman Spatial Searching Tree Algorithm.

Autoren: Zhou, Xiao Jiang, Fan Liu, Wenbing et al.

Quelle: Symmetry (20738994); Nov2025, Vol. 17 Issue 11, p1894, 51p

Schlagwörter: AQUATIC ecology, ALGORITHMS, CARBON, MATHEMATICAL optimization, WATER clusters, EMISSIONS (Air pollution), SUSTAINABLE transportation, ROUTE choice

Groundwater Vulnerability Assessment in the Huangshui River Basin Under Representative Environmental Change.

Autoren: Ma, Tao Zhou, Kexin Wu, Jing et al.

Quelle: Water (20734441); Oct2025, Vol. 17 Issue 19, p2911, 26p

Schlagwörter: GROUNDWATER, GROUNDWATER quality, ENVIRONMENTAL management, ENVIRONMENTAL history, WATERSHEDS, GROUNDWATER analysis, GEOGRAPHIC spatial analysis, WATER quality

Geografische Kategorien: LOESS Plateau (China)

Forecasting the Spatio-Temporal Evolution of Groundwater Vulnerability: A Coupled Time-Series and Hydrogeological Modeling Approach.

Autoren: Yang, Yugang Zhao, Jingtao

Quelle: Water (20734441); Nov2025, Vol. 17 Issue 21, p3033, 30p

Schlagwörter: GROUNDWATER, FORECASTING, URBANIZATION, COMPUTER simulation, HYDROGEOLOGY, SPATIOTEMPORAL processes, SENSITIVITY analysis

Geografische Kategorien: BEIJING (China)

Investigating socio-ecological vulnerability to climate change via remote sensing and a data-driven ranking algorithm

Autoren: Harrison Odion Ikhumhen Qinhua Fang Shanlong Lu et al.

Quelle: Journal of Environmental Management. 347:119254

Schlagwörter: Spatial Analysis, 13. Climate action, Climate Change, 11. Sustainability, Remote Sensing Technology, 14. Life underwater, Algorithms, Ecosystem

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/37806274

Management and prediction of river flood utilizing optimization approach of artificial intelligence evolutionary algorithms

Autoren: Rana Muhammad Adnan Ikram Mo Wang Hossein Moayedi et al.

Quelle: Scientific Reports, Vol 15, Iss 1, Pp 1-31 (2025)

Schlagwörter: Sustainable development, Optimized neural network, Flood susceptibility map, Spatial statistical analysis, Novel and technical processes, Medicine, Science

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2045-2322

Zugangs-URL: https://doaj.org/article/4cf7a2ca78d146b985d6d42182019f6b

Spatial and Economic-Based Clustering of Greek Irrigation Water Organizations: A Data-Driven Framework for Sustainable Water Pricing and Policy Reform.

Autoren: Tsagkoudis, Dimitrios Zafeiriou, Eleni Spinthiropoulos, Konstantinos

Quelle: Water (20734441); Aug2025, Vol. 17 Issue 15, p2242, 24p

Schlagwörter: WATER management, GEOGRAPHIC spatial analysis, SUSTAINABILITY, MACHINE learning, IRRIGATION, REGULATORY reform, FINANCIAL management, CLUSTERING algorithms

Geografische Kategorien: GREECE

Large‐Scale Drought Forecasting in the U.S. Southern Plains Through a Hybrid Cluster‐Based Wavelet‐Machine Learning Approach.

Autoren: Lee, SangHyun Danandeh Mehr, Ali Moriasi, Daniel et al.

Quelle: Water Resources Research; Nov2025, Vol. 61 Issue 11, p1-23, 23p

Schlagwörter: DROUGHT forecasting, MACHINE learning, WAVELET transforms, CLIMATE change adaptation, HYDROLOGIC models, PLAINS, SPATIAL variation, EVAPOTRANSPIRATION

Urban flood hazard assessment using FLA-optimized boost algorithms in Ankara, Türkiye.

Autoren: Gul, Enes

Quelle: Applied Water Science; Apr2025, Vol. 15 Issue 4, p1-21, 21p

Schlagwörter: FLOOD risk, BOOSTING algorithms, CITIES & towns, ENVIRONMENTAL disasters, MACHINE learning, OPTIMIZATION algorithms

Geografische Kategorien: ANKARA (Turkey)

Noncontact full-field spatial-temporal deformation measurement of rockfall prevention structure with deep learning-based optic flow algorithm under complex Environments: Full-scale experiments and field tests.

Autoren: Tong, Jiahui Yang, Xiaoyu Guo, Qian et al.

Quelle: Mechanical Systems & Signal Processing. Jul2025, Vol. 234, pN.PAG-N.PAG. 1p.

Schlagwörter: *EMERGENCY management, *OPTICAL flow, *ENERGY levels (Quantum mechanics), *COMPUTER vision, *MOTION analysis

Enhancing spatial prediction of groundwater-prone areas through optimization of a boosting algorithm with bio-inspired metaheuristic algorithms.

Autoren: Razavi-Termeh, Seyed Vahid Sadeghi-Niaraki, Abolghasem Abba, Sani I. et al.

Quelle: Applied Water Science; Nov2024, Vol. 14 Issue 11, p1-25, 25p

Schlagwörter: METAHEURISTIC algorithms, STANDARD deviations, BOOSTING algorithms, RECEIVER operating characteristic curves, ARTIFICIAL intelligence

Topological and spatial heterogeneity of gut microbiota co-abundance networks in pigs revealed by using large-scale samples.

Autoren: Wu, Lin Liu, Yuxin Chen, Congying et al.

Quelle: Frontiers in Microbiology; 2025, p1-15, 15p

Schlagwörter: GUT microbiota, INTESTINES, MICROBIAL communities, SWINE, SPATIAL variation, PREVOTELLA

HotFuzz: Discovering Temporal and Spatial Denial-of-Service Vulnerabilities Through Guided Micro-Fuzzing

Autoren: William Blair Andrea Mambretti Sajjad Arshad et al.

Quelle: ACM Transactions on Privacy and Security. 25:1-35

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Machine Learning Models of the Geospatial Distribution of Groundwater Quality: A Systematic Review.

Autoren: Mehrabi, Mohammad Polya, David A. Han, Yang

Quelle: Water (20734441); Oct2025, Vol. 17 Issue 19, p2861, 48p

Schlagwörter: MACHINE learning, GROUNDWATER quality, STATISTICAL reliability, PREDICTION models, SPATIAL variation, ENVIRONMENTAL risk assessment, POLLUTANTS

Enhancing Landslide Vulnerability Mapping Through Automated Fuzzy Logic Algorithm-Based Methodology.

Autoren: Bruzón, Adrián G. Arrogante-Funes, Patricia Álvarez-Ripado, Ariadna et al.

Quelle: Geotechnical & Geological Engineering; Jul2024, Vol. 42 Issue 5, p3009-3025, 17p

Schlagwörter: LANDSLIDES, FUZZY logic, GEOGRAPHIC information systems, REMOTE sensing, MACHINE learning, HEURISTIC

Geografische Kategorien: GUERRERO (Mexico : State)

Identifying (less) vulnerable forest structures to storms in different conifer-dominated European forest landscapes by simple spaceborne proxies.

Autoren: Garamszegi, Balázs Jung, Christopher Schindler, Dirk

Quelle: Canadian Journal of Forest Research. 10/30/2025, Vol. 55, p1-13. 13p.

Schlagwörter: *REMOTE sensing, *WIND damage, *RANDOM forest algorithms, *FORESTS & forestry, *WIND pressure, *REMOTE-sensing images, *FOREST ecology, *LANDSCAPE assessment

Geografische Kategorien: EUROPE, ALPS

An Ensemble Machine Learning Approach for High-Resolution Estimation of Groundwater Storage Anomalies.

Autoren: Yuan, Yanbin Shen, Dongyang Cao, Yang et al.

Quelle: Water (20734441); May2025, Vol. 17 Issue 10, p1445, 32p

Schlagwörter: MACHINE learning, STANDARD deviations, GROUNDWATER management, SPATIAL resolution, GROUNDWATER

Predictive Analytics and Soft Computing Models for Groundwater Vulnerability Assessment in High-Salinity Regions of the Southeastern Anatolia Project (GAP), Türkiye.

Autoren: Karabulut, Abdullah Izzeddin Nacar, Sinan Yesilnacar, Mehmet Irfan et al.

Quelle: Water (20734441); Jul2025, Vol. 17 Issue 13, p1855, 29p

Schlagwörter: GROUNDWATER, SALINITY, PREDICTION models, SOFT computing, AGRICULTURE, ARTIFICIAL neural networks, ENVIRONMENTAL risk assessment

Geografische Kategorien: TURKEY

Spatial Modeling of Equine Herpesviruses 1 (EHVs‐1) Risks in Kazakhstan Using 2017–2024 Surveillance Data.

Autoren: Mukhanbetkaliyev, Yersyn Yessembekova, Gulzhan Mukhanbetkaliyeva, Aizada et al.

Quelle: Transboundary & Emerging Diseases. 8/21/2025, Vol. 2025, p1-9. 9p.