A 3D virtual geographic environment for flood representation towards risk communication

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 128, P. 103757 - 103757

Published: March 21, 2024

Risk communication seeks to develop a shared understanding of disaster among stakeholders, thereby amplifying public awareness and empowering them respond more effectively emergencies. However, existing studies have overemphasized specialized numerical modelling, making the professional output challenging understand use by non-research stakeholders. In this context, article proposes 3D virtual geographic environment for flood representation towards risk communication, which integrates parallel computation, in pipeline. Finally, section Rhine River Bonn, Germany, is selected experiment analysis. The experimental results show that proposed approach capable modelling within few hours, speedup ratio reached 6.45. intuitive scene with city models beneficial promoting particularly helpful participants without direct experience floods its spatiotemporal process. It also can be embedded Geospatial Infrastructure Management Ecosystem (GeoIME) cloud application intelligent systems.

Language: Английский

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Downscaled GRACE/GRACE-FO observations for spatial and temporal monitoring of groundwater storage variations at the local scale using machine learning

Groundwater for Sustainable Development, Journal Year: 2024, Volume and Issue: 25, P. 101100 - 101100

Published: Jan. 23, 2024

Language: Английский

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Innovative Drought Classification Matrix and Acceptable Time Period for Temporal Drought Evaluation

Water Resources Management, Journal Year: 2024, Volume and Issue: 38(8), P. 2811 - 2833

Published: March 1, 2024

Abstract Evaluating drought is paramount in water resources management and mitigation plans. Drought indices are essential tools this evaluation, which mainly depends on the time period of original datasets. Investigating effects periods critical for a comprehensive understanding evaluation drought. Also, It holds particular significance regions facing data availability challenges. The existing literature reveals gap assessment comparison analysis using conventional methods based only. This research proposes an innovative classification matrix to compare spatial temporal scenarios; proposed any procedure. Furthermore, it aims investigate differences between several scenarios statistical metrics (R 2 , CC, RMSE, HH, RB) determine acceptable/minimum period. application selection presented three different climates: Durham station United Kingdom, Florya Türkiye, Karapinar Türkiye. results show that able catch reference (RTP) scenario reasonably, with strong correlation negative relative bias. 10-year sufficient as short timescales, such meteorological Conversely, longer hydrological drought, 20-year demonstrates robust powerful framework comparison, making applicable various globally.

Language: Английский

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An appraisal of the local‐scale spatio‐temporal variations of drought based on the integrated GRACE/GRACE‐FO observations and fine‐resolution FLDAS model

Hydrological Processes, Journal Year: 2023, Volume and Issue: 37(11)

Published: Nov. 1, 2023

Abstract The gravity recovery and climate experiment (GRACE) observations have so far been utilized to detect trace the variations of hydrological extremes worldwide. However, applying coarse resolution GRACE estimates for local‐scale analysis remains a big challenge. In this study, new version fine (1 km) Famine early warning systems network Land Data Assimilation System (FLDAS) model data was integrated into machine learning along with evaluate subbasin‐scale water storage, drought. With correlation root mean square error (RMSE) its results, downscaling turned out be very successful in modelling finer TWSA. storage deficit (WSD) Water Storage Deficit Index (WSDI) were used determine episodes severity drought events. Accordingly, two severe droughts (January 2008 March 2009 September 2019 December 2020) discerned Kizilirmak Basin (KB) located Central Türkiye. characterization evaluated based on WSDI, scPDSI, model‐based indices soil moisture percentile (SMSP) groundwater (GWSP). results indicated discrepancies classes different indices. WSDI more correlated GWSP, suggesting high ability monitor as well.

Language: Английский

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Monitoring spatio-temporal variations of terrestrial water storage changes and their potential influencing factors in a humid subtropical climate region of Southeast China

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 634, P. 131095 - 131095

Published: March 28, 2024

Identifying the changes in terrestrial water storage is essential for a comprehensive understanding of regional hydrological mass balance under global climate change. This study used partial least square regression model to fill observation gaps between GRACE and GRACE-FO obtained complete series anomaly data from April 2002 December 2020 southeast China. We investigated variations anomalies region influencing factors. The revealed that (TWS) have been increasing region, with an average increase 0.33 cm/yr (p < 0.01). intra-annual variation showed positive March September negative other months. Terrestrial increased most regions (especially central northern parts), whereas they decreased southern parts. In terms components, soil moisture (SMS) contributes 58.3 % surface (SWS, especially reservoirs storage) 41.4 TWS. also found precipitation explain approximately 71.7 variation, remaining 28.3 %. These results are cycle developing strategies management Southeast

Language: Английский

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Meteorological drought monitoring in Kızılırmak Basin, Türkiye

Environmental Earth Sciences, Journal Year: 2024, Volume and Issue: 83(9)

Published: April 18, 2024

Abstract Drought, a major phenomenon impacting water resources, viability, sustainability, and the economy, has been one of most important hydrological concerns. In literature, it classified into four groups that are meteorological, agricultural, hydrological, socio-economic. Meteorological drought expresses precipitation deficits when they significantly below those recorded normal times. this study, using Standard Precipitation Index (SPI) mean monthly records 17 stations which have obtained from General Directorate Meteorology Türkiye, monitoring analysis conducted for Kızılırmak Basin, is second largest basin country source many provinces allowing time scales 1, 3, 6, 9, 12, 24 months considering cases “dry” (SPI ≤ − 1.5) “wet” ≥ 1.5). To detect possible trends in two categories Severe Extreme SED 1.5), Wet, SEW all scales, developed form innovative trend (ITA) performed by adding vertical lines. addition, traditional Mann–Kendall test applied to SPI series. The findings indicate dry occurrences tend outnumber wet across various scales. Analysis reveals significant majority results exhibit consistent (89%), with notable increase category (62.74%) decrease (60.78%). demonstrates 67% observed show decrease, while 33% an

Language: Английский

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Solving water scarcity challenges in arid regions: A novel approach employing human-based meta-heuristics and machine learning algorithm for groundwater potential mapping

Chemosphere, Journal Year: 2024, Volume and Issue: 363, P. 142859 - 142859

Published: July 20, 2024

Addressing water scarcity challenges in arid regions is a pressing concern and demands innovative solutions for accurate groundwater potential mapping (GPM). This study presents comprehensive evaluation of advanced modeling techniques to enhance the precision GPM. study, conducted Zayandeh Rood watershed, Iran, employed spatial database comprising 16 influential factors on data from 175 wells. introduced an approach GPM by enhancing Random Forest (RF) algorithm. enhancement involved integrating three metaheuristic algorithms inspired human behavior: ICA (Imperialist Competitive Algorithm), TLBO (Teaching-Learning-Based Optimization), SBO (Student Psychology Based Optimization). The process used 70% training 30% data. Data preprocessing was performed using multicollinearity test method frequency ratio (FR) technique refine dataset. Subsequently, generated four distinct models, demonstrating combined power machine learning human-inspired algorithms. performance models systematically assessed through extensive statistical analyses, including root mean squared error (RMSE), absolute (MAE), area under curve (AUC) receiver operating characteristic (ROC), Friedman tests, chi-squared Wilcoxon signed-rank tests. RF-ICA RF-SPBO emerged as frontrunners, displaying statistically comparable accuracy significantly outperforming RF-TLBO non-optimized RF model. results revealed exceptional RF-ICA, which exhibited commanding AUC score 0.865, underscoring its superiority discriminating between different classes. also displayed strong with 0.842, highlighting effectiveness inaccurate classification. model achieved values 0.813 0.810, respectively, indicating performance. outcomes this provide valuable insights policymakers, offering robust framework tackling precise reliable assessments.

Language: Английский

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A machine learning downscaling framework based on a physically constrained sliding window technique for improving resolution of global water storage anomaly

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 313, P. 114359 - 114359

Published: Aug. 10, 2024

Language: Английский

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Combining machine learning algorithms for bridging gaps in GRACE and GRACE Follow-On missions using ERA5-Land reanalysis

Science of Remote Sensing, Journal Year: 2025, Volume and Issue: unknown, P. 100198 - 100198

Published: Jan. 1, 2025

Language: Английский

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A new multivariate composite drought index considering the lag time and the cumulative effects of drought

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 132757 - 132757

Published: Jan. 1, 2025

Language: Английский

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