An Integrated Artificial Intelligence of Things Environment for River Flood Prevention

Sensors, Год журнала: 2022, Номер 22(23), С. 9485 - 9485

Опубликована: Дек. 5, 2022

River floods are listed among the natural disasters that can directly influence different aspects of life, ranging from human lives, to economy, infrastructure, agriculture, etc. Organizations investing heavily in research find more efficient approaches prevent them. The Artificial Intelligence Things (AIoT) is a recent concept combines best both and Internet Things, has already demonstrated its capabilities fields. In this paper, we introduce an AIoT architecture where river flood sensors, each region, transmit their data via LoRaWAN closest local broadcast center. latter will relay collected 4G/5G centralized cloud server analyze data, predict status rivers countrywide using approach, thus, help eventual floods. This approach proven efficiency at every level. On one hand, LoRaWAN-based communication between sensor nodes centers provided lower energy consumption wider range. other Intelligence-based analysis better predictions.

Язык: Английский

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Multi‐hazard assessment using machine learning and remote sensing in the North Central region of Vietnam

Transactions in GIS, Год журнала: 2023, Номер 27(5), С. 1614 - 1640

Опубликована: Июль 23, 2023

Abstract Natural hazards constitute a diverse category and are unevenly distributed in time space. This hinders predictive efforts, leading to significant impacts on human life economies. Multi‐hazard prediction is vital for any natural hazard risk management plan. The main objective of this study was the development multi‐hazard susceptibility mapping framework, by combining two hazards—flooding landslides—in North Central region Vietnam. accomplished using support vector machines, random forest, AdaBoost. input data consisted 4591 flood points, 1315 landslide 13 conditioning factors, split into training (70%), testing (30%) datasets. accuracy models' predictions evaluated statistical indices root mean square error, area under curve (AUC), absolute coefficient determination. All proposed models were good at predicting susceptibility, with AUC values over 0.95. Among them, value machine model 0.98 0.99 flood, respectively. For forest model, these 0.98, AdaBoost, they 0.99. maps built maps. results showed that approximately 60% affected landslides, 30% 8% both hazards. These illustrate how one regions Vietnam most severely hazards, particularly flooding, landslides. adapt evaluate different scales, although expert intervention also required, optimize algorithms. can provide valuable point reference decision makers sustainable land‐use planning infrastructure faced multiple prevent reduce more effectively frequency floods landslides their damage property.

Язык: Английский

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Rapid flood monitoring based on Otsu threshold and damage assessment using multisatellite data

Опубликована: Янв. 3, 2025

Climate change causes weather pattern changes and heavy rainfall more frequently which flooding in many areas. Due to its limitations on cloud cover, optical satellites are not capable of mapping floods. SAR such as Sentinel-I is a high-resolution satellite used for detecting flood inundation because capability penetrate clouds depend the weather. The Otsu thresholding algorithm was applied identify an optimal threshold each preprocessed image separate water from non-water pixels producing best non-flood areas based backscatter intensity. results indicate shows that application VH polarization with method can map floods well Central Java events. For impact flooding, ESA WorldCover 10m resolution estimate amount affected cropland urban population density obtained Global Human Settlement Layer (GHSL) count people exposed flood. In March 15 at early stage event, 63,698 hectares were estimated affected, number 28,000. Based data, 44,080 1,119 by With rapid process disaster-affected areas, it will be easier decision-makers disaster management make proper decisions during time.

Язык: Английский

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Cyclone surge inundation susceptibility assessment in Bangladesh coast through geospatial techniques and machine learning algorithms: a comparative study between an island and a mangrove protected area

Frontiers in Earth Science, Год журнала: 2025, Номер 13

Опубликована: Март 18, 2025

Tropical cyclones, including surge inundation, are a joint event in the coastal regions of Bangladesh. The washes out life and property within very short period. Besides, most cases, area remains flooded for several days. Prediction inundation susceptibility due to cyclone is one key issues reducing vulnerability. Surge can be analyzed effectively through geospatial techniques various algorithms. Two techniques, such as GIS-based Analytical Hierarchy Process (AHP) multi-criteria analysis bivariate Frequency Ratio (FR) three algorithms, i.e., Artificial Neural Network (ANN), k -nearest neighbor (KNN) Random Forest (RF), were applied understand comparative level between an island, Sandwip protected by mangrove, Dacope on Bangladesh coast. A total ten criteria considered influential flooding, Elevation, Slope, Topographic Wetness Index, Drainage density, Distance from river sea, Wind flow distance, LULC, NDVI, Precipitation, Soil types. Among them, distance sea (16.34%) elevation (15.01%) found crucial analysis, according AHP expert’s opinions. Similarly, precipitation (9.88) (6.92) LULC (4.16) NDVI (4.33) highest PR values FR analysis. factor maps final ArcGIS 10.8. categorized into five classes, low, moderate, high, high. Very high was around boundary island upper portion upazila. (45.07%) (49.41%) observed KNN ANN, respectively. receiver operating characteristic (ROC) all acceptable prediction; however, possessed better consistent under curve (AUC) value than algorithms both study sites. Policymakers professionals plan manage disaster reduction activities based outcomes.

Язык: Английский

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Enhancing flood susceptibility mapping in Sana’a, Yemen with random forest and eXtreme gradient boosting algorithms

Geocarto International, Год журнала: 2025, Номер 40(1)

Опубликована: Март 31, 2025

Язык: Английский

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