Integrating geospatial, remote sensing, and machine learning for climate-induced forest fire susceptibility mapping in Similipal Tiger Reserve, India

Forest Ecology and Management, Journal Year: 2024, Volume and Issue: 555, P. 121729 - 121729

Published: Jan. 31, 2024

Accurately assessing forest fire susceptibility (FFS) in the Similipal Tiger Reserve (STR) is essential for biodiversity conservation, climate change mitigation, and community safety. Most existing studies have primarily focused on climatic topographical factors, while this research expands scope by employing a synergistic approach that integrates geographical information systems (GIS), remote sensing (RS), machine learning (ML) methodologies identifying fire-prone areas STR their vulnerability to change. To achieve this, study employed comprehensive dataset of forty-four influencing including topographic, climate-hydrologic, health, vegetation indices, radar features, anthropogenic interference, into ten ML models: neural net (nnet), AdaBag, Extreme Gradient Boosting (XGBTree), Machine (GBM), Random Forest (RF), its hybrid variants with differential evolution algorithm (RF-DEA), Gravitational Based Search (RF-GBS), Grey Wolf Optimization (RF-GWO), Particle Swarm (RF-PSO), genetic (RF-GA). The revealed high FFS both northern southern portions area, nnet RF-PSO models demonstrating percentages 12.44% 12.89%, respectively. Conversely, very low zones consistently displayed scores approximately 23.41% 18.57% models. robust mapping methodology was validated impressive AUROC (>0.88) kappa coefficient (>0.62) across all validation metrics. Future (ssp245 ssp585, 2022–2100) indicated along edges STR, central zone categorized from susceptibility. Boruta analysis identified actual evapotranspiration (AET) relative humidity as key factors ignition. SHAP evaluation reinforced influence these FFS, also highlighting significant role distance road, settlement, dNBR, slope, prediction accuracy. These results emphasize critical importance proposed provide invaluable insights firefighting teams, management, planning, qualification strategies address future sustainability.

Language: Английский

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Prioritizing sub-watersheds for soil erosion using geospatial techniques based on morphometric and hypsometric analysis: a case study of the Indian Wyra River basin

Applied Water Science, Journal Year: 2023, Volume and Issue: 13(7)

Published: June 26, 2023

Abstract The hydrological availability and scarcity of water can be affected by geomorphological processes occurring within a watershed. Hence, it is crucial to perform quantitative evaluation the watershed’s geometry determine impact such on its hydrology. Geographic information systems (GIS) remote sensing (RS) techniques have become increasingly significant because they enable decision-makers strategists make accurate efficient decisions. To prioritize sub-watersheds Wyra watershed, this research employs two methods: morphometric analysis hypsometric analysis. watershed was divided into eleven (SWs). prioritization in involved assessing several parameters, as relief, linear, areal features, for each sub-watershed. Furthermore, importance determined computing integral (HI) values using elevation–relief ratio method. final based through integration principal component (PCA) weighted sum approach (WSA). SW2 SW9 had higher priorities analysis, whereas SW6, SW7, SW10 obtained SW4 most common SW that shares same priority. vulnerable are those with highest priority, therefore, programmes soil conservation should pay more attention them. conclusions study may prove useful various stakeholders initiatives related development management.

Language: Английский

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Efficiency evaluation of low impact development practices on urban flood risk

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 356, P. 120467 - 120467

Published: March 13, 2024

Language: Английский

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Development of an automated method for flood inundation monitoring, flood hazard, and soil erosion susceptibility assessment using machine learning and AHP–MCE techniques

Geoenvironmental Disasters, Journal Year: 2024, Volume and Issue: 11(1)

Published: March 26, 2024

Abstract Background Operational large-scale flood monitoring using publicly available satellite data is possible with the advent of Sentinel-1 microwave data, which enables near-real-time (at 6-day intervals) mapping day and night, even in cloudy monsoon seasons. Automated inundation area identification involves advanced geospatial processing platforms, such as Google Earth Engine robust methodology (Otsu’s algorithm). Objectives The current study employs for extent machine learning (ML) algorithms Assam State, India. We generated a hazard soil erosion susceptibility map by combining multi-source on weather conditions terrain characteristics. Random Forest (RF), Classification Regression Tool (CART), Support Vector Machine (SVM) ML were applied to generate map. Furthermore, we employed multicriteria evaluation (MCE) analytical hierarchical process (AHP) mapping. Summary highest prediction accuracy was observed RF model (overall [OA] > 82%), followed SVM (OA 82%) CART 81%). Over 26% indicated high hazard-prone areas, approximately 60% showed severe potential due flooding. automated platform an essential resource emergency responders decision-makers, it helps guide relief activities identifying suitable regions appropriate logistic route planning improving timeliness response efforts. Periodic maps will help long-term policymaking, management, biodiversity conservation, land degradation, sustainable agriculture interventions, crop insurance, climate resilience studies.

Language: Английский

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Integrating machine learning and geospatial data analysis for comprehensive flood hazard assessment

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(35), P. 48497 - 48522

Published: July 20, 2024

Flooding is a major natural hazard worldwide, causing catastrophic damage to communities and infrastructure. Due climate change exacerbating extreme weather events robust flood modeling crucial support disaster resilience adaptation. This study uses multi-sourced geospatial datasets develop an advanced machine learning framework for assessment in the Arambag region of West Bengal, India. The inventory was constructed through Sentinel-1 SAR analysis global databases. Fifteen conditioning factors related topography, land cover, soil, rainfall, proximity, demographics were incorporated. Rigorous training testing diverse models, including RF, AdaBoost, rFerns, XGB, DeepBoost, GBM, SDA, BAM, monmlp, MARS algorithms, undertaken categorical mapping. Model optimization achieved statistical feature selection techniques. Accuracy metrics model interpretability methods like SHAP Boruta implemented evaluate predictive performance. According area under receiver operating characteristic curve (AUC), prediction accuracy models performed around > 80%. RF achieves AUC 0.847 at resampling factor 5, indicating strong discriminative AdaBoost also consistently exhibits good ability, with values 0.839 10. indicated precipitation elevation as most significantly contributing area. Most pointed out southern portions highly susceptible areas. On average, from 17.2 18.6% hazards. In analysis, various nature-inspired algorithms identified selected input parameters assessment, i.e., elevation, precipitation, distance rivers, TWI, geomorphology, lithology, TRI, slope, soil type, curvature, NDVI, roads, gMIS. As per analyses, it found that rivers play roles decision-making process assessment. results majority building footprints (15.27%) are high very risk, followed by those low risk (43.80%), (24.30%), moderate (16.63%). Similarly, cropland affected flooding this categorized into five classes: (16.85%), (17.28%), (16.07%), (16.51%), (33.29%). However, interdisciplinary contributes towards hydraulic hydrological management.

Language: Английский

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Integrated GIS and analytic hierarchy process for flood risk assessment in the Dades Wadi watershed (Central High Atlas, Morocco)

Results in Earth Sciences, Journal Year: 2024, Volume and Issue: 2, P. 100019 - 100019

Published: March 18, 2024

Flood risk assessment is crucial for delineating flood hazard zones and formulating effective mitigation strategies. Employing a multi-criteria decision support system, this study focused on assessing Risk Index (FHI) at the Dades Wadi watershed scale. Seven main flood-causing criteria were broadly selected, namely flow accumulation, distance from hydrographic network, drainage network density, land use, slope, rainfall, permeability. The relative importance of each criterion prioritized as per their contribution toward risk, which employed blend Analytical Hierarchy Process (AHP) Geographic Information System (GIS)/Remote Sensing (RS) techniques. significance was determined based to hazard, established through an AHP pair-wise comparison matrix. efficacy model performed with consistency ratio 0.08, indicated that weight confirmed. Among criteria, hydrologic accumulation factor identified most influential (weight: 3.11), while permeability exhibited least prominence 0.58). Approximately 40.36% total area, equivalent around 1319, 89 km2, concentrated within very high flood-risk situated near rivers. In contrast, area approximately 399,943 km2 (56.33%) low zone. validation FHI map encompassed application Receiver Operating Characteristic Curve (ROC) technique, revealing Area Under (AUC) 85%.

Language: Английский

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Hybridizing genetic random forest and self-attention based CNN-LSTM algorithms for landslide susceptibility mapping in Darjiling and Kurseong, India

Quaternary Science Advances, Journal Year: 2024, Volume and Issue: 14, P. 100187 - 100187

Published: April 18, 2024

Landslides are a prevalent natural hazard in West Bengal, India, particularly Darjeeling and Kurseong, resulting substantial socio-economic physical consequences. This study aims to develop hybrid model, integrating Genetic-based Random Forest (GA-RF) novel Self-Attention based Convolutional Neural Network Long Short-term Memory (SA-CNN-LSTM), for accurate landslide susceptibility mapping (LSM) generate vulnerability-building map these regions. To achieve this, we compiled database with 1830 historical data points, incorporating inventory as the dependent variable 32 geo-environmental parameters from Remote Sensing (RS) Geographic Information Systems (GIS) layers independent variables. These include features like topography, climate, hydrology, soil properties, terrain distribution, radar features, anthropogenic influences. Our model exhibited superior performance an AUC of 0.92 RMSE 0.28, outperforming standalone SA-CNN-LSTM, GA-RF, RF, MLP, TreeBagger models. Notably, slope, Global Human Modification (gHM), Combined Polarization Index (CPI), distances streams roads, erosion emerged key LSM region. findings identified around 30% area having high very susceptibility, 20% moderate, 50% low low. The 244,552 building footprints indicated varying risk levels, significant proportion (27.74%) at risk. highlighted high-risk zones along roads northeastern southern areas. insights can enhance management guiding sustainable strategies future damage qualification.

Language: Английский

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Flood susceptibility mapping in the Yom River Basin, Thailand: stacking ensemble learning using multi-year flood inventory data

Geocarto International, Journal Year: 2025, Volume and Issue: 40(1)

Published: Feb. 10, 2025

Language: Английский

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Flood risk and shelter suitability mapping using geospatial technique for sustainable urban flood management: a case study in Palembang city, South Sumatera, Indonesia

Geology Ecology and Landscapes, Journal Year: 2023, Volume and Issue: unknown, P. 1 - 11

Published: April 24, 2023

The populous city of Palembang is one the most flood-prone cities in Indonesian region. After some decades, magnitude, duration, and frequency floods have increased. Thus, this study aimed to develop flood risk shelter suitability maps using Analytic Hierarchy Process (AHP) Geographical Information System (GIS) integration. Several flood-related factors that used such as elevation, population, slope, land cover, distance from a river, drainage density, road, settlement, soil type. Results found map area was divided into three classes; 30.3% at high risk, while 60.5% moderate 9.2% low risk. Moreover, assessments revealed approximately 4.1% shelters were highly suitable, 19.4% moderately lowly 16.1% very suitable. highest areas predominantly on northwest north sides which higher elevation (ranging 13–41 m) farther river. They could be assumed good choices for shelters.

Language: Английский

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Spatial implementation of frequency ratio, statistical index and index of entropy models for landslide susceptibility mapping in Al-Balouta river basin, Tartous Governorate, Syria

Geoscience Letters, Journal Year: 2022, Volume and Issue: 9(1)

Published: Dec. 26, 2022

Abstract Landslide vulnerability prediction maps are among the most important tools for managing natural hazards associated with slope stability in river basins that affect ecosystems, properties, infrastructure and society. events hazardous patterns of instability coastal mountains Syria. Thus, main goals this research to evaluate performance three different statistical outputs: Frequency Ratio (FR), Statistical Index (SI) Entropy (IoE) therefore map landslide susceptibility region To end, we identified a total 446 locations events, based on preliminary inventory derived from fieldwork high-resolution imagery surveys. In regard, 13 geo-environmental factors have high influence landslides were selected mapping. The results indicated FR method outperformed SI IoE models AUC 0.824 better adaptability, followed by 0.791. According SCAI values, although model achieved best reliability, other two also showed good capability determining susceptibility. result FR-based modelling 18.51 19.98% study area fall under very susceptible categories, respectively. generated method, about 36% is classified as having or sensitivity. whereas 14.18 25.62% “very susceptible” “high susceptible,” relative importance analysis demonstrated aspects, lithology proximity roads effectively motivated acceleration material influential both models. On hand, faults roads, along factor, influences formation events. As result, bivariate models-based mapping provided reliable systematic approach guide long-term strategic planning procedures area.

Language: Английский

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