Landslide Susceptibility Mapping and Interpretation in the Upper Minjiang River Basin DOI Creative Commons
Xin Wang, Shibiao Bai

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(20), P. 4947 - 4947

Published: Oct. 13, 2023

To enable the accurate assessment of landslide susceptibility in upper reaches Minjiang River Basin, this research intends to spatially compare maps obtained from unclassified landslides directly and spatial superposition different types map, explore interpretability using cartographic principles two methods map-making. This catalogs rainfall seismic selected nine background factors those affect occurrence through correlation analysis finally, including lithology, NDVI, elevation, slope, aspect, profile curve, curvature, land use, distance faults, assess susceptibility, respectively, by a WOE-RF coupling model. Then, an evaluation was conducted merging into dataset that does not distinguish landslides; comparison also made between landslides. Finally, confusion matrix ROC curve were used verify accuracy It found training set, testing entire data set based on model for predicting 0.9248, 0.8317, 0.9347, AUC area 1, 0.949, 0.955; prediction 0.9498, 0.9067, 0.8329, 0.981, 0.921; 0.9446, 0.9080, 0.8352, 0.9997, 0.9822, 0.9207. Both indicated is high. The southeast line Mount Xuebaoding Lixian County high prone area, maps, it extremely located at higher elevation than extracting zones both. results evaluating significantly different. As same factor, distribution areas occupied class according methods, which indicates necessity conducting relevant distinguishing types.

Language: Английский

Leveraging GIS-based AHP, remote sensing, and machine learning for susceptibility assessment of different flood types in peshawar, Pakistan DOI
Muhammad Tayyab, Muhammad Hussain, Jiquan Zhang

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 371, P. 123094 - 123094

Published: Nov. 2, 2024

Language: Английский

Citations

9
An ensemble modeling framework to elucidate the regulatory factors of chlorophyll-a concentrations in the Nanji wetland waters of Poyang Lake DOI Creative Commons

Lizhen Liu,

Qi Huang, Yongming Wu

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: 82, P. 102729 - 102729

Published: July 27, 2024

Chlorophyll-a (Chl a) is an important indicator of algal biomass frequently used to evaluate the severity cultural eutrophication. Identifying key covariates Chl a concentrations essential understand mechanisms that drive eutrophication and develop forecasting tools guide restoration process. In this study, we present novel ensemble modeling framework founded upon complementary features Random Forest (RF) Generalized Additive (GAMs). A series RF models are first developed forecast based on antecedent values multitude environmental predictors. GAMs then explore presence non-linearities in seasonal relationships between identified The optimal using 0–8 day time lag displayed high predictive skills with adjusted R2 consistently above 0.80. Analyses revealed modulating factors display significant seasonality. Dissolved oxygen (DO) turbidity were spring, while water level fluctuations predominantly regulated phytoplankton summer winter. occurrence blooms autumn associated threshold levels 0.06 1.50 mg/L for total phosphorus (TP) nitrogen (TN) concentrations, respectively. These results reveal potential introduced shed light regulatory as well establish real-time predictions Nanji wetland waters Poyang Lake.

Language: Английский

Citations

6
A machine learning-based approach for flash flood susceptibility mapping considering rainfall extremes in the northeast region of Bangladesh DOI
Md. Enayet Chowdhury, A. K. M. Saiful Islam, Rashed Uz Zzaman

et al.

Advances in Space Research, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 1, 2024

Language: Английский

Citations

4
Urban Flood Depth Prediction and Visualization Based on the XGBoost-SHAP Model DOI
Yuan Liu, Hongfa Wang,

Xinjian Guan

et al.

Water Resources Management, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 27, 2024

Language: Английский

Citations

4
Incorporating grey relational analysis into decomposition ensemble models for forecasting air passenger demand DOI
Yi‐Chung Hu, Geng Wu, Jung‐Fa Tsai

et al.

Grey Systems Theory and Application, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 18, 2025

Purpose Linear addition is commonly used to generate ensemble forecasts for decomposition models but traditionally treats individual modes with equal weights simplicity. Using Taiwan air passenger flow as an empirical case, this study examines whether incorporating weighting single-mode assessed by grey relational analysis into linear can improve the accuracy of forecast demand. Design/methodology/approach Data series are decomposed several single mode decomposition, and then different artificial intelligence methods applied individually these modes. By correlation between each forecasted original time learning, a genetic algorithm optimally synthesize obtain forecasts. Findings The results in terms level directional forecasting showed that proposed using improved demand horizons. Practical implications Accurately beneficial both policymakers practitioners aviation industry when making operational plans. Originality/value In light significance improving demand, research contributes development scheme

Language: Английский

Citations

0
A Systematic Review of Urban Flood Susceptibility Mapping: Remote Sensing, Machine Learning, and Other Modeling Approaches DOI Creative Commons
Tania Islam, Ethiopia Bisrat Zeleke,

Mahmud Afroz

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(3), P. 524 - 524

Published: Feb. 3, 2025

Climate change has led to an increase in global temperature and frequent intense precipitation, resulting a rise severe urban flooding worldwide. This growing threat is exacerbated by rapid urbanization, impervious surface expansion, overwhelmed drainage systems, particularly regions. As becomes more catastrophic causes significant environmental property damage, there urgent need understand address flood susceptibility mitigate future damage. review aims evaluate remote sensing datasets key parameters influencing provide comprehensive overview of the causative factors utilized mapping. also highlights evolution traditional, data-driven, big data, GISs (geographic information systems), machine learning approaches discusses advantages limitations different mapping approaches. By evaluating challenges associated with current practices, this paper offers insights into directions for improving management strategies. Understanding identifying foundation developing effective resilient practices will be beneficial mitigating

Language: Английский

Citations

0
Development of a Novel Multi-Model Ensemble Weighting Scheme for Improved Drought Assessment DOI

Mahrukh Yousaf,

A. Iqbal,

Sadia Qamar

et al.

Water Conservation Science and Engineering, Journal Year: 2025, Volume and Issue: 10(2)

Published: April 21, 2025

Language: Английский

Citations

0
Quantifying influence of rainfall events on outdoor thermal comfort in subtropical dense urban areas DOI Creative Commons

Chih-Hong Huang,

Ching-Hsun Wang,

Shih-Han Chen

et al.

Geocarto International, Journal Year: 2024, Volume and Issue: 39(1)

Published: Jan. 1, 2024

Existing research on outdoor thermal comfort in urban areas focuses meteorological factors such as temperature, relative humidity, wind speed, and radiation sunny or cloudy days. However, climate conditions before, during, after rainfall events can cause changes subtropical regions. Rainfall is an atmospheric condition with a large influence comfort, particularly abundant rain.

Language: Английский

Citations

1
Assessment of urban flood susceptibility based on a novel integrated machine learning method DOI
Haidong Yang, Ting Zou, Biyu Liu

et al.

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 197(1)

Published: Dec. 5, 2024

Language: Английский

Citations

1
Landslide Susceptibility Mapping and Interpretation in the Upper Minjiang River Basin DOI Creative Commons
Xin Wang, Shibiao Bai

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(20), P. 4947 - 4947

Published: Oct. 13, 2023

To enable the accurate assessment of landslide susceptibility in upper reaches Minjiang River Basin, this research intends to spatially compare maps obtained from unclassified landslides directly and spatial superposition different types map, explore interpretability using cartographic principles two methods map-making. This catalogs rainfall seismic selected nine background factors those affect occurrence through correlation analysis finally, including lithology, NDVI, elevation, slope, aspect, profile curve, curvature, land use, distance faults, assess susceptibility, respectively, by a WOE-RF coupling model. Then, an evaluation was conducted merging into dataset that does not distinguish landslides; comparison also made between landslides. Finally, confusion matrix ROC curve were used verify accuracy It found training set, testing entire data set based on model for predicting 0.9248, 0.8317, 0.9347, AUC area 1, 0.949, 0.955; prediction 0.9498, 0.9067, 0.8329, 0.981, 0.921; 0.9446, 0.9080, 0.8352, 0.9997, 0.9822, 0.9207. Both indicated is high. The southeast line Mount Xuebaoding Lixian County high prone area, maps, it extremely located at higher elevation than extracting zones both. results evaluating significantly different. As same factor, distribution areas occupied class according methods, which indicates necessity conducting relevant distinguishing types.

Language: Английский

Citations

3