Predicting water level fluctuations in glacier-fed lakes by ensembling individual models into a quad-meta model

Engineering Applications of Computational Fluid Mechanics, Journal Year: 2025, Volume and Issue: 19(1)

Published: Jan. 8, 2025

Predicting water levels in glacier-fed lakes is vital for resource management, flood forecasting, and ecological balance. This study examines the predictive capacity of multiple climate factors affecting Blue Moon Lake Valley, fed by Baishui River glacier on Yulong Snow Mountain. The introduces a novel quad-meta (QM) ensemble model that integrates outputs from four machine learning models – extreme gradient boosting (XGB), random forest (RF), (GBM), decision tree (DT) through meta-learning to improve prediction accuracy under complex environmental conditions. High-frequency depth data, recorded every five minutes using an RBR logger, alongside variables such as temperature, wind speed, humidity, evaporation, solar radiation, rainfall, were analyzed. Temperature was identified most significant factor influencing levels, with importance score 15.69, followed atmospheric pressure (14.08) radiation (12.89), which impacted surface conditions evaporation. Relative humidity (10.24) speed (8.71) influenced lake stability mixing. QM outperformed individual models, achieved RMSE values 0.003 m (climate data) 0.001 (water data), R2 0.994 0.999, respectively. In comparison, XGB GBM exhibited higher lower scores. RF struggled 0.008 0.962, while DT performed better (RMSE: 0.006 but remained inferior proposed model. These findings demonstrate robustness approach handling particularly where fall short. highlights potential enhanced systems, recommending future research directions incorporate deep long-term forecasting expand capabilities global scale.

Language: Английский

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Enhancing flood susceptibility mapping in Meghna River basin by introducing ensemble Naive Bayes with stacking algorithms

Geomatics Natural Hazards and Risk, Journal Year: 2025, Volume and Issue: 16(1)

Published: Feb. 13, 2025

Language: Английский

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Interconnections, trend analysis and forecasting of water-air temperature with water level dynamics in Blue Moon Lake Valley: A statistical and machine learning approach

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 379, P. 124829 - 124829

Published: March 8, 2025

Language: Английский

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Flood risk in mountainous settlements: A new framework based on an interpretable NSGA-II-GB from a point-area duality perspective

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 373, P. 123842 - 123842

Published: Jan. 1, 2025

Language: Английский

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Data Uncertainty of Flood Susceptibility Using Non-Flood Samples

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(3), P. 375 - 375

Published: Jan. 23, 2025

Flood susceptibility provides scientific support for flood prevention planning and infrastructure development by identifying assessing flood-prone areas. The uncertainty posed non-flood sample datasets remains a key challenge in mapping. Therefore, this study proposes novel sampling method points. A model is constructed using machine learning algorithm to examine the due point selection. influencing factors of are analyzed through interpretable models. Compared generated random with buffer method, dataset spatial range identified frequency ratio one-class vector achieves higher accuracy. This significantly improves simulation accuracy model, an increase 24% ENSEMBLE model. (2) In constructing optimal dataset, demonstrates than other methods, AUC 0.95. (3) northern southeastern regions Zijiang River Basin have extremely high susceptibility. Elevation drainage density as causing these areas, whereas southwestern region exhibits low elevation. (4) Elevation, slope, three most important affecting Lower values elevation slope correlate offers new approach reducing technical disaster mitigation basin.

Language: Английский

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Assessing Machine Learning Models on Temporal and Multi‐Sensor Data for Mapping Flooded Areas

Transactions in GIS, Journal Year: 2025, Volume and Issue: 29(2)

Published: March 17, 2025

ABSTRACT Natural disasters, particularly floods, are escalating in frequency and intensity, disproportionately impacting economically disadvantaged populations leading to substantial economic losses. This study leverages temporal multi‐sensor data from Synthetic Aperture Radar (SAR) multispectral sensors on Sentinel satellites evaluate a range of supervised semi‐supervised machine learning (ML) models. These models, combined with feature extraction selection techniques, effectively process large datasets map flood‐affected areas. Case studies Brazil Mozambique demonstrate the efficacy methods. The Support Vector Machine (SVM) an RBF kernel, despite achieving high kappa values, tended overestimate flood extents. In contrast, Classification Regression Trees (CART) Cluster Labeling (CL) methods exhibited superior performance both qualitatively quantitatively. Gaussian Mixture Model (GMM), however, showed sensitivity input was least effective among tested. analysis highlights critical need for careful ML models preprocessing techniques mapping, facilitating rapid, data‐driven decision‐making processes.

Language: Английский

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Flood impact on men’s mental health: evidence from flood-prone areas of Bangladesh

Frontiers in Public Health, Journal Year: 2025, Volume and Issue: 13

Published: April 3, 2025

Disasters can pose significant risks to mental health, often resulting in both temporary and long-lasting psychological distress. This study explores the impact of floods on health. A survey was conducted shortly after 2022 flash flood, which 452 male participants from Ajmiriganj Dharmapasha Upazilas Bangladesh were surveyed. Mental health assessed using DASS-21 instrument, we examined variables associated with issues. Descriptive statistics multiple linear regression analysis employed. Around 47% reported severe or extremely depression, 41% anxiety, 36% stress. Factors such as age, marital status, type home, occupation, flood safety rating, property loss during all found be depression. Anxiety linked safety, housing type, education level, status. Additionally, anxiety-related also issues more prevalent among older, married, illiterate living kacha (temporary) housing, well agricultural workers fishers low ratings. Psychological interventions disaster risk reduction strategies could help mitigate floods. The findings this have important implications for global management public

Language: Английский

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Geospatial Approach to Pluvial Flood-Risk and Vulnerability Assessment in Sunyani Municipality

Heliyon, Journal Year: 2024, Volume and Issue: 10(18), P. e38013 - e38013

Published: Sept. 1, 2024

Language: Английский

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Advancing flood susceptibility prediction: A comparative assessment and scalability analysis of machine learning algorithms via artificial intelligence in high‐risk regions of Pakistan

Journal of Flood Risk Management, Journal Year: 2024, Volume and Issue: 18(1)

Published: Nov. 24, 2024

Abstract Flood susceptibility mapping (FSM) is crucial for effective flood risk management, particularly in flood‐prone regions like Pakistan. This study addresses the need accurate and scalable FSM by systematically evaluating performance of 14 machine learning (ML) models high‐risk areas The novelty lies comprehensive comparison these use explainable artificial intelligence (XAI) techniques. We employed XAI to identify significant conditioning factors at both model training prediction stages. were assessed accuracy scalability, with specific focus on computational efficiency. Our findings indicate that LGBM XGBoost are top performers terms accuracy, also excelling achieving a time ~18 s compared LGBM's 22 random forest's 31 s. evaluation framework presented applicable other highlights superior accuracy‐focused applications, while optimal scenarios constraints. this can assist different scaling up analysis larger geographical region which could better decision‐making informed policy production management.

Language: Английский

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Analysis and visualization of spatio-temporal variations of ecological vulnerability in Pakistan using satellite observation datasets

Environmental and Sustainability Indicators, Journal Year: 2024, Volume and Issue: 23, P. 100425 - 100425

Published: June 20, 2024

Pakistan is the fifth most populous country in world. Its ecological environment facing numerous stresses such as climate change, rapid urbanization, natural disasters, and a decline air quality. Thus, scientific understanding of spatial temporal changes Pakistan's crucial for formulating an informed strategy regional sustainability. This study used Google Earth Engine platform Remote Sensing Ecological Index (RSEI) to investigate vulnerability three provinces 1990, 2000, 2010, 2020. Landsat 5 8 datasets are construct RSEI indicators Principal Component Analysis (PCA) adopted objectively compute past decades. The results indicated that (1) Punjab province exhibited slightly improved trend from 1990 2020 with overall dominance 'moderate' level all four years; (2) Sindh has declining 'poor' contributing 28.6% total area compared 1.04% 1990; (3) Balochistan shown resilience some extent during 1900-2010 vulnerability. However, observed between 2010 These research can provide support Punjab, Sindh, achieving sustainable development while conserving environment.

Language: Английский

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