Remote Sensing and Geological Disasters

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.5	2011	16 Days	CHF 2400	Submit
GeoHazards geohazards	1.6	2.2	2020	20.1 Days	CHF 1400	Submit
Geosciences geosciences	2.1	5.1	2011	23.6 Days	CHF 1800	Submit
Land land	3.2	5.9	2012	17.5 Days	CHF 2600	Submit
Remote Sensing remotesensing	4.1	8.6	2009	24.3 Days	CHF 2700	Submit

26 pages, 11158 KB

Open AccessArticle

SBAS-InSAR Quantifies Groundwater–Urban Construction Evolution Impacts on Tianjin’s Land Subsidence

by Jia Xu, Yongqiang Cao, Jie Liu, Jiayu Hou, Wei Yan, Changrong Yi and Guodong Jia

Geosciences 2026, 16(2), 57; https://doi.org/10.3390/geosciences16020057 - 27 Jan 2026

Viewed by 444

Land subsidence constitutes a critical hazard to coastal megacities globally, amplifying flood risks and damaging infrastructure. Taking Tianjin—a major port city underlain by compressible sediments and affected by groundwater over-exploitation—as a case study, we address two key research gaps: the absence of a [...] Read more.

Land subsidence constitutes a critical hazard to coastal megacities globally, amplifying flood risks and damaging infrastructure. Taking Tianjin—a major port city underlain by compressible sediments and affected by groundwater over-exploitation—as a case study, we address two key research gaps: the absence of a quantitative framework coupling groundwater extraction with construction land expansion, and the inadequate separation of seasonal and long-term subsidence drivers. We developed an integrated remote-sensing-based approach: high-resolution subsidence time series (2016–2023) were derived via Small BAseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) using Sentinel-1 Synthetic Aperture Radar (SAR) imagery, validated against leveling measurements (R > 0.885, error < 20 mm). This subsidence dataset was fused with groundwater level records and annual construction land maps. Seasonal-Trend Decomposition using Loess (STL) isolated trend, seasonal, and residual components, which were input into a Random Forest (RF) model to quantify the relative contributions of subsidence drivers. Dynamic Time Warping (DTW) and Cross-Wavelet Transform (CWT) were further employed to characterize temporal patterns and lag effects between subsidence and its drivers. Our results reveal a distinct shifting subsidence pattern: “areal expansion but intensity weakening.” Groundwater control policies mitigated five historical subsidence funnels, reducing areas with severe subsidence from 72.36% to <5%, while the total subsiding area expanded by 1024.74 km², with new zones emerging (e.g., northern Dongli District). The RF model identified the long-term groundwater level trend as the dominant driver (59.5% contribution), followed by residual (23.3%) and seasonal (17.2%) components. Cross-spectral analysis confirmed high coherence between subsidence and long-term groundwater trends; the seasonal component exhibited a dominant resonance period of 12 months and a consistent subsidence response lag of 3–4 months. Construction impacts were conceptualized as a “load accumulation-soil compression-time lag” mechanism, with high-intensity engineering projects inducing significant local subsidence. This study provides a robust quantitative framework for disentangling the complex interactions between subsidence, groundwater, and urban expansion, offering critical insights for evidence-based hazard mitigation and sustainable urban planning in vulnerable coastal environments worldwide. Full article

(This article belongs to the Topic Remote Sensing and Geological Disasters)

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26 pages, 10994 KB

Open AccessArticle

Mass Movement Risk Assessment in the Loess Hilly Region of Northwest China Using a Weighted Information Theoretic Framework

by Zhiyong Hu, Jinkai Yan, Yongfeng Gong, Fangyuan Jiang, Guorui Wang, Hui Wang, Xiaofeng He, Shichang Gao and Zheng He

Geosciences 2025, 15(12), 468; https://doi.org/10.3390/geosciences15120468 - 10 Dec 2025

Viewed by 427

Abstract

Ground instability represents a major environmental hazard in the Loess Hilly region of Northwest China, threatening infrastructure and human safety. This study establishes an integrated information-theoretic framework for evaluating regional instability risk by coupling the information value model with analytic hierarchy process (AHP) [...] Read more.

Ground instability represents a major environmental hazard in the Loess Hilly region of Northwest China, threatening infrastructure and human safety. This study establishes an integrated information-theoretic framework for evaluating regional instability risk by coupling the information value model with analytic hierarchy process (AHP) weighting and subsequent hazard–exposure synthesis. Seven conditioning factors—geomorphic type, slope, aspect, lithology, distance to faults, river system, and NDVI—were analyzed to derive susceptibility, while rainfall, peak ground acceleration, and human engineering activity were incorporated as triggering elements of hazard. Exposure was quantified from population density and infrastructure exposure, and overall risk was defined as the product of hazard and exposure after normalization and calibration. Results indicate that hilly landforms, slopes of 10–20°, and NDVI values between 0.3 and 0.6 are the dominant controls on instability occurrence. Extreme-risk zones are concentrated in central Guyuan and northwest Shizuishan (0.16% of the study area), with high-risk zones covering 21.87%, moderate-risk zones covering 41.65%, and low-risk zones covering 6.32%. Model validation yields an AUC of 0.833 and a consistent increase in observed disaster-point density from low to extreme classes, confirming strong predictive reliability. These results demonstrate that the proposed calibrated framework provides a practical and transferable tool for ground-instability risk assessment and land-use planning in loess terrains. Full article

(This article belongs to the Topic Remote Sensing and Geological Disasters)

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28 pages, 31846 KB

Open AccessArticle

A Two-Dimensional InSAR-Based Framework for Landslide Identification and Movement Pattern Classification

by Xuhao Li, Qianyou Fan, Yufen Niu, Shuangcheng Zhang, Jinqi Zhao, Jinzhao Si, Zixuan Wang, Ziheng Ju and Zhong Lu

Remote Sens. 2025, 17(23), 3889; https://doi.org/10.3390/rs17233889 - 30 Nov 2025

Viewed by 717

Abstract

Frequent extreme climate events have intensified landslide hazards in mountainous regions, necessitating efficient identification and classification to understand movement mechanisms and mitigate risks. This study develops a novel, non-contact InSAR framework that seamlessly integrates three key steps—Identification, Inversion, and Classification—to address this challenge. [...] Read more.

Frequent extreme climate events have intensified landslide hazards in mountainous regions, necessitating efficient identification and classification to understand movement mechanisms and mitigate risks. This study develops a novel, non-contact InSAR framework that seamlessly integrates three key steps—Identification, Inversion, and Classification—to address this challenge. By applying this framework to ascending and descending Sentinel-1 data in the complex terrain of the Jishi Mountain region, we first introduce geometric distortion masking and a C-Index deformation consistency check, which enables the reliable identification of 530 active landslides, with 154 detected in both orbits. Second, we employ a local parallel flow model to invert the landslide movement geometry without relying on DEM-derived prior assumptions, successfully retrieving the two-dimensional (sliding and normal direction) deformation fields for all 154 consistent landslides. Finally, by synthesizing these 2D deformation patterns with geomorphological features, we achieve a systematic classification of movement types, categorizing them into retrogressive translational (31), progressive translational (66), rotational (19), composite (24), and earthflows (14). This integrated methodology provides a validated, transferable solution for deciphering landslide mechanisms and assessing risks in remote, complex mountainous areas. Full article

(This article belongs to the Topic Remote Sensing and Geological Disasters)

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28 pages, 99069 KB

Open AccessArticle

InSAR-Supported Spatiotemporal Evolution and Prediction of Reservoir Bank Landslide Deformation

by Chun Wang, Na Lin, Boyuan Li, Libing Tan, Yujie Xu, Kai Yang, Qingxin Ni, Kai Ding, Bin Wang, Nanjie Li and Ronghua Yang

Appl. Sci. 2025, 15(22), 12092; https://doi.org/10.3390/app152212092 - 14 Nov 2025

Viewed by 813

Abstract

Landslide disasters pose severe threats to mountainous regions, where accurate monitoring and scientific prediction are crucial for early warning and risk mitigation. This study addresses this challenge by focusing on the Outang Landslide, a representative large-scale bank slope in the Three Gorges Reservoir [...] Read more.

Landslide disasters pose severe threats to mountainous regions, where accurate monitoring and scientific prediction are crucial for early warning and risk mitigation. This study addresses this challenge by focusing on the Outang Landslide, a representative large-scale bank slope in the Three Gorges Reservoir area known for its significant deformation responses to rainfall and reservoir-level fluctuations. The landslide’s behavior, characterized by notable hysteresis and nonlinear trends, poses a significant challenge to accurate prediction. To address this, we derived high-precision time-series deformation data by applying atmosphere-corrected Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) to Sentinel-1A imagery, with validation from GNSS measurements. A systematic analysis was then conducted to uncover the correlation, hysteresis, and spatial heterogeneity between landslide deformation and key influencing variables (rainfall, water level, temperature). Furthermore, we proposed a Spatio-Temporal Enhanced Convolutional Neural Network (STE-CNN), which innovatively converts influencing variables into grayscale images to enhance spatial feature extraction, thereby improving prediction accuracy. The results indicate that: (1) From June 2022 to March 2024, the landslide showed an overall downward displacement trend, with maximum settlement and uplift rates of −49.34 mm/a and 21.77 mm/a, respectively; (2) Deformation exhibited significant correlation, hysteresis, and spatial variability with environmental factors, with dominant variables shifting across seasons—leading to intensified movement in flood seasons and relative stability in dry seasons; (3) The improved STE-CNN outperforms typical prediction models in forecasting landslide deformation.This study presents an integrated methodology that combines InSAR monitoring, multi-factor mechanistic analysis, and deep learning, offering a reliable solution for landslide early warning and risk management. Full article

(This article belongs to the Topic Remote Sensing and Geological Disasters)

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14 pages, 6978 KB

Open AccessArticle

Identification of Landslide Boundaries and Key Morphological Features Using UAV LiDAR Data: A Case Study in Surami, Georgia

by David Bakhsoliani, Archil Magalashvili and George Gaprindashvili

GeoHazards 2025, 6(4), 73; https://doi.org/10.3390/geohazards6040073 - 1 Nov 2025

Viewed by 996

Abstract

Identifying landslide boundaries and morphological features using traditional methods is labor-intensive, costly, and often limited—especially in areas altered by human activity or covered with dense vegetation. In such cases, modern remote sensing methods are considered a good alternative; however, their accuracy and reliability [...] Read more.

Identifying landslide boundaries and morphological features using traditional methods is labor-intensive, costly, and often limited—especially in areas altered by human activity or covered with dense vegetation. In such cases, modern remote sensing methods are considered a good alternative; however, their accuracy and reliability also depend on several factors. This study aims to identify landslide boundaries and morphological features using modern remote sensing techniques and to compare and validate the derived parameters with those obtained through traditional field methods. In this study, the remote sensing technology employed is a high-resolution digital elevation model (HRDEM) generated by a LiDAR sensor mounted on an unmanned aerial vehicle (UAV). Based on this dataset, various terrain parameters were analyzed, including slope, aspect, contour, curvature, hillshade, the topographic ruggedness index (TRI), the topographic position index (TPI), and the topographic wetness index (TWI). Individual analysis, composite analysis, and principal component analysis (PCA) of these parameters enabled the identification of the landslide boundaries and key morphological elements. This study was conducted on a landslide-prone slope in the Surami area of Georgia, which is characterized by extensive anthropogenic impact. The accuracy of the LiDAR-derived results was confirmed through field validation. This study demonstrates the effectiveness of UAV-LiDAR technology in areas affected by anthropogenic activity. Full article

(This article belongs to the Topic Remote Sensing and Geological Disasters)

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21 pages, 6437 KB

Open AccessArticle

A Missing Data Imputation Method for Waste Dump Landslide Deformation Monitoring Based on a Seq2Seq LSTM–Posterior Correction Model

by Tie Jin, Chen Cao, Ming Li, Kuanxing Zhu, Yaxuan Jing, Chenyang Wu, Xiguan An and Ji Bai

Remote Sens. 2025, 17(17), 2962; https://doi.org/10.3390/rs17172962 - 26 Aug 2025

Cited by 1 | Viewed by 1163

Abstract

Surface deformation monitoring is essential for controlling instability processes such as urban infrastructure deformation, mining-induced subsidence, and landslide deformation. However, missing data often disrupt the continuity of the various deformation time series and compromise the reliability of monitoring results. This issue is particularly [...] Read more.

Surface deformation monitoring is essential for controlling instability processes such as urban infrastructure deformation, mining-induced subsidence, and landslide deformation. However, missing data often disrupt the continuity of the various deformation time series and compromise the reliability of monitoring results. This issue is particularly critical in long-term landslide studies, where conventional missing data imputation methods often neglect the nonlinear characteristics of slope deformation and fail to account for external influences under complex environmental conditions. To address these limitations, this study proposes a deep learning-based imputation method that integrates multi-source monitoring data. A Seq2Seq LSTM (sequence-to-sequence long short-term memory) model is constructed to reconstruct missing deformation values, and a posterior correction module is integrated to optimize the preliminary outputs, thereby enhancing imputation accuracy. The proposed approach is validated using a case study of the southern dump slope landslide at the Hesigewula South Open-Pit Coal Mine in Inner Mongolia, China. Experimental results on the test set demonstrate that the Seq2Seq LSTM–Posterior Correction model significantly outperforms traditional methods such as linear regression and baseline LSTM models. This method offers an effective solution to data gaps in landslide deformation monitoring, demonstrating strong potential for accurate nonlinear imputation in complex environments and providing a practical approach for long-term InSAR-based landslide studies in areas affected by missing SAR data. Full article

(This article belongs to the Topic Remote Sensing and Geological Disasters)

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15 pages, 10610 KB

Open AccessArticle

Geological Hazard Risk Assessment Based on Time-Series InSAR Deformation: A Case Study of Xiaojin County, China

by Jiancun Li, Zhao Yan, Liqiang Tong, Yi Wang and Shangyuan Yu

Appl. Sci. 2025, 15(8), 4143; https://doi.org/10.3390/app15084143 - 9 Apr 2025

Cited by 1 | Viewed by 843

Abstract

Geological hazard risk assessment provides essential scientific support for geological disaster prevention and governance. The selection of appropriate evaluation factors is crucial to the accuracy and practicality of the risk assessment results. The existing factors for geological hazard risk assessment often suffer from [...] Read more.

Geological hazard risk assessment provides essential scientific support for geological disaster prevention and governance. The selection of appropriate evaluation factors is crucial to the accuracy and practicality of the risk assessment results. The existing factors for geological hazard risk assessment often suffer from issues such as poor timeliness and insufficient completeness. Interferometric Synthetic Aperture Radar (InSAR) technology, which offers large-scale, high spatiotemporal resolution monitoring of surface deformation, can effectively compensate for the shortcomings of existing risk assessment factors. How to effectively integrate time-series InSAR deformation results into geological hazard risk assessment has become a focus of research. This study fully considers the time-series InSAR deformation information; both the ascending and descending orbit results of the time-series InSAR deformation are introduced as two categories of evaluation factors in the risk assessment model. Subsequently, 11 types of assessment factors are selected by the Pearson correlation coefficient method, while the Information Volume Model and Evidence Weight Model are applied in the partitioning and assessment of risks in Xiaojin County, China. Finally, ROC (Receiver Operating Characteristic Curve) analysis is utilized to compare the accuracy of model evaluations before and after incorporating time-series InSAR deformation results. The results indicate that: (1) after incorporating time-series InSAR deformation monitoring results as evaluation factors into the information volume model and evidence weight model, the evaluation accuracy of the two models improved by 9.69% and 11.26%, respectively; (2) there are differences in risk partitioning among different evaluation models. From the risk partitioning result of Xiaojin County in this study, the evaluation accuracy of the information volume model is higher than that of the evidence weight model, and the performance is more prominent after adding the time-series InSAR deformation results. Full article

(This article belongs to the Topic Remote Sensing and Geological Disasters)

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22 pages, 5387 KB

Open AccessArticle

Landslide Segmentation in High-Resolution Remote Sensing Images: The Van–UPerAttnSeg Framework with Multi-Scale Feature Enhancement

by Chang Li, Quan Zou, Guoqing Li and Wenyang Yu

Remote Sens. 2025, 17(7), 1265; https://doi.org/10.3390/rs17071265 - 2 Apr 2025

Viewed by 1085

Abstract

Among geological disasters, landslides are a common and extremely destructive disaster. Their rapid identification is crucial for disaster analysis and response. However, traditional methods of landslide recognition mainly rely on visual interpretation and manual recognition of remote sensing images, which are time-consuming and [...] Read more.

Among geological disasters, landslides are a common and extremely destructive disaster. Their rapid identification is crucial for disaster analysis and response. However, traditional methods of landslide recognition mainly rely on visual interpretation and manual recognition of remote sensing images, which are time-consuming and susceptible to subjective factors, thereby limiting the accuracy and efficiency of recognition. To overcome these limitations, for high-resolution remote sensing images, this method first uses online equalization sampling and enhancement strategy to sample high-resolution remote sensing images to ensure data balance and diversity. Then, it adopts an encoder–decoder structure, where the encoder is a visual attention network (Van) that focuses on extracting discriminative features of different scales from landslide images. The decoder consists of a pyramid pooling module (PPM) and feature pyramid network (FPN), combined with a convolutional block attention module (CBAM) module. Through this structure, the model can effectively integrate features of different scales, achieving precise positioning and recognition of landslide areas. In addition, this study introduces a sliding window algorithm based on Gaussian fusion as a post-processing method, which optimizes the prediction of landslide edge in high-resolution remote sensing images and ensures the context reasoning ability of the model. In the validation set, this method achieved a significant landslide recognition effect with a Dice score of 84.75%, demonstrating high accuracy and efficiency. This result demonstrates the importance and effectiveness of the research method in improving the accuracy and efficiency of landslide recognition, providing strong technical support for analysis and response to geological disasters. Full article

(This article belongs to the Topic Remote Sensing and Geological Disasters)

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23 pages, 28011 KB

Open AccessArticle

Predicting the Spatial Distribution of Geological Hazards in Southern Sichuan, China, Using Machine Learning and ArcGIS

by Ruizhi Zhang, Dayong Zhang, Bo Shu and Yang Chen

Land 2025, 14(3), 577; https://doi.org/10.3390/land14030577 - 10 Mar 2025

Cited by 4 | Viewed by 1587

Abstract

Geological hazards in Southern Sichuan have become increasingly frequent, posing severe risks to local communities and infrastructure. This study aims to predict the spatial distribution of potential geological hazards using machine learning models and ArcGIS-based spatial analysis. A dataset comprising 2700 known geological [...] Read more.

Geological hazards in Southern Sichuan have become increasingly frequent, posing severe risks to local communities and infrastructure. This study aims to predict the spatial distribution of potential geological hazards using machine learning models and ArcGIS-based spatial analysis. A dataset comprising 2700 known geological hazard locations in Yibin City was analyzed to extract key environmental and topographic features influencing hazard susceptibility. Several machine learning models were evaluated, including random forest, XGBoost, and CatBoost, with model optimization performed using the Sparrow Search Algorithm (SSA) to enhance prediction accuracy. This study produced high-resolution susceptibility maps identifying high-risk zones, revealing a distinct spatial pattern characterized by a concentration of hazards in mountainous areas such as Pingshan County, Junlian County, and Gong County, while plains exhibited a relatively lower risk. Among different hazard types, landslides were found to be the most prevalent. The results further indicate a strong spatial overlap between predicted high-risk zones and existing rural settlements, highlighting the challenges of hazard resilience in these areas. This research provides a refined methodological framework for integrating machine learning and geospatial analysis in hazard prediction. The findings offer valuable insights for rural land use planning and hazard mitigation strategies, emphasizing the necessity of adopting a “small aggregations and multi-point placement” approach to settlement planning in Southern Sichuan’s mountainous regions. Full article

(This article belongs to the Topic Remote Sensing and Geological Disasters)

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15 pages, 6796 KB

Open AccessArticle

A Micro-Topography Enhancement Method for DEMs: Advancing Geological Hazard Identification

by Qiulin He, Xiujun Dong, Haoliang Li, Bo Deng and Jingsong Sima

Remote Sens. 2025, 17(5), 920; https://doi.org/10.3390/rs17050920 - 5 Mar 2025

Cited by 4 | Viewed by 1672

Abstract

Geological hazards in densely vegetated mountainous regions are challenging to detect due to terrain concealment and the limitations of traditional visualization methods. This study introduces the LiDAR image highlighting algorithm (LIHA), a novel approach for enhancing micro-topographical features in digital elevation models (DEMs) [...] Read more.

Geological hazards in densely vegetated mountainous regions are challenging to detect due to terrain concealment and the limitations of traditional visualization methods. This study introduces the LiDAR image highlighting algorithm (LIHA), a novel approach for enhancing micro-topographical features in digital elevation models (DEMs) derived from airborne LiDAR data. By analogizing terrain profiles to non-stationary spectral signals, LIHA applies locally estimated scatterplot smoothing (Loess smoothing), wavelet decomposition, and high-frequency component amplification to emphasize subtle features such as landslide boundaries, cracks, and gullies. The algorithm was validated using the Mengu landslide case study, where edge detection analysis revealed a 20-fold increase in identified micro-topographical features (from 1907 to 37,452) after enhancement. Quantitative evaluation demonstrated LIHA’s effectiveness in improving both human interpretation and automated detection accuracy. The results highlight LIHA’s potential to advance early geological hazard identification and mitigation, particularly when integrated with machine learning for future applications. This work bridges signal processing and geospatial analysis, offering a reproducible framework for high-precision terrain feature extraction in complex environments. Full article

(This article belongs to the Topic Remote Sensing and Geological Disasters)

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16 pages, 12273 KB

Open AccessArticle

Early Identification of Geological Hazards Along the Power Transmission Line in Weinan Based on SBAS-InSAR

by Bo Shan, Jianguo Qi, Wucheng Tian, Kuanxing Zhu, Tie Jin, Qingkun Yang, Xiguan An, Guang Yang, Qi Hu and Chen Cao

Appl. Sci. 2025, 15(2), 920; https://doi.org/10.3390/app15020920 - 18 Jan 2025

Cited by 5 | Viewed by 1743

Abstract

Landslides and ground subsidence pose significant threats to the successful construction and operation of transmission line projects in the Loess Plateau region. This study aims to explore an accurate early identification method for geological hazards, providing support for the construction and smooth operation [...] Read more.

Landslides and ground subsidence pose significant threats to the successful construction and operation of transmission line projects in the Loess Plateau region. This study aims to explore an accurate early identification method for geological hazards, providing support for the construction and smooth operation of the transmission project along the route from Baishui County, Weinan City, Shaanxi Province to Lantian County, Xi’an City, Shaanxi Province. Small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology was used to acquire the surface deformation data of the study area from 4 February 2018 to 21 May 2023. The deformation data were spatially analyzed through kernel density analysis, which quickly and intuitively identified 52 potential geological hazard points in the region, including eight landslides and 44 ground subsidence. Detailed field investigations of the hazards confirmed the accuracy of the identification results. A thorough analysis of typical hazards, such as landslide No. 9 and ground subsidence No. 29, revealed severe deformation, posing a threat to the proposed transmission project. This study indicates that combining InSAR, kernel density analysis, and field investigations can accurately and quickly identify geological hazards around transmission lines, providing support for the site selection and implementation of transmission projects. Full article

(This article belongs to the Topic Remote Sensing and Geological Disasters)

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23 pages, 6062 KB

Open AccessArticle

MetaTrans-FSTSF: A Transformer-Based Meta-Learning Framework for Few-Shot Time Series Forecasting in Flood Prediction

by Jiange Jiang, Chen Chen, Anna Lackinger, Huimin Li, Wan Li, Qingqi Pei and Schahram Dustdar

Remote Sens. 2025, 17(1), 77; https://doi.org/10.3390/rs17010077 - 28 Dec 2024

Cited by 5 | Viewed by 4228

Abstract

Time series forecasting, particularly within the Internet of Things (IoT) and hydrological domains, plays a critical role in predicting future events based on historical data, which is essential for strategic decision making. Effective flood forecasting is pivotal for optimal water resource management and [...] Read more.

Time series forecasting, particularly within the Internet of Things (IoT) and hydrological domains, plays a critical role in predicting future events based on historical data, which is essential for strategic decision making. Effective flood forecasting is pivotal for optimal water resource management and for mitigating the adverse impacts of flood events. While deep learning methods have demonstrated exceptional performance in time series prediction through advanced feature extraction and pattern recognition, they encounter significant limitations when applied to scenarios with sparse data, especially in flood forecasting. The scarcity of historical data can severely hinder the generalization capabilities of traditional deep learning models, presenting a notable challenge in practical flood prediction applications. To address this issue, we introduce MetaTrans-FSTSF, a pioneering meta-learning framework that redefines few-shot time series forecasting. By innovatively integrating MAML and Transformer architectures, our framework provides a specialized solution tailored for the unique challenges of flood prediction, including data scarcity and complex temporal patterns. This framework goes beyond standard implementations, delivering significant improvements in predictive accuracy and adaptability. Our approach leverages Model-Agnostic Meta-Learning (MAML) to enable rapid adaptation to new forecasting tasks with minimal historical data. Our inner architecture is a Transformer-based meta-predictor capable of capturing intricate temporal dependencies inherent in flood time series data. Our framework was evaluated using diverse datasets, including a real-world hydrological dataset from a small catchment area in Wuyuan, China, and other benchmark time series datasets. These datasets were preprocessed to align with the meta-learning approach, ensuring their suitability for tasks with limited data availability. Through extensive evaluation, we demonstrate that MetaTrans-FSTSF substantially improves predictive accuracy, achieving a reduction of up to 16%, 19%, and 8% in MAE compared to state-of-the-art methods. This study highlights the efficacy of meta-learning techniques in overcoming the limitations posed by data scarcity and enhancing flood forecasting accuracy where historical data are limited. Full article

(This article belongs to the Topic Remote Sensing and Geological Disasters)

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15 pages, 10808 KB

Open AccessArticle

A Strong Noise Reduction Network for Seismic Records

by Tong Shen, Xuan Jiang, Wenzheng Rong, Lei Xu, Xianguo Tuo and Guili Peng

Appl. Sci. 2024, 14(22), 10262; https://doi.org/10.3390/app142210262 - 7 Nov 2024

Cited by 2 | Viewed by 2217

Abstract

Noise reduction is a critical step in seismic data processing. A novel strong noise reduction network is proposed in this study. The network enhances the U-Net architecture with an improved inception module and coordinate attention (CA) mechanism, suppressing noise and enhancing signal clarity. [...] Read more.

Noise reduction is a critical step in seismic data processing. A novel strong noise reduction network is proposed in this study. The network enhances the U-Net architecture with an improved inception module and coordinate attention (CA) mechanism, suppressing noise and enhancing signal clarity. These enhancements improve the network’s capability to distinguish between signal and noise in the time–frequency domain. We trained and tested our model on the STEAD dataset, which eliminated noise across various frequency bands, improved the signal-to-noise ratio (SNR) of seismic records, and reduced the waveform distortion significantly. Comparative analyses against U-Net, DeepDenoiser, and DnRDB models, using signals with SNRs ranging from −14 dB to 0 dB, demonstrated our model’s superior performance. At the same time, we demonstrated that the Inception Conv Block has a significant impact on the denoising ability of the network. Furthermore, validation using the “Di Ting” dataset and real noisy signals confirmed the model’s generalizability. These results show that the proposed model significantly outperforms the comparative methods in terms of the SNR, correlation coefficient (r), and root mean square error (RMSE), delivering higher-quality seismograms. The enhanced phase-picking accuracy underscores the potential of our approach to advance in geophysics applications. Full article

(This article belongs to the Topic Remote Sensing and Geological Disasters)

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