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Automated Machine Learning-Based Landslide Susceptibility Map** for the Three Gorges Reservoir Area, China
Machine learning (ML)-based landslide susceptibility map** (LSM) has achieved substantial success in landslide risk management applications....
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Landslide susceptibility map** with GIS in high mountain area of Nepal: a comparison of four methods
Landslide susceptibility map** (LSM) assists identifying and targeting landslide preventive measures, thereby minimizing potential losses. Multiple...
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Model Error Representations Using the Covariance Inflation Methods in Ensemble Data Assimilation System
Ensemble data assimilationEnsemble Data Assimilation (EDA) estimates the initial conditionsInitial Condition (IC) and the flow-dependent background...
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Fusion of In-Situ Soil Moisture and Land Surface Model Estimates Using Localized Ensemble Optimum Interpolation over China
Land data assimilation (DA) is an effective method to provide high-quality spatially and temporally continuous soil moisture datasets that are...
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Novel Physical Parameterizations in Vegetated Land Surface Processes for Carbon Allocations and Snow-Covered Surface Albedo
Vegetation growth/decay is important in land surface modeling to represent fluxes of surface energy and mass as well as surface albedoSurface albedo....
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Regional-scale spatiotemporal landslide probability assessment through machine learning and potential applications for operational warning systems: a case study in Kvam (Norway)
The use of machine learning models for landslide susceptibility map** is widespread but limited to spatial prediction. The potential of employing...
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Assessment of EnKF data assimilation of satellite-derived soil moisture over the Indian domain with the Noah land surface model
Land surface models (LSMs) are typically forced with observed precipitation and surface meteorology and hence the soil moisture estimates obtained...
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Global patterns in river water storage dependent on residence time
Accurate assessment of global river flows and stores is critical for informing water management practices, but current estimates of global river...
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The Role of Space-Based Observations for Groundwater Resource Monitoring over Africa
AbstractAfrica is particularly vulnerable to climate change impacts, which threatens food security, ecosystem protection and restoration initiatives,...
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Landslide susceptibility prediction using an incremental learning Bayesian Network model considering the continuously updated landslide inventories
Existing studies relating to landslide susceptibility prediction (LSP) either do not pay enough attentions to the continuously updated landslide...
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Landslide susceptibility prediction and map** using the LD-BiLSTM model in seismically active mountainous regions
Machine learning models have been widely used in landslide susceptibility prediction. However, landslide multidimensional feature extraction, model...
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Evaluation of Routed-Runoff from Land Surface Models and Reanalyses Using Observed Streamflow in Chinese River Basins
Previous studies have demonstrated that offline land surface models (LSMs) and global hydrological models (GHMs) can reasonably reproduce streamflow...
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Improving physiological simulations in seasonally dry tropical forests with limited measurements
Semiarid regions and seasonally dry tropical forests play a critical role in global carbon exchange. In the Southern Hemisphere, these areas can...
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A comparative evaluation of landslide susceptibility map** using machine learning-based methods in Bogor area of Indonesia
Landslide is one of the most highly frequent natural hazards that can bring serious casualties. One of the most susceptible landslide regions in...
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Novel Ensemble of M5P and Deep Learning Neural Network for Predicting Landslide Susceptibility: A Cross-Validation Approach
The landslides frequently affect Kurseong and the villages around it causing loss of life and property. The current study used the M5P technique, a...
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An attention-constrained neural network with overall cognition for landslide spatial prediction
The occurrence of landslides is affected by various environmental factors. When predicting landslides, conventional neural networks optimize...
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Modeling landslide susceptibility using data mining techniques of kernel logistic regression, fuzzy unordered rule induction algorithm, SysFor and random forest
This paper introduces four advanced intelligent algorithms, namely kernel logistic regression, fuzzy unordered rule induction algorithm,...
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Strategies for sampling pseudo-absences of landslide locations for landslide susceptibility map** in complex mountainous terrain of Northwest Himalaya
In the mountainous region of the world, landslides are a major issue that significantly impacts the socioeconomic, infrastructure, livestock, and...
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Measuring Landslide Susceptibility in Jakholi Region of Garhwal Himalaya Using Landsat Images and Ensembles of Statistical and Machine Learning Algorithms
Landslide susceptibility in the Jakholi region of Garhwal Himalaya was assessed applying novel ensembles of statistical and machine learning...
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Stability factor prediction of multilayer slope using three-dimensional convolutional neural network based on digital twin and prior knowledge data
In order to solve the disadvantage of considering slopes as a homogeneous layer in intelligent stability assessment, this paper proposes a compatible...
