EarthArXiv @eartharxiv@qoto.org

Factors Affecting Aboveground Carbon Storage in Mixed Oak-Pine Forests: A Multiple Regression Analysis of Southeastern U.S. Forest Inventory Data
https://doi.org/10.31223/X5T17V #AppliedStatistics #Climate #EcologyAndEvolutionaryBiology #EnvironmentalIndicatorsAndImpactAssessment #EnvironmentalSciences #ForestBiology #ForestManagement #MultivariateAnalysis #NaturalResourcesAndConservation #PlantSciences #StatisticalModels

Nature’s role in national security
https://doi.org/10.31223/X56746 #AquacultureAndFisheriesLifeSciences #EcologyAndEvolutionaryBiology #EnvironmentalHealthAndProtection #EnvironmentalStudies #MarineBiology #NaturalResourcesAndConservation #NaturalResourcesManagementAndPolicy #NationalSecurity #EcosystemServices #EnvironmentalChange

Anomaly detection of Synthetic Aperture Radar Interferograms with semi-supervised machine learning
https://doi.org/10.31223/X59X75 #GeophysicsAndSeismology #Volcanology

Unified Cross-Modal Learning for Hydrological Processes Using Multi-Task Transformer Framework
https://doi.org/10.31223/X5FQ8Q #EnvironmentalEngineering #EnvironmentalMonitoring #HydraulicEngineering #Hydrology #WaterResourceManagement #Multi-modal #DeepLearning #Multi-taskLearning(mtl) #Transformers #Hydrology #Streamflow #Rainfall

A Weighted Fitting Approach for Diameter Distributions from Horizontal Point Sampling
https://doi.org/10.31223/X5KF39 #AppliedStatistics #OtherForestryAndForestSciences #StatisticalMethodology #StatisticalModels #HorizontalPointSampling #DiameterDistributionModelling #WeightedNonlinearLeastSquares #ForestBiometrics #ReproducibleResearch

ForestCast: Forecasting Deforestation Risk at Scale with Deep Learning
https://doi.org/10.31223/X5Q757 #ArtificialIntelligenceAndRobotics #EnvironmentalMonitoring #Deforestation #Forecasting #Remote-sensing #Deep-learning

HydroQuantum: A new quantum-driven Python package for hydrological simulation
https://doi.org/10.31223/X5TX7T #Education #Engineering

A Static Enthalpy Equilibrium Approach to the Stability of Saturated Sands (Version 2)
https://doi.org/10.31223/X5NF3X #CivilAndEnvironmentalEngineering #CivilEngineering #ConstructionEngineeringAndManagement #EarthSciences #Engineering #EnvironmentalEngineering #GeotechnicalEngineering #HydraulicEngineering #MineralPhysics #PhysicalSciencesAndMathematics #Sedimentology #SaturatedSands #EnthalpyEquilibrium #StructuralCollapse #EnergeticStability #SoilLiquefaction #GranularPhysics #ThermodynamicSoilMmechanics #SpontaneousLiquefaction #InitialLiquefaction

Multi-Task Learning as a Step Toward Building General-Purpose Hydrological Forecasting Systems
https://doi.org/10.31223/X58B3G #EnvironmentalEngineering #EnvironmentalMonitoring #HydraulicEngineering #Hydrology #MultivariateAnalysis #Multi-taskLearning(mtl) #Transformer #Hydrology #Streamflow #SoilMoisture #ExplainableAi(shap) #DeepLearning #State-spaceModels(mamba)

Nature’s role in national security
https://doi.org/10.31223/X56746 #AquacultureAndFisheriesLifeSciences #EcologyAndEvolutionaryBiology #EnvironmentalHealthAndProtection #EnvironmentalStudies #MarineBiology #NaturalResourcesAndConservation #NaturalResourcesManagementAndPolicy #NationalSecurity #EcosystemServices #EnvironmentalChange

Anomaly detection of Synthetic Aperture Radar Interferograms with semi-supervised machine learning
https://doi.org/10.31223/X59X75 #GeophysicsAndSeismology #Volcanology

Unified Cross-Modal Learning for Hydrological Processes Using Multi-Task Transformer Framework
https://doi.org/10.31223/X5FQ8Q #EnvironmentalEngineering #EnvironmentalMonitoring #HydraulicEngineering #Hydrology #WaterResourceManagement #Multi-modal #DeepLearning #Multi-taskLearning(mtl) #Transformers #Hydrology #Streamflow #Rainfall

A Weighted Fitting Approach for Diameter Distributions from Horizontal Point Sampling
https://doi.org/10.31223/X5KF39 #AppliedStatistics #OtherForestryAndForestSciences #StatisticalMethodology #StatisticalModels #HorizontalPointSampling #DiameterDistributionModelling #WeightedNonlinearLeastSquares #ForestBiometrics #ReproducibleResearch

ForestCast: Forecasting Deforestation Risk at Scale with Deep Learning
https://doi.org/10.31223/X5Q757 #ArtificialIntelligenceAndRobotics #EnvironmentalMonitoring #Deforestation #Forecasting #Remote-sensing #Deep-learning

HydroQuantum: A new quantum-driven Python package for hydrological simulation
https://doi.org/10.31223/X5TX7T #Education #Engineering

A Static Enthalpy Equilibrium Approach to the Stability of Saturated Sands (Version 2)
https://doi.org/10.31223/X5NF3X #CivilAndEnvironmentalEngineering #CivilEngineering #ConstructionEngineeringAndManagement #EarthSciences #Engineering #EnvironmentalEngineering #GeotechnicalEngineering #HydraulicEngineering #MineralPhysics #PhysicalSciencesAndMathematics #Sedimentology #SaturatedSands #EnthalpyEquilibrium #StructuralCollapse #EnergeticStability #SoilLiquefaction #GranularPhysics #ThermodynamicSoilMmechanics #SpontaneousLiquefaction #InitialLiquefaction

Multi-Task Learning as a Step Toward Building General-Purpose Hydrological Forecasting Systems
https://doi.org/10.31223/X58B3G #EnvironmentalEngineering #EnvironmentalMonitoring #HydraulicEngineering #Hydrology #MultivariateAnalysis #Multi-taskLearning(mtl) #Transformer #Hydrology #Streamflow #SoilMoisture #ExplainableAi(shap) #DeepLearning #State-spaceModels(mamba)

Local Prediction of Temperate Forest Structure in Eastern North America Using LiDAR, Radar, and Optical Data
https://doi.org/10.31223/X5ZR0P #OtherForestryAndForestSciences #TerrestrialAndAquaticEcology #Gedi #Lidar #VegetationStructure #TemperateForest #EasternNorthAmerica #LocalModeling #MachineLearning

Past, Present and Future of the Indus Water Treaty: Implications for Transboundary Water Governance Challenges and Modernization Prospects
https://doi.org/10.31223/X53F2K #InternationalAndAreaStudies #PhysicalAndEnvironmentalGeography #WaterResourceManagement #InternationalWaterResources #Hydrodiplomacy #WaterSharingTreaties #IndusRiverBasin #ClimateChangeImpacts #Socio-economicCondition #India-pakistan

Formation of Parasequence-like Successions at Different Depositional Hierarchical Scales
https://doi.org/10.31223/X57768 #Sedimentology #Stratigraphy