Preprints
Filtering by Subject: Applied Statistics
An Improved Methodology to Estimate Cross-Scale Kinetic Energy Transfers from Third-Order Structure Functions using Regularized Least-Squares
Published: 2025-03-25
Subjects: Analysis, Applied Statistics, Fluid Dynamics, Longitudinal Data Analysis and Time Series, Oceanography, Oceanography and Atmospheric Sciences and Meteorology, Other Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Statistical Methodology, Statistical, Nonlinear, and Soft Matter Physics
Several methods exist for estimating cross-scale kinetic energy (KE) transfers; however, they are ill-adapted for sparse ocean observations, hindering the study of oceanic KE transfers. A newly developed third-order structure function $D3(r)$ framework allows estimation of KE injection rates $\epsilon_j(k)$ and KE transfers $F(k)$ across scales using sparse data. This approach requires inverse [...]
locationallocation: solving Maximal Coverage Location-Allocation geospatial infrastructure assessment and planning problems
Published: 2025-03-19
Subjects: Applied Statistics, Geographic Information Sciences, Operations Research, Systems Engineering and Industrial Engineering, Spatial Science
Assessing and planning infrastructure and networks over space conditional to a spatially distributed demand and with consideration of accessibility and spatial justice goals and under infrastructure allocation constraints is a key policy objective. Potential applications extend to the domains of public infrastructure assessment and planning (public services provision, e.g. transport, social [...]
Site Planning for a Network of Government-operated Weather Stations in the Dominican Republic Using Zonal Statistics from Geospatial Sources, Multi-Criteria Decision-Making, and Neighborhood Analysis
Published: 2024-12-30
Subjects: Applied Statistics, Climate, Earth Sciences, Environmental Monitoring, Environmental Sciences, Geographic Information Sciences, Geography, Meteorology, Oceanography and Atmospheric Sciences and Meteorology, Physical and Environmental Geography, Physical Sciences and Mathematics, Remote Sensing, Spatial Science
Many weather station networks lack sufficient representativeness, and their station density is often inadequate to capture spatial and climatic variability effectively. Optimal site selection is therefore essential to enhance spatial coverage and improve data quality. This study proposes a methodology for identifying optimal sites for a meteorological station network in the Dominican Republic, [...]
Functional regression for space-time prediction of precipitation-induced shallow landslides in South Tyrol, Italy
Published: 2024-12-10
Subjects: Applied Statistics, Environmental Indicators and Impact Assessment, Environmental Monitoring, Geology, Geomorphology, Hydrology, Multivariate Analysis, Statistical Models
Shallow landslides are geomorphic hazards in mountainous terrains across the globe. Their occurrence can be attributed to the interplay of static and dynamic landslide controls. In previous studies, data-driven approaches have been employed to model shallow landslides on a regional scale, focusing on analyzing the spatial aspects and time-varying conditions separately. Still, the joint assessment [...]
Challenges and Opportunities of Data Driven Advance Classification for Hard Rock TBM excavations
Published: 2024-09-21
Subjects: Applied Statistics, Civil and Environmental Engineering, Civil Engineering, Earth Sciences, Geology, Geotechnical Engineering
Excavation with tunnel Boring Machines (TBMs) is a widely used method of tunneling in all ground types including soil and rock today. The paper addresses the shift from traditional subjective methods to data-driven approaches for advance classification of TBMs in hard rock tunnel excavation. By leveraging continuous TBM operational data, these methodologies offer more objective, transparent, [...]
Uncertainty aware sample mass determination of coarse-grained soils for particle size analyses
Published: 2024-08-23
Subjects: Applied Statistics, Civil and Environmental Engineering, Civil Engineering, Earth Sciences, Engineering, Environmental Engineering, Environmental Monitoring, Geology, Geomorphology, Geotechnical Engineering, Hydraulic Engineering, Hydrology, Other Civil and Environmental Engineering, Other Earth Sciences, Probability, Sedimentology, Soil Science, Statistical Models, Statistics and Probability, Stratigraphy
Determining particle size distributions (PSD) of soils is a basic first step in manygeotechnical analyses and guidance is given in different national standards. Forambiguous reasons, the recommended minimum sample mass (m_min) for the PSD-analyses of soils with a main component of gravel or greater is based on equationsincluding the soil's maximum grain diameter (D_max). We claim that the [...]
Increasingly seasonal jet stream raises risk of co-occurring flooding and extreme wind in Great Britain
Published: 2024-07-03
Subjects: Applied Statistics, Atmospheric Sciences, Climate, Hydrology, Multivariate Analysis
Insurers and risk managers for critical infrastructure such as transport or power networks typically do not account for flooding and extreme winds happening at the same time in their quantitative risk assessments. We explore this potentially critical underestimation of risk from these co-occurring hazards through studying events using the regional 12 km resolution UK Climate Projections for a [...]
Tackling water table depth modeling via machine learning: From proxy observations to verifiability
Published: 2024-05-02
Subjects: Applied Statistics, Hydrology
Spatial patterns of water table depth (WTD) play a crucial role in shaping ecological resilience, hydrological connectivity, and human-centric systems. Generally, a large-scale (e.g., continental or global) continuous map of static WTD can be simulated using either physically-based (PB) or machine learning-based (ML) models. We construct three fine-resolution (500 m) ML simulations of WTD, using [...]
Estimating the mass eruption rate of volcanic eruptions from the plume height using Bayesian regression with historical data: the MERPH model
Published: 2024-04-18
Subjects: Applied Statistics, Statistical Models, Statistics and Probability, Volcanology
The mass eruption rate (MER) of an explosive volcanic eruption is a commonly used quantifier of the magnitude of the eruption, and estimating it is importance in managing volcanic hazards. The physical connection between the MER and the rise height of the eruption column results in a scaling relationship between these quantities, allowing one to be inferred from the other. Eruption source [...]
Advancing forest aboveground biomass mapping by integrating GEDI with other Earth Observation data using a cloud computing platform: A case study of Alabama, United States
Published: 2024-04-06
Subjects: Applied Statistics, Climate, Earth Sciences, Environmental Monitoring, Environmental Sciences, Forest Management, Other Forestry and Forest Sciences
Forest aboveground biomass (AGB) is a crucial indicator for monitoring carbon and requires accurate quantification. This study aimed to advance AGB estimation using open access Earth observation (EO) data and cloud computing, focusing on Alabama, USA. The specific objectives were to: (1) develop a workflow for creating a 30 m forest AGBD map with GEDI, using GEE, (2) evaluate and compare [...]
An updated landslide susceptibility model and a log-Gaussian Cox process extension for Scotland
Published: 2024-02-01
Subjects: Applied Statistics, Geomorphology
At the time of its development, GeoSure was created using expert knowledge based on a thorough understanding of the engineering geology of the rocks and soils of Great Britian. The ability to use a data-driven methodology to develop a national scale landslide susceptibility was not possible due to the relatively small size of the landslide inventory at the time. In the intervening 20 years the [...]
Model Ensemble with Dropout for Uncertainty Estimation in Binary Sea Ice or Water Segmentation using Sentinel-1 SAR
Published: 2024-01-19
Subjects: Applied Statistics, Environmental Monitoring, Signal Processing
Despite the growing use of deep learning in sea ice mapping with SAR imagery, the study of model uncertainty and segmentation results remains limited. Deep learning models often produce overconfident predictions, a concern in sea ice mapping where misclassification can impact marine navigation safety. We incorporate and compare dropout and model ensemble within a convolutional neural network [...]
Demystifying the Dynamics of Global and Regional Sea Level Trends from 1993 to 2021
Published: 2023-07-07
Subjects: Applied Statistics, Earth Sciences, Physical Sciences and Mathematics, Statistical Models
As global sea levels rise, questions persist about the robustness of trends and their dynamics. Here, we offer a fresh perspective by examining the dynamics of global and regional mean sea-level trends using a probabilistic framework applied to the altimetric record. We show that the global mean sea-level (GMSL) rise accelerated from 2.5 mm/yr (1993-2000) to 4.2 mm/yr (2014-2021) with an average [...]
Assessing vertical accuracy and spatial coverage of ICESat-2 and GEDI spaceborne lidar for creating global terrain models
Published: 2023-07-06
Subjects: Applied Statistics, Geomorphology, Remote Sensing
Digital elevation models (DEMs) are a necessity for modelling many large-scale environmental processes. In this study, we investigate the potential of data from two spaceborne lidar altimetry missions, ICESat-2 and GEDI—with respect to their vertical accuracies and planimetric data collection patterns—as sources for rasterisation towards global DEMs. We validate the terrain [...]
Carbon Utilization and Storage through Rehabilitation of Groundwater Wells
Published: 2023-05-21
Subjects: Applied Statistics, Civil and Environmental Engineering, Climate, Earth Sciences, Environmental Sciences, Geochemistry, Hydrology, Longitudinal Data Analysis and Time Series, Oil, Gas, and Energy, Statistical Methodology, Statistical Models, Statistics and Probability
According to the Intergovernmental Panel on Climate Change (IPCC) of the United Nations (UN), rise in atmospheric concentration of carbon dioxide (CO2) due to anthropogenic factors is considered as the primary driver for global climate change. With almost every major corporation around the world working towards their “net-zero goals”, it is becoming increasingly important to have more [...]