An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0

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This study integrates deep learning and geospatial analysis to enhance soil loss estimation in the Moulouya Watershed, a region prone to erosion due to diverse topography and climatic conditions. Traditional models like the Universal Soil Loss Equation (US ...

Accurate and reliable electricity load forecasts are becoming increasingly important as the share of intermittent resources in the system increases. Distribution System Operators (DSOs) are called to accurately forecast their production and consumption to ...

As large, data-driven artificial intelligence models become ubiquitous, guaranteeing high data quality is imperative for constructing models. Crowdsourcing, community sensing, and data filtering have long been the standard approaches to guaranteeing or imp ...

Selection bias may arise when data have been chosen in a way that subsequent analysis does not account for. Such bias can arise in climate event attribution studies that are performed rapidly after a devastating "trigger event'', whose occurrence correspon ...

Human thermo-physiology models (HTPM) are useful tools to assess dynamic and non-uniform human thermal states. However, they are developed based on the physiological data of an average person. In this paper, we present a detailed evaluation of two sophisti ...

The surface mass balance (SMB) of large polar ice sheets and of snow and ice surfaces in general are incompletely understood because of the complexity of processes involved. One such process, drifting and blowing snow, has only been considered in a very si ...

This thesis presents a comprehensive investigation of the interaction between precipitation and wind-induced erosion and deposition of snow on Arctic sea ice. The study uses observations from the 2019-2020 MOSAiC expedition and makes use of the 3D snow cov ...

Sublimation influences the water storage in snow covers and glaciers, which is important for water use and projections of the sea level rise. Yet, it is challenging to quantify sublimation for large areas or in conditions of snow transport. In-situ measure ...

Dynamic downscaling of atmospheric forcing data to the hectometer resolution has shown increases in accuracy for landsurface models, but at great computational cost. Here we present a validation of a novel intermediate complexity atmospheric model, HICAR, ...

Wind energy potential in complex terrain is still poorly understood and difficult to quantify. With Switzerland’s current efforts to shift to renewable energy resources, it is now becoming even more crucial to investigate the hidden potential of wind energ ...

Snow plays a crucial role in processes regulating ecosystems, the climate, and human development. Mountain snowpack in particular has great relevance for downstream communities. Knowledge about the distribution and properties of the snowpack thus help in p ...

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

One of the primary causes of non-uniform snowfall deposition on the ground in mountainous regions is the preferential deposition of snow, which results from the interaction of near-surface winds with topography and snow particles. However, producing high-r ...

The Mountain snowpack stores months of winter precipitation at high elevations, supplying snowmelt to lowland areas in drier seasons for agriculture and human consumption worldwide. Accurate seasonal predictions of the snowpack are thus of great importance ...

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...

Sweat secreted by the human eccrine sweat glands can provide valuable biomarker information during exercise. Real-time non-invasive biomarker recordings are therefore useful for evaluating the physiological conditions of an athlete such as their hydration ...

Battery health prediction is significant while challenging for intelligent battery management. This article proposes a general framework for both short-term and long-term predictions of battery health under unseen dynamic loading and temperature conditions ...

We study the problem of estimating an unknown function from noisy data using shallow ReLU neural networks. The estimators we study minimize the sum of squared data-fitting errors plus a regularization term proportional to the squared Euclidean norm of the ...

Quantification of multiple drugs is of great importance and urgently needed in therapeutic drug monitoring (TDM) and personalized therapy. Especially, based on cyclic voltammograms (CVs) obtained by electrochemical sensors, the use of artificial neural net ...

In this work, we examine a network of agents operating asynchronously, aiming to discover an ideal global model that suits individual local datasets. Our assumption is that each agent independently chooses when to participate throughout the algorithm and t ...

An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0

Chattez avec Graph Search

Application of Deep Learning and Geospatial Analysis in Soil Loss Risk in the Moulouya Watershed, Morocco

Probabilistic Load Forecasting of distribution power systems based on empirical copulas

Extensions of Peer Prediction Incentive Mechanisms

Timing and spatial selection bias in rapid extreme event attribution

Modeling local thermal responses of individuals: Validation of advanced human thermo-physiology models

CRYOWRF—Model Evaluation and the Effect of Blowing Snow on the Antarctic Surface Mass Balance

Spatio-Temporal Dynamics of Snow on Central Arctic Sea Ice and its Implications

Assessing sublimation during snow transport and sublimation effects on the isotopic composition of water vapor

Intermediate complexity atmospheric modeling in complex terrain: is it right?

Combining Weather Station Data and Short-Term LiDAR Deployment to Estimate Wind Energy Potential with Machine Learning: A Case Study from the Swiss Alps

Good enough: Intermediate complexity atmospheric models improve the representation of processes affecting seasonal snow

Spectral Estimators for High-Dimensional Matrix Inference

Snowfall deposition in mountainous terrain: a statistical downscaling scheme from high-resolution model data on simulated topographies

A seasonal snowpack model forced with dynamically downscaled forcing data resolves hydrologically relevant accumulation patterns

Partial discharge localization in power transformer tanks using machine learning methods

Multisensing wearables for real-time monitoring of sweat electrolyte biomarkers during exercise and analysis on their correlation with core body temperature

Health Prediction for Lithium-Ion Batteries Under Unseen Working Conditions

Near-Minimax Optimal Estimation With Shallow ReLU Neural Networks

Optimized Quantification of Multiple Drug Concentrations by WeightedMSE With Machine Learning on Electrochemical Sensor

Asynchronous Diffusion Learning with Agent Subsampling and Local Updates