Could electrical conductivity replace water level in rating curves for alpine streams?

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Despite their high ecological value, non-perennial streams have received less attention than their perennial counterparts. This doctoral thesis addresses this disparity by advancing knowledge on the dynamics of the drainage density and hydrologic processes ...

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 ...

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 ...

Ecohydrology published by John Wiley & Sons Ltd.The re-emergence of water in dry stream reaches affects streamwater chemistry due to the flushing of sediment and nutrients from previously dry channels and the increased connectivity between hillslopes and s ...

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 ...

The young water fraction represents the portion of water molecules in a stream that have entered the catchment relatively recently, typically within 2-3 months. It can be reliably estimated in spatially heterogeneous and nonstationary catchments from the a ...

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, ...

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 ...

The growing demand for online gait phase (GP) estimation, driven by advancements in exoskeletons and prostheses, has prompted numerous approaches in the literature. Some approaches explicitly use time, while others rely on state variables to estimate the G ...

The training of neural networks by gradient descent methods is a cornerstone of the deep learning revolution. Yet, despite some recent progress, a complete theory explaining its success is still missing. This article presents, for orthogonal input vectors, ...

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 ...

Spatially distributed meteorological information at the slope scale is relevant for many processes in complex terrain, yet information at this sub-km spatial resolution is difficult to obtain. While downscaling to kilometer resolutions is well described in ...

We present a method for segmenting cracks in images of masonry buildings damaged by earthquakes. Existing methods of crack detection fail to preserve the continuity of cracks, and their performance deteriorates with imprecise training labels. We address th ...

Headwater streams often experience intermittent flow. Consequently, the flowing drainage network expands and contracts and the flowing drainage density (DD) varies over time. Monitoring the DD dynamics is essential to understand the processes controlling i ...

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 ...

In this article, we consider decentralized optimization problems where agents have individual cost functions to minimize subject to subspace constraints that require the minimizers across the network to lie in low-dimensional subspaces. This constrained fo ...

The sensitivity of future groundwater recharge and temperature development was investigated for three alluvial aquifers in the urban agglomeration of the city of Basel, Switzerland. For selected climate projections groundwater recharge and the associated t ...

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 ...

Understanding concentration-discharge (C - Q) relationships can inform catchment solute and particulate export processes. Previous studies have shown that the extent to which baseflow contributes to streamflow can affect C-Q relationships in some catchment ...

We present a statistical framework to benchmark the performance of reconstruction algorithms for linear inverse problems, in particular, neural-network-based methods that require large quantities of training data. We generate synthetic signals as realizati ...

Could electrical conductivity replace water level in rating curves for alpine streams?

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Spatiotemporal Dynamics of Flow and Hydrochemistry in Drainage Networks

Partial discharge localization in power transformer tanks using machine learning methods

Spectral Estimators for High-Dimensional Matrix Inference

Comparison of the Dynamics of the Flowing Drainage Network and Water Chemistry for Four Headwater Catchments

Health Prediction for Lithium-Ion Batteries Under Unseen Working Conditions

Technical note: Two-component electrical-conductivity-based hydrograph separation employing an exponential mixing model (EXPECT) provides reliable high-temporal-resolution young water fraction estimates in three small Swiss catchments

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

Near-Minimax Optimal Estimation With Shallow ReLU Neural Networks

State-Based Versus Time-Based Estimation of the Gait Phase for Hip Exoskeletons in Steady and Transient Walking

Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs

Timing and spatial selection bias in rapid extreme event attribution

A Downscaling Intercomparison Study: The Representation of Slope- and Ridge-Scale Processes in Models of Different Complexity

TOPO-Loss for continuity-preserving crack detection using deep learning

Short-term dynamics of drainage density based on a combination of channel flow state surveys and water level measurements

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

Quantization for Decentralized Learning Under Subspace Constraints

Impacts of climate change on Swiss alluvial aquifers - A quantitative forecast focused on natural and artificial groundwater recharge by surface water infiltration

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

Synthesizing the impacts of baseflow contribution on concentration-discharge (C-Q) relationships across Australia using a Bayesian hierarchical model

A Statistical Framework to Investigate the Optimality of Signal-Reconstruction Methods