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Regional climate model emulator based on deep learning: concept and first evaluation of a novel hybrid downscaling approach
Providing reliable information on climate change at local scale remains a challenge of first importance for impact studies and policymakers. Here, we...
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Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator: concept for bias correction, assessment of surrogate reliability and its application to the carbon dioxide benchmark
Surrogate models are widely used to improve the computational efficiency in various geophysical simulation problems by reducing the number of model...
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Emulator-based global sensitivity analysis for flow-like landslide run-out models
Landslide run-out modeling involves various uncertainties originating from model input data. It is therefore desirable to assess the model’s...
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Efficient inference and learning of a generative model for ENSO predictions from large multi-model datasets
Historical simulations of global sea-surface temperature (SST) from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) are...
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Approximation of Metro Water District Basin Using Parallel Computing of Emulator Based Spatial Optimization (PCESO)
Metro Water District (MWD) is an agency that administers water distribution in a large geographic region. It targets for existing conditions with...
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Speeding Up Reactive Transport Simulations in Cement Systems by Surrogate Geochemical Modeling: Deep Neural Networks and k-Nearest Neighbors
We accelerate reactive transport (RT) simulation by replacing the geochemical solver in the RT code by a surrogate model or emulator, considering...
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Multi-level emulation of tsunami simulations over Cilacap, South Java, Indonesia
Carrying out a Probabilistic Tsunami Hazard Assessment (PTHA) requires a large number of simulations done at a high resolution. Statistical emulation...
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Probabilistic Landslide-Generated Tsunamis in the Indus Canyon, NW Indian Ocean, Using Statistical Emulation
The Indus Canyon in the northwestern Indian Ocean has been reported to be the site of numerous submarine mass failures in the past. This study is the...
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Volcanic effects on climate: recent advances and future avenues
Volcanic eruptions have long been studied for their wide range of climatic effects. Although global-scale climatic impacts following the formation of...
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The Urban Heat Footprint (UHF)—a new unified climatic and statistical framework for urban warming
In this paper we combine statistical modelling and climate models in order to develop a unified statistical framework for quantifying the Urban Heat...
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Bayesian active learning for parameter calibration of landslide run-out models
Landslide run-out modeling is a powerful model-based decision support tool for landslide hazard assessment and mitigation. Most landslide run-out...
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Machine learning and the quest for objectivity in climate model parameterization
Parameterization and parameter tuning are central aspects of climate modeling, and there is widespread consensus that these procedures involve...
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Comparison between Bayesian updating and approximate Bayesian computation for model identification of masonry towers through dynamic data
Model updating procedures based on experimental data are commonly used in case of historic buildings to identify numerical models that are...
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River Flow Modeling in Semi-Arid and Humid Regions Using an Integrated Method Based on LARS-WG and LSTM Models
River flow or runoff is an important water flux that can pose great threats to water security because of changes in its timing, magnitude, and...
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PMTools: New Application for Paleomagnetic Data Analysis
AbstractThis paper introduces PMTools ( https://pmtools.ru ), a novel cross-platform open-source web application designed for the analysis of...
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Probabilistic projections of baseline twenty-first century CO2 emissions using a simple calibrated integrated assessment model
Probabilistic projections of baseline (with no additional mitigation policies) future carbon emissions are important for sound climate risk...
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Application of Machine Learning Approaches in Particle Tracking Model to Estimate Sediment Transport in Natural Streams
Numerous empirical equations and machine learning (ML) techniques have emerged to forecast dispersion coefficients in open channels. However, the...
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Quantifying the influence of natural climate variability on in situ measurements of seasonal total and extreme daily precipitation
While various studies explore the relationship between individual sources of climate variability and extreme precipitation, there is a need for...
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Elevating the possibilities of meshless groundwater flow modeling: a developed approach for parameter estimation and uncertainty quantification
Groundwater modeling is often associated with uncertainties due to incomplete knowledge of the subsurface system or uncertainties arising from...
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Quantifying contributions of ozone changes to global and arctic warming during the second half of the twentieth century
Ozone is the third most important greenhouse gas in driving global warming, mainly due to increased tropospheric ozone. About 50% of the growth of...
