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Evaluation and attribution of trends in compound dry-hot events for major river basins in China
Concurrent compound dry and hot events (CDHEs) amplified more damange on the ecosystems and human society than individual extremes. Under climate...
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A new framework to substantiate the prevalence of drought intensities
Drought is a natural disaster originating from a shortage of precipitation over an extended period, causing water deficiency for various groups,...
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A hybrid intelligent prediction model of autoencoder neural network and multivariate adaptive regression spline for uniaxial compressive strength of rocks
In geomechanics, the determination of uniaxial compressive strength (UCS) from typical laboratory procedures is a challenging and time-consuming...
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Recurrence statistics of M ≥ 6 earthquakes in the Nepal Himalaya: formulation and relevance to future earthquake hazards
Recurrence statistics of large earthquakes has a long-term economic and societal importance. This study investigates the temporal distribution of...
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Using machine learning for model benchmarking and forecasting of depletion-induced seismicity in the Groningen gas field
The Groningen gas field in the Netherlands is experiencing induced seismicity as a result of ongoing depletion. The physical mechanisms that control...
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Stability criteria for Bayesian calibration of reservoir sedimentation models
Modeling reservoir sedimentation is particularly challenging due to the simultaneous simulation of shallow shores, tributary deltas, and deep waters....
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Probabilistic Back Analysis Based on Nadam, Bayesian, and Matrix-Variate Deep Gaussian Process for Rock Tunnels
Efficiently determining the properties of the rock mass is essential for evaluating tunnel stability in tunneling projects. The back-analysis...
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Terrestrial carbon cycle model-data fusion: Progress and challenges
The terrestrial carbon cycle is an important component of global biogeochemical cycling and is closely related to human well-being and sustainable...
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A gamma mixture model-based approach for the estimation of natural background levels of \({{\mathrm{NO}}_{3}}^{-}\)–\({\mathrm{N}}\) in groundwater
The first stage in determining the chemical status of a groundwater body in an aquifer system is to determine natural background levels (NBLs). The...
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Sequential design strategy for kriging and cokriging-based machine learning in the context of reservoir history-matching
Numerical models representing geological reservoirs can be used to forecast production and help engineers to design optimal development plans. These...
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The changing nature of hydroclimatic risks across South Africa
We present results from large ensembles of projected twenty-first century changes in seasonal precipitation and near-surface air temperature for the...
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GNSS NLOS detection method based on stacking ensemble learning and applications in smartphones
Global Satellite Navigation System (GNSS) has been widely used in various high-precision positioning services and has become an indispensable part of...
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Advanced ensemble machine-learning and explainable ai with hybridized clustering for solar irradiation prediction in Bangladesh
The solar revolution in Bangladesh stands as a symbol of hope and self-reliance, illuminating communities and steering the nation towards a more...
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A stacked ensemble learning-based framework for mineral map** using AVIRIS-NG hyperspectral image
AbstractHyperspectral data has a significant count of spectral channels with an enhanced spectral resolution, which provides detailed information at...
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Grade Control with Ensembled Machine Learning: A Comparative Case Study at the Carmen de Andacollo Copper Mine
The main goal of grade control is the prediction of material destination based on all available data. The common approach to grade control is based...
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A multifaceted journey in coastal meteorological projections through multioutput regression: a two-layer stacking ensemble approach
Coastal regions have experienced economic losses from natural disasters due to rising global temperatures and climate change. Accurate and timely...
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Copula-based multivariate analysis of hydro-meteorological drought
Droughts have far-reaching detrimental impacts on the environment, society, and economy, ranging from regional to national levels. As the drought...
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On the use of VMD-LSTM neural network for approximate earthquake prediction
Earthquake prediction has been widely studied in many fields using various technologies, including machine learning, which is able to explore the...
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A case study of tunnel boring machines advance rate prediction using meta-heuristic techniques
The advance rate (AR) of tunnel boring machines (TBMs) plays a pivotal role in evaluating their efficiency in tunnel engineering projects. This study...
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Detection of Tornado damage in forested regions via convolutional neural networks and uncrewed aerial system photogrammetry
Disaster damage assessments are a critical component to response and recovery operations. In recent years, the field of remote sensing has seen...
