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Improving the spatial resolution of GRACE-based groundwater storage estimates using a machine learning algorithm and hydrological model
The low-resolution characteristic of Gravity Recovery and Climate Experiment (GRACE) satellite data greatly limits their application in many fields...
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Using an innovative bivariate colour scheme to infer spatial links and patterns between prediction and uncertainty: an example based on an explainable soil CN ratio model
Although valuable for discovering geographical relationships and spatial statistics, bivariate maps or colour schemes are rarely employed in...
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A New Chemistry-Climate Model GRIMs-CCM: Model Evaluation of Interactive Chemistry-Meteorology Simulations
We describe a new chemistry-climate model, Global/Regional Integrated Model system Chemistry Climate Model (GRIMs-CCM), developed by coupling the...
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Accurate and generalizable soil liquefaction prediction model based on the CatBoost algorithm
Accurate prediction of soil liquefaction is important for preventing geological disasters. Soil liquefaction prediction models based on machine...
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Enhanced Prediction Model for Blast-Induced Air Over-Pressure in Open-Pit Mines Using Data Enrichment and Random Walk-Based Grey Wolf Optimization–Two-Layer ANN Model
In this study, two innovative techniques were introduced, including data enrichment and optimization, with the aim of significantly improving the...
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Machine Learning-Based Prediction of Shear Strength Parameters of Rock Materials
Shear strength parameters play a crucial role in the design and construction of rock slopes, underground openings, tunnels, excavations, and...
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Runoff Forecasting of Machine Learning Model Based on Selective Ensemble
Reliable runoff forecasting plays an important role in water resource management. In this study, we propose a homogeneous selective ensemble...
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Updated Simulation of Tropospheric Ozone and Its Radiative Forcing over the Globe and China Based on a Newly Developed Chemistry-Climate Model
This study evaluates the performance of a newly developed atmospheric chemistry-climate model, BCCAGCM_CUACE2.0 (Bei**g Climate Center Atmospheric...
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The Load-Bearing Mechanism of Rock-Socketed Piles Considering Rock Fragmentation
Based on the random distribution patterns of rock fragmentation (RF), a concrete–rock shear model (CRSM) considering RF was developed through the...
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A stacked generalization ensemble model for optimization and prediction of the gas well rate of penetration: a case study in **njiang
In gas drilling operations, the rate of penetration (ROP) parameter has an important influence on drilling costs. Prediction of ROP can optimize the...
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Application of a new machine learning model to improve earthquake ground motion predictions
A cross-region prediction model named SeisEML (an acronym for Seismological Ensemble Machine Learning) has been developed in this paper to predict...
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Flood prioritization integrating picture fuzzy-analytic hierarchy and fuzzy-linear assignment model
Flood is one of the most destructive natural hazards associated with substantial damage in various world regions. In this study, Iran’s Kalan basin...
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Enhancing the performance of tunnel water inflow prediction using Random Forest optimized by Grey Wolf Optimizer
In this research, a groundbreaking intelligent model named the GWO-RF model is introduced for the prediction of water inflow (WI) during tunnel...
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Improving glacio-hydrological model calibration and model performance in cold regions using satellite snow cover data
Hydrological modeling realism is a central research question in hydrological studies. However, it is still a common practice to calibrate...
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A Novel Fusion Forecast Model for Hail Weather in Plateau Areas Based on Machine Learning
In order to improve the accuracy of hail forecasting for mountainous and plateau areas in China, this study presents a novel fusion forecast model...
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Predicting species abundance using machine learning approach: a comparative assessment of random forest spatial variants and performance metrics
For informed decision-making in biodiversity conservation and ecological management, accurate predictions of species abundance are essential. This...
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Study on the influence of input variables on the supervised machine learning model for landslide susceptibility map**
Supervised machine learning (ML) models are currently popular in landslide susceptibility map** (LSM). However, the input variables of these models...
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Toward a Learnable Climate Model in the Artificial Intelligence Era
Artificial intelligence (AI) models have significantly impacted various areas of the atmospheric sciences, resha** our approach to climate-related...
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An innovative method for landslide susceptibility map** supported by fractal theory, GeoDetector, and random forest: a case study in Sichuan Province, SW China
Globally, but especially in Sichuan Province (Southwest China), landslides are considered to be one of the most common geological hazards. The...
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Analysis of rainfall and temperature using deep learning model
The uncertainty of climatic or weather variations makes human adaptation a challenging task. Though lots of techno developments have taken place...
