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A New Data-Driven Model to Predict Monthly Runoff at Watershed Scale: Insights from Deep Learning Method Applied in Data-Driven Model
Accurate forecasting of mid to long-term runoff is essential for water resources management. However, the traditional model cannot predict well and...
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Paleontology Knowledge Graph for Data-Driven Discovery
A knowledge graph (KG) is a knowledge base that integrates and represents data based on a graph-structured data model or topology. Geoscientists have...
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Workflow-Induced Uncertainty in Data-Driven Mineral Prospectivity Map**
The primary goal of mineral prospectivity map** (MPM) is to narrow the search for mineral resources by producing spatially selective maps. However,...
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Data-driven approaches for estimation of sediment discharge in rivers
Sediment discharge in rivers is among the most important water and environmental engineering issues. The present study employed the published...
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Special Issue: Data-Driven Discovery in Geosciences: Opportunities and Challenges
With the rapid expansion in big data and artificial intelligence (AI), Earth sciences are undergoing unprecedented advances in data processing and...
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Local climate regionalization of the Tibetan Plateau: A data-driven scale-dependent analysis
The Tibetan Plateau (TP) has experienced an overall rapid warming and moistening; however, the knowledge of TP climate regionalization and of its...
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Data-driven modelling with coarse-grid network models
We propose to use a conventional simulator, formulated on the topology of a coarse volumetric 3D grid, as a data-driven network model that seeks to...
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A novel data-driven dynamical model for predicting future climate trends
Climate change, driven by greenhouse gas emissions and rising temperatures, poses a significant environmental, societal, and economic threat....
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Data-Driven Mineral Prospectivity Map** Based on Known Deposits Using Association Rules
Recently, machine learning methods have been utilized to mine correlations between geological variables and mineral deposits because of their...
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Ontology-driven relational data map** for constructing a knowledge graph of porphyry copper deposits
Geoscience knowledge graph has become a popular topic in recent years. A series of studies have been reported to introduce the construction and...
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Data-driven ensemble model for probabilistic prediction of debris-flow volume using Bayesian model averaging
Accurate and reliable predictions of the debris-flow volume are the necessary prerequisite for potential hazard delineation and risk assessment of...
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A Framework for Data-Driven Mineral Prospectivity Map** with Interpretable Machine Learning and Modulated Predictive Modeling
Although mineral prospectivity modeling (MPM) has undergone decades of development, it has not yet been widely adopted in the global mineral...
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Assessments of Data-Driven Deep Learning Models on One-Month Predictions of Pan-Arctic Sea Ice Thickness
In recent years, deep learning methods have gradually been applied to prediction tasks related to Arctic sea ice concentration, but relatively little...
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A data-driven approach for regional-scale fine-resolution disaster impact prediction under tropical cyclones
Tropical cyclones (TCs) pose a significant threat to coastal regions worldwide, demanding accurate and timely predictions of potential disaster...
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Data-Driven Earthquake Multi-impact Modeling: A Comparison of Models
In this study, a broad range of supervised machine learning and parametric statistical, geospatial, and non-geospatial models were applied to model...
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Assessment of data-driven models for estimating total sediment discharge
Estimating total sediment discharge is challenging. This study aims to assess performances of various data-driven models including empirical...
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Pan evaporation forecasting using empirical and ensemble empirical mode decomposition (EEMD) based data-driven models in the Euphrates sub-basin, Turkey
Forecasting evaporation, an important variable in the hydrological cycle, is crucial for managing water resources and taking precautions against...
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A regionalized partially nonergodic ground-motion data driven model for low to moderate seismicity areas: using RESIF-RAP, ESM, RESORCE and NGA-West2 data
The aim of this work is to develop a regionalized partially nonergodic data driven ground-motion model (R-GMM) that provides functional forms (ffs)...
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Disaster map** and assessment of Pakistan’s 2022 mega-flood based on multi-source data-driven approach
Climate change-induced mega-floods have become increasingly frequent worldwide. The rapid map** and assessment of flood disasters pose urgent...
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Regional flood frequency analysis using data-driven models (M5, random forest, and ANFIS) and a multivariate regression method in ungauged catchments
Flooding is recognized worldwide joined of the most expensive natural hazards. To adopt proper structural and nonstructural measurements for...
