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Downscaling Seasonal Precipitation Forecasts over East Africa with Deep Convolutional Neural Networks
This study assesses the suitability of convolutional neural networks (CNNs) for downscaling precipitation over East Africa in the context of seasonal...
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Comparative analysis of five convolutional neural networks for landslide susceptibility assessment
To evaluate the performance of deep learning methods on the landslide susceptibility map**, five different convolutional neural networks...
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Rock CT Image Fracture Segmentation Based on Convolutional Neural Networks
Image-based automatic fracture extraction methods have many practical applications in geological and engineering. Fracture identification and...
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Prediction of permeability of porous media using optimized convolutional neural networks
Permeability is an important parameter to describe the behavior of a fluid flow in porous media. To perform realistic flow simulations, it is...
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Real-Time Urban Flood Depth Map**: Convolutional Neural Networks for Pluvial and Fluvial Flood Emulation
The flood-prone city of Zaio in Morocco necessitates a precise, fast, real-time flood depth map** model due to its recurrent flood history. Whether...
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Travel time picking of ambient noise cross-correlation using a deep neural network combining convolutional neural networks and Transformer
The travel time of ambient noise cross-correlation is widely used in geophysics, but traditional methods for picking the travel time of correlation...
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An Urban Road Risk Assessment Framework Based on Convolutional Neural Networks
In contemporary cities, road collapse is one of the most common disasters. This study proposed a framework for assessing the risk of urban road...
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Manganese mineral prospectivity based on deep convolutional neural networks in Songtao of northeastern Guizhou
The world has moved into an era of hidden ore body exploration, necessitating the development of new prospecting and exploration methods. One...
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Parametric elastic full waveform inversion with convolutional neural network
Elastic full waveform inversion (EFWI) is a powerful tool for estimating elastic models by reducing the misfit between multi-component seismic...
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Forecasting seasonal to sub-seasonal rainfall in Great Britain using convolutional-neural networks
Traditional weather forecasting approaches use various numerical simulations and empirical models to produce a gridded estimate of rainfall, often...
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Prediction of stability of a slope with weak layers using convolutional neural networks
Artificial intelligence (AI)-based methods have been widely applied to slope stability assessment, but due to the scarcity of samples, most AI models...
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Landslide susceptibility map** based on landslide classification and improved convolutional neural networks
Based on landslide survey data and geological conditions of the research area, landslide susceptibility map** is to analyze the impact of...
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Predicting the Height of the Hydraulic Fracture Zone Using a Convolutional Neural Network
After analyzing a large amount of related data, five indicators, such as mining thickness and mining depth, were selected as the main factors...
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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...
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Application of Convolutional Neural Networks for Detecting Sea Ice Leads in the Laptev Sea with Landsat-8 Satellite Imagery
AbstractA method for detecting leads in the ice of the Arctic seas from satellite images of the visible range is presented. It is shown that sea ice...
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A comprehensive review of seismic inversion based on neural networks
Seismic inversion is one of the fundamental techniques for solving geophysics problems. To obtain the elastic parameters or petrophysical parameters,...
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Multi-Step-Ahead Monthly Streamflow Forecasting Using Convolutional Neural Networks
Many hydrological applications related to water resource planning and management primarily rely on a succession of streamflow forecasts with...
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Sparse Seismic Data Reconstruction Based on a Convolutional Neural Network Algorithm
At present, the acquisition of seismic data is develo** toward high-precision and high-density methods. However, complex natural environments and...
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An improved dung beetle optimization with recurrent convolutional neural networks for efficient detection and classification of undersea water object images
The exploration of the underwater environment has become increasingly important due to the utilization and development of deep-sea resources in...
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On the use of convolutional neural networks for downscaling daily temperatures over southern South America in a climate change scenario
Global Climate Models (GCMs) depict a notable influence of climate change on southern South America (SSA). Future regional-to-local information for...
