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Flood and Non-Flood Image Classification using Deep Ensemble Learning
Floods are one of the most frequent natural disasters, often resulting in widespread devastation. Identifying floods accurately is crucial for...
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Enhancing stormwater network overflow prediction: investigation of ensemble learning models
This study addresses the critical issue of urban flooding caused by stormwater network overflow, necessitating unified and efficient management...
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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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Probabilistic slope stability analysis using subset simulation enhanced by ensemble machine learning techniques
Within the field of geotechnical engineering, complex challenges arise due to uncertainties associated with variable loads, soil properties, ground...
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Intelligent regional subsurface prediction based on limited borehole data and interpretability stacking technique of ensemble learning
This study introduces an intelligent method for regional subsurface prediction using a Stacking ensemble learning approach, which incorporates...
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Application of hybrid machine learning-based ensemble techniques for rainfall-runoff modeling
The main aim of this study was to develop hybrid machine learning (ML)-based ensemble modeling of the rainfall-runoff process in the Katar catchment,...
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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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Incremental–decremental data transformation based ensemble deep learning model (IDT-eDL) for temperature prediction
Human life heavily depends on weather conditions, which affect the necessary operations like agriculture, aviation, tourism, industries, etc., where...
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An ensemble deep learning approach for air quality estimation in Delhi, India
South Asian megacities, notably Delhi, are significant contributors to air pollution, driven by factors such as population density, vehicular...
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Earthquake prediction from seismic indicators using tree-based ensemble learning
Earthquake prediction is a challenging research area, but the use of a variety of machine learning models, together with a range of seismic...
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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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Rockburst Intensity Grade Prediction Based on Data Preprocessing Techniques and Multi-model Ensemble Learning Algorithms
Rockburst is a mine dynamic disaster caused by the rapid release of elastic strain energy of surrounding rock. As the depth of engineering project...
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Application of a weighted ensemble forecasting method based on online learning in subseasonal forecast in the South China
Under the proposal of “seamless forecasting”, it has become a key problem for meteorologists to improve the skills of subseasonal forecasts. Since...
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Debris-flow susceptibility assessment in Dongchuan using stacking ensemble learning including multiple heterogeneous learners with RFE for factor optimization
An accurate assessment of debris-flow susceptibility is of great importance to the prevention and control of debris-flow disasters in mountainous...
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A comparative analysis of ensemble learning algorithms with hyperparameter optimization for soil liquefaction prediction
Accurate prediction of soil liquefaction potential is crucial for evaluating the stability of structures in earthquake regions. This study focuses on...
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Flood map** based on novel ensemble modeling involving the deep learning, Harris Hawk optimization algorithm and stacking based machine learning
Among the various natural disasters that take place around the world, flood is considered to be the most extensive. There have been several floods in...
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Prediction of production rate of surface miner in coal mine: an application of single and ensemble machine learning methods
Surface miner is an eco-friendly excavation machine which is gradually replacing the traditional drilling and blasting method of excavation. Hence,...
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Meta ensemble learning in geospatial sentiment analysis and community survey map**: a water supply case study
Amidst the proliferation of social media and online platforms, sentiment analysis stands out as a pivotal tool in Natural Language Processing (NLP),...
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Two-Speed Deep-Learning Ensemble for Classification of Incremental Land-Cover Satellite Image Patches
High-velocity data streams present a challenge to deep learning-based computer vision models due to the resources needed to retrain for new...
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Uncertainty analysis method of slope safety factor based on quantile-based ensemble learning
To overcome the problem that the point prediction method of slope safety factor has uncertainty in its prediction and hence cannot a reliable slope...
