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Integration of extreme learning machines with CEEMDAN and VMD techniques in the prediction of the multiscalar standardized runoff index and standardized precipitation evapotranspiration index
Accurate prediction of droughts is vital for effectively managing droughts, assessing drought risks and impacts, drought early warning systems,...
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Analysis, characterization, prediction, and attribution of extreme atmospheric events with machine learning and deep learning techniques: a review
Atmospheric extreme events cause severe damage to human societies and ecosystems. The frequency and intensity of extremes and other associated events...
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Prediction of significant wave height using machine learning and its application to extreme wave analysis
Waves of large size can damage offshore infrastructures and affect marine facilities. In coastal engineering studies, it is essential to have the...
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CitrusDiseaseNet: An integrated approach for automated citrus disease detection using deep learning and kernel extreme learning machine
Citrus fruit and leaf diseases pose a significant threat to citrus production worldwide, leading to substantial yield declines and economic losses....
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Rainfall prediction using optimally pruned extreme learning machines
Rainfall impacts local water quantity and quality. Accurate and timely prediction of rainfall is highly desirable in water management and...
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A SMOTified extreme learning machine for identifying mineralization anomalies from geochemical exploration data: a case study from the Yeniugou area, **njiang, China
Extreme learning Machine (ELM) is a novel supervised machine learning algorithm, which has the advantages of fast-learning speed, good...
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Rockburst Prediction and Evaluation Model for Hard Rock Engineering Based on Extreme Gradient Boosting Ensemble Learning and SHAP Value
Rockburst prediction is the basis of rockburst prevention and construction guidance. However, the complexity of the rock burst occurrence mechanism...
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Modeling triangular, rectangular, and parabolic weirs using weighted robust extreme learning machine
In this study, dimensionless parameters influencing the coefficient of discharge (COD) are found and four different WRELM models are developed. After...
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Evaluation of discharge coefficient of triangular side orifices by using regularized extreme learning machine
The present paper attempts to reproduce the discharge coefficient (DC) of triangular side orifices by a new training approach entitled “Regularized...
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Estimating discharge coefficient of side weirs in trapezoidal and rectangular flumes using outlier robust extreme learning machine
Using the outlier robust extreme learning machine (ORELM) method, the discharge coefficient of side weirs placed on rectangular and trapezoidal...
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A wavelet-outlier robust extreme learning machine for rainfall forecasting in Ardabil City, Iran
In this paper, the monthly long-term precipitation of the city of Ardabil from 1976 to 2020 is simulated by a modern hybrid learning machine. To this...
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A novel prediction method for coalbed methane production capacity combined extreme gradient boosting with bayesian optimization
Coalbed methane plays a significant role for the sustainable utilizing of resources and ecological environment. Production capacity forecasting of...
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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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A new approach to dividing the tectonic setting of igneous rocks: machine learning and GeoTectAI software
For a long time, elucidating the tectonic setting of unknown rock samples has been a focal point for geologists. Traditional methodologies for this...
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Machine learning approach for GNSS geodetic velocity estimation
This study aimed to investigate the performance of machine learning (ML) algorithms in determining horizontal velocity at specific points using the...
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Evaporation Prediction with Wavelet-Based Hyperparameter Optimized K-Nearest Neighbors and Extreme Gradient Boosting Algorithms in a Semi-Arid Environment
The study aims to reveal which mother wavelet type performs best in evaporation prediction. This study used a hybrid algorithm that combined...
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Climate Change Through Quantum Lens: Computing and Machine Learning
Quantum computing (QC) is a new approach to perform computations using the principles of quantum mechanics. The demonstration of quantum superiority...
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Application of machine learning to the Vs-based soil liquefaction potential assessment
Earthquakes can cause violent liquefaction of the soil, resulting in unstable foundations that can cause serious damage to facilities such as...
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Urmia lake water depth modeling using extreme learning machine-improved grey wolf optimizer hybrid algorithm
Lake water level changes are relatively sensitive to the climate-born events that rely on numerous phenomena, e.g., surface soil type, adjacent...
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Predicting and analyzing flood susceptibility using boosting-based ensemble machine learning algorithms with SHapley Additive exPlanations
In recent years, the number of floods around the world has increased. As a result, Flood Susceptibility Maps (FSMs) became vital for flood...
