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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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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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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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Comparison of machine learning algorithms for slope stability prediction using an automated machine learning approach
Evaluation of slope failures, which cause significant loss of life and property comparable to natural disasters such as earthquakes, floods and...
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Modeling long-term rainfall-runoff time series through wavelet-weighted regularization extreme learning machine
As one of the most critical points of Iran, Lake Urmia has always been subjected to ecosystem changes due to severe water level drops. Many basins...
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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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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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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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A novel approach to estimate rock deformation under uniaxial compression using a machine learning technique
Understanding rock deformation is crucial for various engineering and geological applications, including mining, tunneling, and earthquake...
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Integrating Machine Learning Models with Comprehensive Data Strategies and Optimization Techniques to Enhance Flood Prediction Accuracy: A Review
The occurrence of natural disasters, accelerated by climate change, has become a continuous menace to the environment and consequently impacts the...
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How does extreme point sampling affect non-extreme simulation in geographical random forest?
Spatial heterogeneity brings numerous uncertainties to training datasets in the modeling process. An arbitrary selection of training samples can...
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Investigating the Role of the Key Conditioning Factors in Flood Susceptibility Map** Through Machine Learning Approaches
This study harnessed the formidable predictive capabilities of three state-of-the-art machine learning models—extreme gradient boosting (XGB), random...
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Landslide susceptibility map** and sensitivity analysis using various machine learning models: a case study of Beas valley, Indian Himalaya
Landslide is one of the most destructive hazards in the Upper Beas valley of the Himalayan region of India. Landslide susceptibility map** is an...
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Drought Forecasting of Seyhan and Ceyhan Basins Using Machine Learning Methods
AbstractA drought is a prolonged natural disaster with numerous economic, social, and environmental consequences; it occurs when the natural water...
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Machine Learning Analysis of Impact of Western US Fires on Central US Hailstorms
Fires, including wildfires, harm air quality and essential public services like transportation, communication, and utilities. These fires can also...
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Interpolation Problem on Outlier Contaminated Seismogram Using Extreme Learning Machine
In this work, we present a weighted l1 norm-based Extreme Learning Machine (ELM), namely, enhanced Regularized ELM (eRELM) for regression problems...
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Classification machine learning models for urban flood hazard map**: case study of Zaio, NE Morocco
Floods have become increasingly frequent and devastating in recent decades, posing unignorable risks as highly destructive natural hazards. To...
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A Comprehensive Machine and Deep Learning Approach for Aerosol Optical Depth Forecasting: New Evidence from the Arabian Peninsula
Accurate forecasting of environmental pollution indicators holds significant importance in diverse fields, including climate modeling, environmental...
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Hyperparameters’ role in machine learning algorithm for modeling of compressive strength of recycled aggregate concrete
RAC is a kind of concrete made from Recycled Concrete Aggregates instead of natural aggregates. The use of RAC has been popular in recent years due...
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A machine learning method for distinguishing detrital zircon provenance
Zircon geochemistry provides a sensitive monitor of its parental magma composition. However, due to the complexity of the uptake of trace elements...
