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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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Improved forecasting via physics-guided machine learning as exemplified using “21·7” extreme rainfall event in Henan
As a natural disaster, extreme precipitation is among the most destructive and influential, but predicting its occurrence and evolution accurately is...
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A hybrid groundwater level prediction model using signal decomposition and optimised extreme learning machine
The estimation and prediction of groundwater levels (GWLs) are key to water resource management and directly linked to the socio-economic growth of...
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A new intelligence model for evaluating clay compressibility in soft ground improvement: a combined approach of bees optimization and extreme learning machine
This study investigated the compressibility of clay ( C c ) for soft ground improvement and developed six optimized metaheuristic-based extreme learning...
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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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Novel residual hybrid machine learning for solar activity prediction in smart cities
Predicting global solar activity is crucial for smart cities, especially for space activities, communication industries, and climate change...
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Petrophysical log-driven kerogen ty**: unveiling the potential of hybrid machine learning
The importance of characterizing kerogen type in evaluating source rock and the nature of hydrocarbon yield is emphasized. However, traditional...
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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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Sediment load prediction in Johor river: deep learning versus machine learning models
Sediment transport is a normal phenomenon in rivers and streams, contributing significantly to ecosystem production and preservation by replenishing...
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Understanding evacuation behavior for effective disaster preparedness: a hybrid machine learning approach
This paper delves into the pivotal role of machine learning in responding to natural disasters and understanding human behavior during crises....
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Skillful prediction of boreal winter-spring seasonal precipitation in Southern China based on machine learning approach and dynamical ENSO prediction
El Niño-Southern Oscillation (ENSO) and the antisymmetric combination mode (C-mode) have a significant impact on the seasonal precipitation in...
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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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Comparative analysis of the TabNet algorithm and traditional machine learning algorithms for landslide susceptibility assessment in the Wanzhou Region of China
Landslides, widespread and highly dangerous geological disasters, pose significant risks to humankind and the ecological environment. Consequently,...
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Groundwater level forecasting in Northern Bangladesh using nonlinear autoregressive exogenous (NARX) and extreme learning machine (ELM) neural networks
Groundwater resources (GWR) are vital to agricultural crop production, everyday life, and economic development. As a result, accurate groundwater...
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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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Machine learning model combined with CEEMDAN algorithm for monthly precipitation prediction
Accurate forecasting of monthly precipitation is of great significance for national production, disaster prevention and mitigation, and water...
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The most suitable mode decomposition technique for machine learning in meteorological time series prediction
To predict the most suitable mode decomposition technique for machine learning in meteorological time series prediction, this study has been carried...
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Analysis of the characteristics and environmental benefits of rice husk ash as a supplementary cementitious material through experimental and machine learning approaches
Rising cement consumption over the past few decades has become a huge environmental concern since Ordinary Portland Cement emits huge amounts of...
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Improved prediction of monthly streamflow in a mountainous region by Metaheuristic-Enhanced deep learning and machine learning models using hydroclimatic data
This study compares the ability of Long Short-Term Memory (LSTM) tuned with Grey Wolf Optimization (GWO) and machine learning models, artificial...
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Estimation of Unconfined Aquifer Transmissivity Using a Comparative Study of Machine Learning Models
Groundwater management is key to attaining sustainable development goals, especially in arid and semi-arid countries. Hence, a precise estimate of...
