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Spatio-temporal estimation of wind speed and wind power using extreme learning machines: predictions, uncertainty and technical potential
With wind power providing an increasing amount of electricity worldwide, the quantification of its spatio-temporal variations and the related...
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Snow avalanche susceptibility map** using novel tree-based machine learning algorithms (XGBoost, NGBoost, and LightGBM) with eXplainable Artificial Intelligence (XAI) approach
This study examines the use of snow avalanche susceptibility maps (SASMs) to identify areas prone to avalanches and develop measures to mitigate the...
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Machine Learning and Fuzzy Technique for Environmental Time Series Analysis
In this chapter, we will revise the application of machine learning techniques in some Environmental Time Series problems. In Particular, we will...
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A hybrid framework for forecasting monthly reservoir inflow based on machine learning techniques with dynamic climate forecasts, satellite-based data, and climate phenomenon information
In this study, we developed and evaluated a hybrid framework for reservoir inflow forecast. This framework is unprecedented, which integrates new...
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Modeling and predicting city-level CO2 emissions using open access data and machine learning
Globally, urban has been the major contributor to greenhouse gas (GHG) emissions and thus plays an increasingly important role in its efforts to...
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Generating 250 m-resolution regional NO2 concentration products first from MODIS retrievals using extreme gradient boosting
Surface Nitrogen dioxide (NO 2 ) is highly related to multiple adverse human health and environmental effects. The current satellite-derived surface NO 2 ...
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Evolution of neural network to deep learning in prediction of air, water pollution and its Indian context
The scenario of developed and develo** countries nowadays is disturbed due to modern living style which affects environment, wildlife and natural...
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Spatial distribution pattern and health risk of groundwater contamination by cadmium, manganese, lead and nitrate in groundwater of an arid area
Combining the results of base models to create a meta-model is one of the ensemble approaches known as stacking . In this study, stacking of five base...
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Predicting ecological footprint based on global macro indicators in G-20 countries using machine learning approaches
Paying attention to human activities in terms of land grazing infrastructure, crops, forest products, and carbon impact, the so-called ecological...
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High-resolution estimation of ambient sulfate concentration over Taiwan Island using a novel ensemble machine-learning model
Heavy loadings of sulfate aerosol trigger haze formation and pose great damage to human health in Taiwan Island. Nevertheless, high-resolution...
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Multi-phase hybrid bidirectional deep learning model integrated with Markov chain Monte Carlo bivariate copulas function for streamflow prediction
In recent years, deep learning (DL) approaches have been proven effective in addressing high nonlinear relationships within complex systems. Although...
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Deep Learning and Its Environmental Applications
The rapid development of technologies brings a new set of challenges and difficulties in scientific studies and research in different fields. These...
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Artificial intelligence methods for modeling gasification of waste biomass: a review
Gasification is a highly promising thermochemical process that shows considerable potential for the efficient conversion of waste biomass into...
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Stepwise extreme learning machine for statistical downscaling of daily maximum and minimum temperature
Increasing temperature from climate change can bring a number of different risks such as more droughts and heat waves, and increasing sea level rise....
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Optimization algorithms as training approach with hybrid deep learning methods to develop an ultraviolet index forecasting model
The solar ultraviolet index (UVI) is a key public health indicator to mitigate the ultraviolet-exposure related diseases. This study aimed to develop...
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Identification of the best model to predict optical properties of water
This research studies prediction models for optical water properties, which are very important to save time consumption to easy control of irrigation...
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Analyzing the effects of solar energy innovations, digitalization, and economic globalization on environmental quality in the United States
The escalating apprehension regarding climate change mitigation has intensified the quest for energy alternatives that are low in carbon emissions,...
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Prediction of groundwater nitrate concentration in a semiarid region using hybrid Bayesian artificial intelligence approaches
Nitrate is a major pollutant in groundwater whose main source is municipal wastewater and agricultural activities. In the present study, Bayesian...
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Mobile monitoring and spatial prediction of black carbon in Cairo, Egypt
This study harnesses the power of mobile data in develo** a spatial model for predicting black carbon (BC) concentrations within one of the most...
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Extended decomposition ensemble framework based on full data analysis and optimized combination with relaxed boundary for carbon price forecasting
The carbon price forecasting is a challenging and meaningful task which can help investors, manufacturers, and policymakers to make decisions. The...
