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Machine learning modeling structures and framework for short-term forecasting and long-term projection of Streamflow
Reliable short-term forecasting and long-term projection of streamflow are essential. However, few research models for machine learning structures...
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Stochastic modeling of solar irradiance during hurricanes
The unprecedented growth of solar generation adoption indicates that solar can become a significant source of modern and clean energy for our power...
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Climate risk management in agriculture using alternative electricity and water resources: a stochastic programming framework
Climate change, extreme weather events, and water scarcity have severely impacted the agricultural sector. Under scarce conventional water supplies,...
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Contextual contact tracing based on stochastic compartment modeling and spatial risk assessment
The current situation of COVID-19 highlights the paramount importance of infectious disease surveillance, which necessitates early monitoring for...
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Electric vehicle battery charging framework using artificial intelligence modeling of a small wind turbine based on experimental characterization
The objective of this paper is to develop a generic electric vehicle battery charging framework using wind energy as the direct energy source. A...
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A Multi-model Framework for Streamflow Forecasting Based on Stochastic Models: an Application to the State Of Ceará, Brazil
Reliable long-term (decadal scale) streamflow prediction would provide significant planning information for water resources management, particularly...
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Hourly solar irradiance forecasting based on statistical methods and a stochastic modeling approach for residual error compensation
By reducing fossil fuel use, renewable energy improves the economy, quality of life, and environment. These impacts make renewable energy forecasting...
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A stochastic Bayesian bootstrap** model for COVID-19 data
We provide a stochastic modeling framework for the incidence of COVID-19 in Castilla-Leon (Spain) for the period March 1, 2020 to February 12, 2021,...
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evalPM: a framework for evaluating machine learning models for particulate matter prediction
Air pollution through particulate matter (PM) is one of the largest threats to human health. To understand the causes of PM pollution and enact...
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Modeling spatial dependencies of natural hazards in coastal regions: a nonstationary approach with barriers
Natural hazards like floods, cyclones, earthquakes, or, tsunamis have deep impacts on the environment and society causing damage to both life and...
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Stochastic community assembly of abundant taxa maintains the relationship of soil biodiversity-multifunctionality under mercury stress
• Soil abundant taxa diversity positively related to multifunctionality under Hg stress.
• Microbial network complexity of soil abundant taxa...
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Contaminant source identification in an aquifer using a Bayesian framework with arbitrary polynomial chaos expansion
Stochastic methods are widely used for the identification of contaminant source information. However, these methods suffer from low computational...
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A review on the scaling properties in maximum rainfall marginal distributions: theoretical background, probabilistic modeling, and recent developments
The modeling of sub-daily extreme rainfall has long constituted a challenge for hydrologists in view of the limited availability of data, both in...
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A comparison of numerical approaches for statistical inference with stochastic models
Due to our limited knowledge about complex environmental systems, our predictions of their behavior under different scenarios or decision...
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Hybrid evolutionary algorithm for stochastic multiobjective disassembly line balancing problem in remanufacturing
With the development of the industrial economy and the accelerated renewal of products, many end-of-life products (EOL) have been generated to...
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Stochastic-based approach to quantify the uncertainty of groundwater vulnerability
The study proposes a stochastic approach to quantify the uncertainty of groundwater vulnerability (GV) produced by classical index-overlay methods....
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A hierarchical path-segmentation movement ecology framework
This paper lays out a hierarchical, appropriate-complexity framework for conceptualizing movement-path segments at different spatiotemporal scales in...
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A systematic review of modeling approaches in green supply chain optimization
Over the past decade, the significance of optimizing green supply chain management (GSCM) has gained unprecedented attention from both scholars and...
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Bidirectional machine learning–assisted sensitivity-based stochastic searching approach for groundwater DNAPL source characterization
In this study, we designed a machine learning–based parallel global searching method using the Bayesian inversion framework for efficient...
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Effects of China’s low-carbon policy under stochastic shocks—a multi-agent DSGE model analysis
China has announced a target of achieving carbon peaking by 2030 and carbon neutrality by 2060. Therefore, it is important to assess the economic...
