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Examining site intervention efficacy and uncertainties with conceptual Bayesian networks: preventing offsite migration of DNAPL and contaminated groundwater
For contaminated sites, conceptual site models (CSMs) guide the assessment and management of risks, including remediation strategies. Recent research...
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Using Bayesian Belief Networks to Investigate Farmer Behavior and Policy Interventions for Improved Nitrogen Management
Increasing farmers’ adoption of sustainable nitrogen management practices is crucial for improving water quality. Yet, research to date provides...
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Quantitative association between lead exposure and amyotrophic lateral sclerosis: a Bayesian network-based predictive study
BackgroundEnvironmental lead (Pb) exposure have been suggested as a causative factor for amyotrophic lateral sclerosis (ALS). However, the role of Pb...
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Game-theoretic optimization of landslide susceptibility map**: a comparative study between Bayesian-optimized basic neural network and new generation neural network models
Landslide susceptibility map** is essential for reducing the risk of landslides and ensuring the safety of people and infrastructure in...
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Evaluating the influence of road construction on landslide susceptibility in Saudi Arabia’s mountainous terrain: a Bayesian-optimised deep learning approach with attention mechanism and sensitivity analysis
In the mountainous region of Asir region of Saudi Arabia, road construction activities are closely associated with frequent landslides, posing...
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Federated Bayesian network approach for cross-regional air pollution classification: a case study of the Bei**g–Tian**–Hebei region
Although machine learning methods have enabled considerable progress in air quality assessment, challenges persist regarding data privacy,...
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An application of Bayesian vector heterogeneous autoregressions to study network interlinkages of the crude oil and gold, stock, and cryptocurrency markets during the COVID-19 outbreak
We investigate fat tails and network interconnections of crude oil, gold, stock, and cryptocurrency using seven Bayesian vector heterogeneous...
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A novel model for rainfall prediction using hybrid stochastic-based Bayesian optimization algorithm
Rainfall forecasting is considered one of the key concerns in the meteorological department because it is related strongly to social as well as...
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Fat tails, serial dependence, and interlinkages of the geopolitical risk and food market during the COVID-19 pandemic and war crisis: an application of Bayesian vector heterogeneous autoregressions
We investigate fat tails and network interconnections of geopolitical risk index and food prices, including the price of corn, rice, and wheat, using...
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A benchmark-based method for evaluating hyperparameter optimization techniques of neural networks for surface water quality prediction
Neural networks (NNs) have been used extensively in surface water prediction tasks due to computing algorithm improvements and data accumulation. An...
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Source identification in river pollution incidents using a cellular automata model and Bayesian Markov chain Monte Carlo method
Identification of contaminant sources in rivers is crucial for river protection and emergency response. This study presents an innovative approach...
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A Bayesian machine learning approach for spatio-temporal prediction of COVID-19 cases
Modeling the spread of infectious diseases in space and time needs to take care of complex dependencies and uncertainties. Machine learning methods,...
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Entropy-based air quality monitoring network optimization using NINP and Bayesian maximum entropy
Effectual air quality monitoring network (AQMN) design plays a prominent role in environmental engineering. An optimal AQMN design should consider...
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Identification of pollution sources in rivers using a hydrodynamic diffusion wave model and improved Bayesian-Markov chain Monte Carlo algorithm
Water quality restoration in rivers requires identification of the locations and discharges of pollution sources, and a reliable mathematical model...
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Tracing the dominant sources of sediment flowing towards Lake Victoria using geochemical tracers and a Bayesian mixing model
PurposeLake Victoria has been increasingly silting over the past decades, impacting water quality and loss of biodiversity. Sediment control...
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Strategies to overcome challenges to smart sustainable logistics: a Bayesian-based group decision-making approach
The logistics sector has seen rapid growth in the past few years due to globalization and the rise in demand for goods and commodities. With the...
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Protective consumption behavior under smog: using a data-driven dynamic Bayesian network
In the midst of the deteriorating air pollution and collective stress, people pay close attention to risk mitigation measures such as kee** indoor...
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Simulation of the projected river flow changes using integrated downscaling and Bayesian optimization-tuned kernel-based models
In terms of having a comprehensive vision toward investigating the impact of future climate changes, LARS-WG, a weather generator model, was employed...
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Use of a Bayesian network as a decision support tool for watershed management: a case study in a highly managed river-dominated estuary
Decision making in water resource management has many dimensions including water supply, flood protection, and meeting ecological needs, therefore,...
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Towards assessing the importance of individual stations in hydrometric networks: application of complex networks
Optimal design of hydrometric networks has been a long-standing problem in hydrology. Evaluation of the importance (or influence) of the individual...
