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Bayesian modeling for pro-environmental behavior data: sorting and selecting relevant variables
Pro-environmental behaviors towards climate change can be measured and evaluated in different fields. Typically, surveys are the standard tool for...
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Convolutional graph neural networks-based research on estimating heavy metal concentrations in a soil-rice system
Estimating heavy metal concentrations in soil-rice systems is of great significance to identify the factors controlling heavy metal transfer in...
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A Bayesian network approach for understanding the role of large-scale and local hydro-meteorological variables as drivers of basin-scale rainfall and streamflow
The present study examines the role of large-scale climate modes and local hydro-meteorological variables, at 1–6 months lead, as drivers of...
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Modeling of agricultural soil compaction using discrete Bayesian networks
The intensive use of machinery in the agricultural area can have serious effects on the soil quality causing its compaction and changes in the...
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Explainable Prediction of Compressive Strength and Elastic Modulus for Concrete Containing Waste Foundry Sand Using Bayesian-Optimized XGBoost with 10-Fold Cross-Validation
Waste foundry sand (WFS), generated by the metal casting industry, is commonly disposed of in landfills, posing a significant environmental hazard....
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High-risk clones of Pseudomonas aeruginosa contaminate the drinking water networks of French cities
Pseudomonas aeruginosa is a major opportunistic pathogen responsible for severe infections in immunocompromised patients. The contamination of...
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Decoupling pollution-agricultural growth and predicting climate change impacts on decoupling index using Bayesian network in different climatic regions
Applying the principles of healthy products through agriculture practices has become an important issue due to significant environmental impacts of...
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A hybrid Bayesian BWM and Pythagorean fuzzy WASPAS-based decision-making framework for parcel locker location selection problem
One of the main causes of the significant commercial vehicle traffic in the city region is last-mile deliveries. Parcel lockers, which are one of...
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Estimation of ecological footprint based on tourism development indicators using neural networks and multivariate regression
The ecological footprint has attracted a lot of attention in the top tourism destination countries, and this issue may be worrying. This study aims...
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Towards facing uncertainties in biofuel supply chain networks: a systematic literature review
Biofuel supply chains (BSCs) face diverse uncertainties that pose serious challenges. This has led to an expanding body of research focused on...
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Clustering constrained on linear networks
An unsupervised classification method for point events occurring on a geometric network is proposed. The idea relies on the distributional...
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An innovative approach for predicting pandemic hotspots in complex wastewater networks using graph theory coupled with fuzzy logic
Early prediction of COVID-19 infected communities (potential hotspots) is essential to limit the spread of virus. Diagnostic testing has limitations...
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Multi-watershed nonpoint source pollution management through coupling Bayesian-based simulation and mechanism-based effluent trading optimization
Multiple rivers flowing into the same bay can be correlated in water quality management and together determine the environmental status of the bay....
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Application of information fusion techniques and satellite products in the optimal redesign of rain gauge networks
Rain gauge networks are usually redesigned to improve the accuracy of spatial and temporal estimates and reduce the monitoring costs. In addition to...
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A Bayesian network approach for determining optimal ecological base flow of rivers in water shortage areas of Northwest China
How to balance natural river base flow with water loss from agricultural activities such as irrigation has become an important global challenge in...
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Feature Selection for Rainfall Prediction and Drought Assessment Using Bayesian Network Technique
This study explores the potential of Bayesian Network (BN), which is a class of Graphical Modeling (GM) as a feature selection technique for...
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An innovative framework for real-time monitoring of pollutant point sources in river networks
Simultaneous identification of the location and release history of pollutant sources in river networks is an ill-posed and complicated problem,...
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Improved prediction of daily pan evaporation using Bayesian Model Averaging and optimized Kernel Extreme Machine models in different climates
Evaporation is one of the most important parameters of meteorological science. Therefore, predicting evaporation is necessary for both water...
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Anomaly detection in groundwater monitoring data using LSTM-Autoencoder neural networks
Groundwater monitoring data can be prone to errors and biases due to various factors like borehole and equipment malfunctions, or human mistakes....
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Improving the ARIMA Model Prediction for Water Quality Parameters of Urban Water Distribution Networks (Case Study: CANARY Dataset)
Water distribution networks are susceptible to pollutants entering the system. Continuous monitoring of water quality parameters in distribution...
