Applications of Bayesian belief networks in water resource management: A systematic review

Phan, Thuc D.; Smart, James C. R.; Capon, Samantha J.; Hadwen, Wade L.; Sahin, Oz

doi:10.1016/j.envsoft.2016.08.006

Cited by 121 publications

(93 citation statements)

References 117 publications

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“…CPTs, which are marginal probability distributions, are often parameterized and calculated using approaches based on Monte Carlo simulation (Phan et al, 2016), Gibbs sampling or dynamic discretization (Nojavan, Qian, & Stow, 2017;Pérez-Miñana, 2016), maximum likelihood or the Laplace correction (Aguilera et al, 2011), or the expectation maximization algorithm for small and incomplete datasets and the gradient learning algorithm for large incomplete datasets and continuous data (McDonald et al, 2015). CPTs, which are marginal probability distributions, are often parameterized and calculated using approaches based on Monte Carlo simulation (Phan et al, 2016), Gibbs sampling or dynamic discretization (Nojavan, Qian, & Stow, 2017;Pérez-Miñana, 2016), maximum likelihood or the Laplace correction (Aguilera et al, 2011), or the expectation maximization algorithm for small and incomplete datasets and the gradient learning algorithm for large incomplete datasets and continuous data (McDonald et al, 2015).…”

Section: Bayesian Belief Networkmentioning

confidence: 99%

“…There are recent reviews covering BBN applications in areas such as environmental modelling (Aguilera et al, 2011), ecosystem services modelling (Landuyt et al, 2013;Pérez-Miñana, 2016), ecological risk assessment (McDonald et al, 2015), and water resources management (Phan et al, 2016), which also include some early studies related to stream health modelling Marcot, Holthausen, Raphael, Rowland, & Wisdom, 2001). The aforementioned reviews addressed different aspects of BBN model development/application such as data preprocessing, complexity, training, optimization, validation methods, variations and extensions (e.g., dynamic Bayesian networks for time series modelling and hidden Markov models for higher order relationships; Tucker & Duplisea, 2012), integration with other modelling techniques, and software comparison.…”

Section: Bayesian Belief Networkmentioning

confidence: 99%

“…Probability distributions are usually defined for each node in terms of the states (i.e., conditional probability tables [CPTs]; McCann et al, 2006). Consequently, a BBN is composed of a qualitative component referring to the network structure and a quantitative component given by the CPTs within each node (Phan, Smart, Capon, Hadwen, & Sahin, 2016).…”

Section: Bayesian Belief Networkmentioning

confidence: 99%

See 2 more Smart Citations

A review of macroinvertebrate‐ and fish‐based stream health modelling techniques

Hernandez‐Suarez

Nejadhashemi

2018

Ecohydrology

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During the last three decades, explaining cause-effect relationships between natural and anthropogenic disturbances with measures of stream health have motivated the growing application of statistical, machine learning, and soft computing methods.The aim of this review is to provide insight into the most widely used methods for predicting biological variables based on macroinvertebrate and fish species in riverine ecosystems. Therefore, we describe several methods including multiple linear regression, generalized linear models, generalized additive models, boosted regression trees, random forests, artificial neural networks, fuzzy logic-based, and Bayesian belief networks along with recent applications of these. Moreover, issues regarding variable selection, model interpretability, ensemble modelling, and model evaluation and overfitting are discussed. Recent advances have suggested the need for integrated modelling systems to enhance the predictive ability and improve interpretability.However, trade-offs between model complexity and accuracy demand research efforts in uncertainty quantification/propagation in model ensembles. Additionally, models should be perceived as complementary tools that require further validation with field measurements. Therefore, a consensus regarding monitoring and modelling practices for stream health applications is recommended.

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Section: Bayesian Belief Networkmentioning

confidence: 99%

Section: Bayesian Belief Networkmentioning

confidence: 99%

Section: Bayesian Belief Networkmentioning

confidence: 99%

See 1 more Smart Citation

A review of macroinvertebrate‐ and fish‐based stream health modelling techniques

Hernandez‐Suarez

Nejadhashemi

2018

Ecohydrology

View full text Add to dashboard Cite

show abstract

“…Bayesian modeling is applicable for prediction, risk analysis, diagnosis, monitoring, reliability, and dependency [44] and it can act as a decision support system in government [45][46][47][48]. A Bayesian Network (BN) was developed to analyze the probabilistic causal relationship between the DPS components and actualize the study models.…”

Section: Modeling Toolmentioning

confidence: 99%

Model Prediction of Secondary Soil Salinization in the Keriya Oasis, Northwest China

Seydehmet

Nurmemet

et al. 2018

Sustainability

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Significant anthropogenic and biophysical changes have caused fluctuations in the soil salinization area of the Keriya Oasis in China. The Driver-Pressure-State-Impact-Response (DPSIR) sustainability framework and Bayesian networks (BNs) were used to integrate information from anthropogenic and natural systems to model the trend of secondary soil salinization. The developed model predicted that light salinization (vegetation coverage of around 15-20%, soil salt 5-10 g/kg) of the ecotone will increase in the near term but decelerate slightly in the future, and that farmland salinization will decrease in the near term. This trend is expected to accelerate in the future. Both trends are attributed to decreased water logging, increased groundwater exploitation, and decreased ratio of evaporation/precipitation. In contrast, severe salinization (vegetation coverage of around 2%, soil salt ≥20 g/kg) of the ecotone will increase in the near term. This trend will accelerate in the future because decreased river flow will reduce the flushing of severely salinized soil crust. Anthropogenic factors have negative impacts and natural causes have positive impacts on light salinization of ecotones. In situations involving severe farmland salinization, anthropogenic factors have persistent negative impacts.

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“…The mechanism models are based on the hydrodynamic model and water quality equations in order to simulate the diffusion and attenuation process of pollutants [6][7][8]. However, due to the wide variety of influencing factors and the cognitive limitations of the real water quality, this makes the model construction and parameter determination difficult [9][10][11]. Therefore, this indicates that the simulation effect of the nature river is better than that of the urban river, which is affected by human activities.…”

Section: Introductionmentioning

confidence: 99%

Combined Exceedance Probability Assessment of Water Quality Indicators Based on Multivariate Joint Probability Distribution in Urban Rivers

Liu¹,

Cheng²,

Zhang³

et al. 2018

Water

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Abstract:Discharge and water quality are two important attributes of rivers, although the joint response relationship between discharge and multiple water quality indicators is not clear. In this paper, the joint probability distributions are established by copula functions to reveal the statistical characteristics and occurrence probability of different combinations of discharge and multiple water quality indicators. Based on the data of discharge, ammonia nitrogen content index (NH 4 + ) and permanganate index (COD Mn ) in the Xiaoqing River in Jinan, we first tested the joint change-point with the data from 1980-2016, before we focused on analyzing the data after the change-point and established the multivariate joint probability distributions. The results show that the Gaussian copula is more suitable for describing the joint distribution of discharge and water quality, while the year of 2005 is a joint change-point of water quantity and quality. Furthermore, it is more reasonable to use the trivariate joint probability distribution as compared to the bivariate distributions to reflect the exceedance probability of water quality combination events under different discharge conditions. The research results can provide technical support for the water quality management of urban rivers.

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Applications of Bayesian belief networks in water resource management: A systematic review

Cited by 121 publications

References 117 publications

A review of macroinvertebrate‐ and fish‐based stream health modelling techniques

A review of macroinvertebrate‐ and fish‐based stream health modelling techniques

Model Prediction of Secondary Soil Salinization in the Keriya Oasis, Northwest China

Combined Exceedance Probability Assessment of Water Quality Indicators Based on Multivariate Joint Probability Distribution in Urban Rivers

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