2016
DOI: 10.1002/env.2432
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Applications of hybrid dynamic Bayesian networks to water reservoir management

Abstract: Bayesian networks (BNs) have been widely applied in environmental modelling to predict the behavior of an ecosystem under conditions of change. However, this approximation doesn't take time into consideration. To solve this issue, an extension of BNs, the dynamic Bayesian networks (DBNs), has been developed in mathematics and computer science areas but has scarcely been applied in environmental modelling. This paper presents the application of DBN to water reservoir systems in Andalusia, Spain. The aim is to p… Show more

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Cited by 10 publications
(4 citation statements)
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“…A high proportion of these studies have sought to understand climatic and/or non-climatic drivers and their impacts on water-related systems and to evaluate the performance of management options under these changing conditions. Effects of numerous climatic drivers have been considered, including sea-level rise on water supply management (e.g., [2]), and on water quality management (e.g., [49]); precipitation and temperature on groundwater management (e.g., [63,64,67]), on reservoir management (e.g., [135]), on water supply management (e.g., [3,132]), on water quality management (e.g., [27,43,45] and on nutrient management (e.g., [119,126]) and precipitation on water supply and demand management (e.g., [18,127,130,131]). Similarly, the non-climatic drivers that have been considered, have included effects of population growth on water supply and demand management (e.g., [128]), and on water quality management (e.g., [41]); crop production changes in irrigation system management (e.g., [96,102]); population growth and agricultural production on water supply and demand management (e.g., [128]); agricultural production on irrigation water management (e.g., [93][94][95]100]), on water supply management (e.g., [85]), and on groundwater management (e.g., [67]); changes in domestic use and in agricultural and industrial production on water supplies and demand management (e.g., [127]).…”
Section: Discussion and Recommendationsmentioning
confidence: 99%
“…A high proportion of these studies have sought to understand climatic and/or non-climatic drivers and their impacts on water-related systems and to evaluate the performance of management options under these changing conditions. Effects of numerous climatic drivers have been considered, including sea-level rise on water supply management (e.g., [2]), and on water quality management (e.g., [49]); precipitation and temperature on groundwater management (e.g., [63,64,67]), on reservoir management (e.g., [135]), on water supply management (e.g., [3,132]), on water quality management (e.g., [27,43,45] and on nutrient management (e.g., [119,126]) and precipitation on water supply and demand management (e.g., [18,127,130,131]). Similarly, the non-climatic drivers that have been considered, have included effects of population growth on water supply and demand management (e.g., [128]), and on water quality management (e.g., [41]); crop production changes in irrigation system management (e.g., [96,102]); population growth and agricultural production on water supply and demand management (e.g., [128]); agricultural production on irrigation water management (e.g., [93][94][95]100]), on water supply management (e.g., [85]), and on groundwater management (e.g., [67]); changes in domestic use and in agricultural and industrial production on water supplies and demand management (e.g., [127]).…”
Section: Discussion and Recommendationsmentioning
confidence: 99%
“…Another important limitation is the difficulty to address dynamic processes, since nontransient treatments of the cause-effect relationships are assumed. Although dynamic BNs have been used for water resource system management (e.g., Molina, Pulido-Velázquez, García-Aróstegui, & Pulido-Velázquez, 2013;Ropero, Flores, Rumi, & Aguilera, 2017), they are mainly suitable for a nontransient treatment of cause and effect. BNs have been compared with regression procedures to reproduce optimal operating rules, showing better results (Malekmohammadi et al, 2009); as well to decision trees (Sherafatpour, Roozbahani, & Hasani, 2019).…”
Section: Bayesian Networkmentioning
confidence: 99%
“…This approximation allows both discrete and continuous variables to be included simultaneously in the model with no changes into the methodology followed (Ropero, Aguilera, Fernández, & Rumí, ). In environmental science, BNs based on MTE models have been successfully applied for regression (Maldonado, Aguilera, & Salmerón, ), classification (Maldonado, Aguilera, & Salmerón, ), characterization (Ropero, Rumí, & Aguilera, ), and even dynamic models (Ropero, Flores, Rumí, & Aguilera, ).…”
Section: Bayesian Networkmentioning
confidence: 99%