An Artificial Neural Network model for mountainous water-resources management: The case of Cyprus mountainous watersheds

Iliadis, Lazaros; Maris, Fotios

doi:10.1016/j.envsoft.2006.05.026

Cited by 79 publications

(31 citation statements)

References 13 publications

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“…Advanced data analysis techniques, among which artificial neural networks have become particularly popular in recent years, and have been increasingly used in interpreting the results of environmental research (Gabriels et al, 2007;Iliadis & Maris, 2007;Samecka-Cymerman et al, 2009;Gevrey et al, 2010;Penczak et al, 2012). Statistical programs that are based on artificial neural networks are applicable where traditional methods of data analysis do not provide satisfactory results (Lencioni et al, 2007;Palialexis et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network modelling of macrophyte indices based on physico-chemical characteristics of water

et al. 2013

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The study focused on modelling of macropyte indices against physico-chemical parameters of waters by artificial neural networks. Several macrophyte diversity indices were analysed (species richness-N, the Shannon index-H 0 , the Simpson index-D, and the Pielou index-J) as well as the ecological status index (the Macrophyte Index for Rivers-MIR). The aim of the study was to verify knowledge about potential application of macrophytes in the environmental monitoring. A Multi-Layer Perceptron type of network was used in the analyses. The study included 260 river sites located throughout Poland. Alkalinity, conductivity, pH, nitrate and ammonium nitrogen, reactive and total phosphorus, and biochemical oxygen demand were used as the explanatory variables. The quality of the constructed models was assessed using calculated errors (RMSE and NRMSE) and r Pearson's linear correlation coefficient. The neural network for the MIR index was characterised by the highest quality. Neural networks for other diversity indices (N, H 0 , D, and J) did not provide adequate results for modelling, which shows their ineffectiveness biological monitoring. Sensitivity analysis revealed the influence of each variable to the models. It indicated that modelled values of MIR are most strongly influenced by total phosphorus and alkalinity.

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Section: Introductionmentioning

confidence: 99%

Artificial neural network modelling of macrophyte indices based on physico-chemical characteristics of water

et al. 2013

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“…So, the assessment of water environmental vulnerability is the process of multiple objective decision-makings, in which mathematics model needs to be established to provide scientific basis for the sustainable water environmental management. General assessment methods included fuzzy set theory, artificial neural network model, analytic hierarchy process and so on [2][3][4]. Those assessment methods have some difficulties for assessing complex uncertain water resources system vulnerability rationally.…”

Section: Introductionmentioning

confidence: 99%

Vulnerability Assessment of Water Environmental System in Beijing-Tianjin-Hebei(BTH) Region

Yang¹,

Xie²,

Sun³

et al. 2017

Proceedings of the 2nd 2016 International Conference on Sustainable Development (ICSD 2016)

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Abstract-Rapid population growth and increased economic activity impose an urgent challenge on the sustainability of water environmental system in Beijing -Tianjin -Hebei (BTH) region. Water environmental system is a complex uncertain system under change which is of vulnerability. But water environmental system vulnerability research is relatively weak. In this study, we present a multifunctional hierarchy indicator system for the performance evaluation of water environmental vulnerability (WEV). This paper constructed the assessment index system of water environment system based on PSR model which contained 24 indicators considering social-economy conditions and situation of water resources. Weights were calculated by the method of analytic hierarchy process (AHP) and entropy, and fuzzy set pair analysis assessment method (FSPAAM) was applied to evaluate the degree of vulnerability in the Beijing -Tianjin -Hebei region.

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“…Over the past decade, there has been widespread interest in the field of artificial intelligence (ASCE Task Committee, 2000a, 2000bJain and Srinivasuls, 2004;Campolo et al, 1999;Lliadis and Maris, 2007). The recent advancements in artificial intelligence technologies are making it possible to solve the intelligent operation of sluice gates.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence model for water resources management

Tang

Lei

Lin

et al. 2010

Proceedings of the Institution of Civil Engineers - Water Manag

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The channel network in Pudong New District, Shanghai, is very complex owing to the large area of its basin, its numerous sluice gates, complex influencing factors and some other management issues involving water delivery, flood prevention, floodwater drainage, navigation and saltwater intrusion. It is generally difficult to achieve efficient water resources management merely through manually operating the sluice gates. Therefore, an artificial intelligence modelling system for managing the water resources in the channel network of Pudong New District has been developed by combining hydrodynamic simulation with an artificial intelligence technique. The artificial neural network model is used to develop sluice gate operation procedures according to the water levels in both the outer and inner rivers. The hydrodynamic model is used to simulate the flow discharges and water levels based on the sluice gate operation procedures. This modelling system has been applied successfully to the water resources management of the Pudong channel network. The results indicate that the modelling system satisfactorily meets the demands for sluice gate operation and water resources optimisation management of the channel network and thus provides decision-making support for integrated management of water resources in this inland channel network.

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An Artificial Neural Network model for mountainous water-resources management: The case of Cyprus mountainous watersheds

Cited by 79 publications

References 13 publications

Artificial neural network modelling of macrophyte indices based on physico-chemical characteristics of water

Artificial neural network modelling of macrophyte indices based on physico-chemical characteristics of water

Vulnerability Assessment of Water Environmental System in Beijing-Tianjin-Hebei(BTH) Region

Artificial intelligence model for water resources management

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