Hydroinformatics techniques in eco-environmental modelling and management

Chen, Qiuwen; Morales-Chaves, Y.; Li, Hong; Mynett, Arthur E.

doi:10.2166/hydro.2006.011

Cited by 17 publications

(8 citation statements)

References 46 publications

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“…Recently, numerous computafional and statistical approaches have been developed, leading to the appearance of ecological informatics (Chon & Park 2oo6). The future of eco-environmental modelling lies in the integration of different paradigms and techniques (Chen et al 2006).…”

Section: Introductionmentioning

confidence: 99%

Prediction of dissolved oxygen in reservoirs using adaptive network-based fuzzy inference system

Ranković

Radulović

Radojević

et al. 2011

Journal of Hydroinformatics

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BSTRACTPredicting water quality is the key factor in the water quality management of reservoirs. Since a large number of factors affect the water quality, traditional data processing methods are no longer good enough for solving the problem. The dissolved oxygen (DO) level is a measure of the health of the aquatic system and its prediction is very important. DO dynamics are highly nonlinear and artificial intelligence techniques are capable of modelling this complex system. The objective of this study was to develop an adaptive network-based fuzzy inference system (ANFIS) to predict the DO in the Gruža Reservoir, Serbia. The fuzzy model was developed using experimental data which were collected during a 3-year period. The input variables analysed in this paper are: water pH, water temperature, total phosphate, nitrites, ammonia, iron, manganese and electrical conductivity. The selection of an appropriate set of input variables is based on the building of ANFIS models for each possible combination of input variables. Results of fuzzy models are compared with measured data on the basis of correlation coefficient, mean absolute error and mean square error. Comparing the predicted values by ANFIS with the experimental data indicates that fuzzy models provide accurate results.

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Section: Introductionmentioning

confidence: 99%

Prediction of dissolved oxygen in reservoirs using adaptive network-based fuzzy inference system

Ranković

Radulović

Radojević

et al. 2011

Journal of Hydroinformatics

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“…As a proxy indicator of phytoplankton growth [ Chen et al . ], the selection of chlorophyll a within the set of inputs is not surprising; Recknagel et al . [] also find chlorophyll a to be an important input to explain diatom and cyanobacteria dynamics.…”

Section: Discussionmentioning

confidence: 99%

“…Two modeling methodologies, the empirical and the mechanistic approach, are typically used. The mechanistic approach produces process‐based models, based on a priori knowledge of the processes involved in phytoplankton growth [ Chen et al ., ]. These models have been successfully used in many studies [e.g., Ostfeld and Salomons , ; Elliott et al ., ], and their contribution to model nonstationary processes is generally well recognized [ Wagener et al ., ].…”

Section: Introductionmentioning

confidence: 99%

“…The choice of the model structure, i.e., the form of the transfer function from the set of inputs to the output, is key (i) to obtain accurate predictions of unknown or future values of the output and (ii) to find interpretable patterns within the data [ Chau and Muttil , ]. Artificial neural networks (ANNs) are the most commonly used data‐driven technique in ecological modeling [ Lee et al ., ; Jeong et al ., ; Chen et al ., , and references therein], because of their recognized capability in predicting highly nonlinear and complex relationships. Once the ANN structure is validated, variable contribution and sensitivity analyses methods are applied on the input variables and connection weights to gain insights into the physical processes [ Gevrey et al ., ; Jeong et al ., ; Oh et al ., ; Mouton et al ., ].…”

Section: Introductionmentioning

confidence: 99%

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An empirical modeling approach to predict and understand phytoplankton dynamics in a reservoir affected by interbasin water transfers

et al. 2013

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[1] In this paper, we use empirical modeling to predict and understand phytoplankton dynamics in a reservoir affected by water transfers. Prediction of phytoplankton biovolume is central to the management of water resources, particularly given the significant impacts on quality of the water-quantity oriented management of transfers between reservoirs. A novel tree-based iterative input variable selection algorithm is applied for the first time in an ecological context, and identifies a maximum of eight driving factors out of 77 candidates to explain the biovolume of chlorophytes, cyanobacteria and diatoms. The stepwise forward-selection to iteratively identify the most important inputs leads to a physically interpretable model able to infer the physical processes controlling phytoplankton biovolume. Reservoir inflows and outflows are found to exert a strong control over diatom and chlorophyte dynamics while water temperature, nitrate and phosphorus determine the biovolume of cyanobacteria. Following the selection of the most relevant inputs, the 1 week ahead predictions of four different data-driven model classes, i.e., neural networks, extra trees (ETs), model trees and linear regressions, are compared based on performance indices and statistical tests. ETs are found to outperform the other models by providing accurate predictions of cyanobacteria, chlorophyte and diatom biovolume by explaining 66.6%, 66.9%, and 80.5% of the variance, respectively. The methodology is applicable to different environmental studies and combines the strength of empirical modeling, i.e., compact models and accurate predictions, with a good understanding of the physical processes involved.Citation: Fornarelli, R., S. Galelli, A. Castelletti, J. P. Antenucci, and C. L. Marti (2013), An empirical modeling approach to predict and understand phytoplankton dynamics in a reservoir affected by interbasin water transfers, Water Resour. Res., 49,[3626][3627][3628][3629][3630][3631][3632][3633][3634][3635][3636][3637][3638][3639][3640][3641]

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“…Hydrological systems encompass not only hydro-and geosphere partitions, but are affected by the bio-and atmosphere as well. An integrated process understanding is required in order to evaluate the impact of anthropogenic influences and the evolution of hydrosystems (Chen et al 2006). (Anderson & Burt 1985;Abbott et al 1986a;Singh & Frevert 2005).…”

Section: Introductionmentioning

confidence: 99%

Numerical analysis of coupled hydrosystems based on an object-oriented compartment approach

Kolditz

Delfs

Bürger

et al. 2008

Journal of Hydroinformatics

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In this paper we present an object-oriented concept for numerical simulation of multi-field problems for coupled hydrosystem analysis. Individual (flow) processes modelled by a particular partial However, the coupling requires a bridging of different temporal and spatial scales, which is solved here by the integration of fluxes (spatially and temporally). In closing we present three application examples: a benchmark test for overland flow on an infiltrating surface and two case studies -at the Borden site in Canada and the Beerze-Reusel drainage basin in the Netherlands.

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Hydroinformatics techniques in eco-environmental modelling and management

Cited by 17 publications

References 46 publications

Prediction of dissolved oxygen in reservoirs using adaptive network-based fuzzy inference system

Prediction of dissolved oxygen in reservoirs using adaptive network-based fuzzy inference system

An empirical modeling approach to predict and understand phytoplankton dynamics in a reservoir affected by interbasin water transfers

Numerical analysis of coupled hydrosystems based on an object-oriented compartment approach

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