Turbidity Prediction in a River Basin by Using Artificial Neural Networks: A Case Study in Northern Spain

Iglesias, Carla; Martínez, Javier Martínez; Nieto, P.J. Garcı́a; Fernández, J.R. Alonso; Muñiz, C. Díaz; Piñeiro, J.; Taboada, J.

doi:10.1007/s11269-013-0487-9

Cited by 54 publications

(27 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This fact could be explained by the limited number of input variables and observations available, since they usually perform well with considerable databases [37].…”

Section: Discussionmentioning

confidence: 99%

Influence of Heartwood on Wood Density and Pulp Properties Explained by Machine Learning Techniques

Iglesias

Santos

Martínez

et al. 2017

Forests

View full text Add to dashboard Cite

Abstract:The aim of this work is to develop a tool to predict some pulp properties e.g., pulp yield, Kappa number, ISO brightness (ISO 2470(ISO :2008, fiber length and fiber width, using the sapwood and heartwood proportion in the raw-material. For this purpose, Acacia melanoxylon trees were collected from four sites in Portugal. Percentage of sapwood and heartwood, area and the stem eccentricity (in N-S and E-W directions) were measured on transversal stem sections of A. melanoxylon R. Br. The relative position of the samples with respect to the total tree height was also considered as an input variable. Different configurations were tested until the maximum correlation coefficient was achieved. A classical mathematical technique (multiple linear regression) and machine learning methods (classification and regression trees, multi-layer perceptron and support vector machines) were tested. Classification and regression trees (CART) was the most accurate model for the prediction of pulp ISO brightness (R = 0.85). The other parameters could be predicted with fair results (R = 0.64-0.75) by CART. Hence, the proportion of heartwood and sapwood is a relevant parameter for pulping and pulp properties, and should be taken as a quality trait when assessing a pulpwood resource.

show abstract

“…This fact could be explained by the limited number of input variables and observations available, since they usually perform well with considerable databases [37].…”

Section: Discussionmentioning

confidence: 99%

Influence of Heartwood on Wood Density and Pulp Properties Explained by Machine Learning Techniques

Iglesias

Santos

Martínez

et al. 2017

Forests

View full text Add to dashboard Cite

show abstract

“…The use of independent parameters (river head, turbidity, SAC and particle density) supported the indication of characteristic behaviour of the system under observation. High turbidity values are often associated with surface water infiltration and potential contamination and are used by drinking-water supply-plant operators for water quality control (Iglesias et al 2014). Similarly, the elevated SAC values in the groundwater observation wells indicated an increase in organic matter content.…”

Section: Discussionmentioning

confidence: 99%

Multivariate Analysis of Groundwater-Quality Time-Series Using Self-organizing Maps and Sammon’s Mapping

Page

Huggenberger

Lischeid

2015

Water Resour Manage

View full text Add to dashboard Cite

Groundwater extracted from alluvial aquifers close to rivers is vulnerable to contamination by infiltrating river water. Infiltration is often increased during high discharge events, when the levels of waterborne pathogens are also increased. Water suppliers with low-level treatment thus rely on alternative measures derived from information on system state to manage the resource and maintain drinking-water quality. In this study, a combination of Self-Organizing Maps and Sammon's Mapping (SOM-SM) was used as a proxy analysis of a multivariate time-series to detect critical system states whereby contamination of the drinking water extraction wells is imminent. Groundwater head, temperature and electrical conductivity time-series from groundwater observation wells were analysed using the SOM-SM method. Independent measurements (spectral absorption coefficient, turbidity, particle density and river stage) were used. This approach can identify critical system states and can be integrated into an adaptive, online, automated groundwater-management process.

show abstract

“…Turbidity is one of the most important water quality parameters for reservoirs that supply drinking water [1][2][3]. The location of an intake station with low turbidity from which drinking water can be sourced is determined by the characteristics and transport of the turbidity plume.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Turbidity Variation in Two Reservoirs Connected by a Water Transfer Tunnel in South Korea

Park¹,

Um²,

Song³

et al. 2017

Sustainability

View full text Add to dashboard Cite

Abstract:The Andong and Imha reservoirs in South Korea are connected by a water transfer tunnel. The turbidity of the Imha reservoir is much higher than that of the Andong reservoir. Thus, it is necessary to examine the movement of turbidity between the two reservoirs via the water transfer tunnel. The aim of this study was to investigate the effect of the water transfer tunnel on the turbidity behavior of the two connecting reservoirs and to further understand the effect of reservoir turbidity distribution as a function of the selective withdrawal depth. This study applied the CE-QUAL-W2, a water quality and 2-dimensional hydrodynamic model, for simulating the hydrodynamic processes of the two reservoirs. Results indicate that, in the Andong reservoir, the turbidity of the released water with the water transfer tunnel was similar to that without the tunnel. However, in the Imha reservoir, the turbidity of the released water with the water transfer tunnel was lower than that without the tunnel. This can be attributed to the higher capacity of the Andong reservoir, which has double the storage of the Imha reservoir. Withdrawal turbidity in the Imha reservoir was investigated using the water transfer tunnel. This study applied three withdrawal selections as elevation (EL.) 141.0 m, 146.5 m, and 152.0 m. The highest withdrawal turbidity resulted in EL. 141.0 m, which indicates that the high turbidity current is located at a vertical depth of about 20-30 m because of the density difference. These results will be helpful for understanding the release and selective withdrawal turbidity behaviors for a water transfer tunnel between two reservoirs.

show abstract

Turbidity Prediction in a River Basin by Using Artificial Neural Networks: A Case Study in Northern Spain

Cited by 54 publications

References 28 publications

Influence of Heartwood on Wood Density and Pulp Properties Explained by Machine Learning Techniques

Influence of Heartwood on Wood Density and Pulp Properties Explained by Machine Learning Techniques

Multivariate Analysis of Groundwater-Quality Time-Series Using Self-organizing Maps and Sammon’s Mapping

Modeling of Turbidity Variation in Two Reservoirs Connected by a Water Transfer Tunnel in South Korea

Contact Info

Product

Resources

About