Neural network models as a management tool in lakes

Date

1999-01-01

Author

Karul, C
Soyupak, S
Yurteri, C

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

141
views

0
downloads

A research was made on the potential use of neural network based models in eutrophication modelling. As a result, an algorithm was developed to handle the practical aspects of designing, implementing and assessing the results of a neural network based model as a lake management tool. To illustrate the advantages and limitations of the neural network model, a case study was carried out to estimate the chlorophyll-a concentration in Keban Dam Reservoir as a function of sampled water quality parameters (PO4 phosphorus, NO3 nitrogen, alkalinity, suspended solids concentration, pH, water temperature, electrical conductivity, dissolved oxygen concentration and Secchi depth) by a neural network based model. Alternatively, the same system was solved with a linear multiple regression model in order to compare the performances of the proposed neural network based model and the traditional linear multiple regression model. For both of the models, the linear correlation coefficients between the logarithms of observed and calculated chlorophyll-a concentrations were calculated. The correlation coefficient R, the best linear fit between the observed and calculated values, was evaluated to assess the performances of the two models. R values of 0.74 and 0.71 were obtained for the neural network based model and the linear multiple regression model, respectively. The study showed that the neural network based model can be used to estimate chlorophyll-a with a performance similar to that of the traditional linear multiple regression method. However, for cases where the input and the output variables are not linearly correlated, neural network based models are expected to show a better performance.

Subject Keywords

Aquatic Science

URI

https://hdl.handle.net/11511/66409

Journal

HYDROBIOLOGIA

DOI

https://doi.org/10.1023/a:1017007313690

Collections

Department of Environmental Engineering, Article

Suggestions

OpenMETU
Core

3D object representation using transform and scale invariant 3D features AKAGÜNDÜZ, Erdem; Ulusoy, İlkay (2007-10-21) An algorithm is proposed for 3D object representation using generic 3D features which are transformation and scale invariant. Descriptive 3D features and their relations are used to construct a graphical model for the object which is later trained and then used for detection purposes. Descriptive 3D features are the fundamental structures which are extracted from the surface of the 3D scanner output. This surface is described by mean and Gaussian curvature values at every data point at various scales and a ...
Neural network based orbit prediction for a geostationary satellite Kutay, Ali Türker; Tulunay, Ersin; Tekinalp, Ozan (null; 2001-05-23) An artificial Neural Network (NN) model was developed to estimate the semi-major axis (a), the eccentricity (e) and the inclination (i) of a geostationary satellite orbit. To facilitate a comparison between the NN model developed herewith and a real case, the TORKSAT lB geostationary satellite has been taken as example. A code that numerically solves the parameters of the TORKSAT's orbit, namely METUAEE1, is used to generate the training data for the NN model and to evaluate its performance. A Multi-La...
Coarse-to-fine surface reconstruction from silhouettes and range data using mesh deformation Sahillioğlu, Yusuf; Yemez, Y. (2010-03-01) We present a coarse-to-fine surface reconstruction method based on mesh deformation to build watertight surface models of complex objects from their silhouettes and range data. The deformable mesh, which initially represents the object visual hull, is iteratively displaced towards the triangulated range surface using the line-of-sight information. Each iteration of the deformation algorithm involves smoothing and restructuring operations to regularize the surface evolution process. We define a non-shrinking...
Computational representation of protein sequences for homology detection and classification Oğul, Hasan; Mumcuoğlu, Ünal Erkan; Department of Information Systems (2006) Machine learning techniques have been widely used for classification problems in computational biology. They require that the input must be a collection of fixedlength feature vectors. Since proteins are of varying lengths, there is a need for a means of representing protein sequences by a fixed-number of features. This thesis introduces three novel methods for this purpose: n-peptide compositions with reduced alphabets, pairwise similarity scores by maximal unique matches, and pairwise similarity scores by...
BOUNDARY CURRENT INSTABILITIES, UPWELLING, SHELF MIXING AND EUTROPHICATION PROCESSES IN THE BLACK-SEA SUR, HI; OZSOY, E; UNLUATA, U (Elsevier BV, 1994-01-01) Satellite and in situ data are utilized to investigate the mesoscale dynamics of the Black Sea boundary current system with special emphasis on aspects of transport and productivity. The satellite data are especially helpful in capturing rapid sub-mesoscale motions insufficiently resolved by the in situ measurements.

Citation Formats

C. Karul, S. Soyupak, and C. Yurteri, “Neural network models as a management tool in lakes,” HYDROBIOLOGIA, pp. 139–144, 1999, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/66409.