Case studies on the use of neural networks in eutrophication modeling

Date

2000-10-30

Author

Karul, C
Soyupak, S
Cilesiz, AF
Akbay, N
Germen, E

Metadata

Show full item record

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

Item Usage Stats

260
views

0
downloads

Artificial neural networks are becoming more and more common to be used in development of prediction models for complex systems as the theory behind them develops and the processing power of computers increase. A three layer Levenberg-Marquardt feedforward learning algorithm was used to model the eutrophication process in three water bodies of Turkey (Keban Dam Reservoir, Mogan and Eymir Lakes). Despite the very complex and peculiar nature of Keban Dam, a relatively good correlation (correlation coefficient between 0.60 and 0.75) was observed between the measured and calculated values. For Mogan and Eymir, which are much smaller and more homogenous lakes compared to Keban Dam Reservoir, correlation values as high as 0.95 were achieved between the measured and calculated values. Neural network models were able to model non-linear behavior in eutrophication process reasonably well and could successfully estimate some extreme values from validation and test data sets which were not used in training the neural network.

Subject Keywords

Eutrophication, Modeling, Neural Network, Chlorophyll-A

URI

https://hdl.handle.net/11511/68079

Journal

ECOLOGICAL MODELLING

DOI

https://doi.org/10.1016/s0304-3800(00)00360-4

Collections

Department of Environmental Engineering, Article

Suggestions

OpenMETU
Core

A new approach to mathematical water quality modeling in reservoirs: Neural networks Karul, C; Soyupak, S; Germen, E (1998-01-01) Neural Networks are becoming more and more valuable tools for system modeling and function approximation as computing power of microcomputers increase. Modeling of complex ecological systems such as reservoir limnology is very difficult since the ecological interactions within a reservoir are difficult to define mathematically and are usually system specific. To illustrate the potential use of Neural Networks in ecological modeling, a software was developed to train the data from Keban Dam Reservoir by back...
Neural networks with piecewise constant argument and impact activation Yılmaz, Enes; Akhmet, Marat; Department of Scientific Computing (2011) This dissertation addresses the new models in mathematical neuroscience: artificial neural networks, which have many similarities with the structure of human brain and the functions of cells by electronic circuits. The networks have been investigated due to their extensive applications in classification of patterns, associative memories, image processing, artificial intelligence, signal processing and optimization problems. These applications depend crucially on the dynamical behaviors of the networks. In t...
Geospatial object recognition using deep networks for satellite images Barut, Onur; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2018) Deep learning paradigm has been drawing significant interest during the last decade due to the recent developments in novel machine learning algorithms and improvements in computational hardware. Satellite image analysis is also an important scientific area with many objectives, such as disaster and crisis management, forest cover, road mapping, city planning, even military purposes. Spatial correlations of land cover or geospatial objects between different images lead to widely utilization of convolutional...
Geospatial Object Detection Using Deep Networks Barut, Onur; Alatan, Abdullah Aydın (2019-01-01) In the last decade, deep learning has been drawing a huge interest due to the developments in the computational hardware and novel machine learning techniques. This progress also significantly effects satellite image analysis for various objectives, such as disaster and crisis management, forest cover, road mapping, city planning and even military purposes. For all these applications, detection of geospatial objects has crucial importance and some recent object detection techniques are still unexplored to b...
Position estimation for timing belt drives of precision machinery using structured neural networks KILIÇ, Ergin; DOĞRUER, CAN ULAŞ; Dölen, Melik; Koku, Ahmet Buğra (2012-05-01) This paper focuses on a viable position estimation scheme for timing-belt drives using artificial neural networks. In this study, the position of a carriage (load) is calculated via a structured neural network topology accepting input from a position sensor on the actuator side of the timing belt. The paper presents a detailed discussion on the source of transmission errors. The characteristics of the error in different operation regimes are exploited to construct different network topologies. That is, a re...

Citation Formats

C. Karul, S. Soyupak, A. Cilesiz, N. Akbay, and E. Germen, “Case studies on the use of neural networks in eutrophication modeling,” ECOLOGICAL MODELLING, pp. 145–152, 2000, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/68079.