Fuzzy Neural Network (EFuNN) for Modelling Dissolved Oxygen Concentration (DO)
The aim of this research is to propose a new fuzzy neural network based model, called evolving fuzzy neural network (EFuNN) that extends existing artificial intelligence methods for modelling hourly dissolved oxygen concentration in river ecosystem. To de
- PDF / 625,089 Bytes
- 23 Pages / 439.37 x 666.142 pts Page_size
- 62 Downloads / 190 Views
Fuzzy Neural Network (EFuNN) for Modelling Dissolved Oxygen Concentration (DO) Salim Heddam
Abstract The aim of this research is to propose a new fuzzy neural network based model, called evolving fuzzy neural network (EFuNN) that extends existing artificial intelligence methods for modelling hourly dissolved oxygen concentration in river ecosystem. To demonstrate the capability and the usefulness of the EFuNN model, a one year period from 1 January 2014 to 31 December 2014, of hourly dissolved oxygen (DO) and Water quality variables data collected by the United States Geological Survey (USGS), were used for the development of the models. Two stations are chosen: the bottom (USGS station no: 420741121554001) and the top (USGS station no: 11509370), at Klamath River above Keno Dam nr Keno, Oregon, USA. For comparison purposes, a multiple linear regression (MLR) model that was frequently used for predicting water quality variables in previous studies is also built. The inputs variables used for the EFuNN and MLR models are water pH, temperature (TE), specific conductance (SC), and sensor depth (SD). In both models, 60 % of the data set was randomly assigned to the training set, 20 % to the validation set, and 20 % to the test set. The performances of the models are evaluated using root mean square errors (RMSE), mean absolute error (MAE) and correlation coefficient (CC) statistics. The lowest RMSE and highest CC values were obtained with the EFuNN model. The results obtained in the current study demonstrate the potential applicability of the proposed modeling approach in modelling dissolved oxygen concentration in river ecosystem.
11.1
Introduction
Dissolved oxygen (DO) is a vital component of the aquatic ecosystems, and its accurate estimation in river and stream is highly recommended by many researchers worldwide. DO can be defined as the quantity of molecular oxygen dissolved in S. Heddam (&) Agronomy Department, Faculty of Science, Hydraulics Division University, 20 Août 1955, Route EL HADAIK, BP 26, Skikda, Algeria e-mail: [email protected] © Springer International Publishing Switzerland 2017 C. Kahraman and I.U. Sarı (eds.), Intelligence Systems in Environmental Management: Theory and Applications, Intelligent Systems Reference Library 113, DOI 10.1007/978-3-319-42993-9_11
231
232
S. Heddam
water and is one of the most important parameters affecting the health of aquatic ecosystems, fish mortality, odors, and other aesthetic qualities of surface waters (Chin 2006). According to O’Driscoll et al. (2016) the principal sources of DO in stream are (i) diffusion from the atmosphere, (ii) mixing of the stream water at riffles, and (iii) photosynthesis from in-stream primary production. DO can be affected by forest management activities (O’Driscoll et al. 2016), ambient temperature, atmospheric pressure, and ion activity (USGS 2008). DO is used as a water quality index and mainly reported as an important indicator of water pollution in river (Sun et al. 2016; Mohan and Pavan Kumar 2016), and used for studying an
Data Loading...
