Measurement and mathematical modelling of nutrient level and water quality parameters

Abstract

Physico-chemical water quality parameters and nutrient levels such as water temperature, turbidity, saturated oxygen, dissolved oxygen, pH, chlorophyll-a, salinity, conductivity, total nitrogen and total phosphorus, were measured from April to September 2011 in the Karaj dam area, Iran. Total nitrogen in water was modelled using an artificial neural network system. In the proposed system, water temperature, depth, saturated oxygen, dissolved oxygen, pH, chlorophyll-a, salinity, turbidity and conductivity were considered as input data, and the total nitrogen in water was considered as output. The weights and biases for various systems were obtained by the quick propagation, batch back propagation, incremental back propagation, genetic and Levenberg-Marquardt algorithms. The proposed system uses 144 experimental data points; 70% of the experimental data are randomly selected for training the network and 30% of the data are used for testing. The best network topology was obtained as (9-5-1) using the quick propagation method with tangent transform function. The average absolute deviation percentages (AAD%) are 2.329 and 2.301 for training and testing processes, respectively. It is emphasized that the results of the artificial neural network (ANN) model are compatible with the experimental data.

Integration of sensors for dam water quality analysis – a prototype

“…The nutrient-based monitoring has also been remarkable in estimating quality. The biological changes and their understanding is highly needed for such a study (Kashefi Alasl et al 2012). The discussions on the sensors gradually resulted in cost-effective and efficient sensors for quality parameter measurement.…”