Suspended sediment load prediction using artificial intelligence techniques: comparison between four state-of-the-art artificial neural network techniques

Accurate prediction of suspended sediment (SS) concentration is a difficult task for water resource projects. In recent years, methodologies such as artificial intelligence (AI) algorithms have been applied for sediment load estimation and these models have provided efficient results. The present study investigates the abilities of four distinct AI approaches for estimating monthly SS load in Roodak station on Jajrood River, one of the longest waterways in the north of Iran, using the combinations of the present and antecedent monthly river flow data. This study aims to compare the predictive ability of artificial neural network (ANN), adaptive neuro-fuzzy inference systems (ANFIS), group method of data handling (GMDH), and least square support vector machines (LS-SVM) applied to predict the SS load. To develop the models, the monthly average river flow and the SS data for 50 years were obtained from Tehran regional water authority. Data were separated into three subsets (training, validation, and testing) and the SS concentration was predicted where the reliability of utilized approaches was assessed by statistical criterion including the correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE). A comparison of the developed models revealed that the use of antecedent average river flow is able to enhance the prediction precision of suspended sediment concentration. The results indicate that the LS-SVM model generated superior results than the other models in terms of the mean error criteria, showing the ability of the model to reasonably predict the observed SS load values

Artificial intelligence modeling to evaluate field performance of photocatalytic asphalt pavement for ambient air purification

In recent years, the application of titanium dioxide (TiO2) as a photocatalyst in asphalt pavement has received considerable attention for purifying ambient air from traffic-emitted pollutants via photocatalytic processes. In order to control the increasing deterioration of ambient air quality, urgent and proper risk assessment tools are deemed necessary. However, in practice, monitoring all process parameters for various operating conditions is difficult due to the complex and non-linear nature of air pollution-based problems. Therefore, the development of models to predict air pollutant concentrations is very useful because it can provide early warnings to the population and also reduce the number of measuring sites. This study used artificial neural network (ANN) and neuro-fuzzy (NF) models to predict NOx concentration in the air as a function of traffic count (Tr) and climatic conditions including humidity (H), temperature (T), solar radiation (S), and wind speed (W) before and after the application of TiO2 on the pavement surface. These models are useful for modeling because of their ability to be trained using historical data and because of their capability for modeling highly non-linear relationships. To build these models, data were collected from a field study where an aqueous nano TiO2 solution was sprayed on a 0.2-mile of asphalt pavement in Baton Rouge, LA. Results of this study showed that the NF model provided a better fitting to NOx measurements than the ANN model in the training, validation, and test steps. Results of a parametric study indicated that traffic level, relative humidity, and solar radiation had the most influence on photocatalytic efficiency. © 2014 Springer-Verlag Berlin Heidelberg