A new approach for deriving temperature and salinity fields in the Indian Ocean using artificial neural networks

This work reports a new methodology for deriving monthly averages of temperature (T) and salinity (S) fields for the Indian Ocean based on the use of an artificial neural network (ANN). Investigation and analysis were performed for this region with two distinct datasets: (1) monthly climatological data for T and S fields (in 1° × 1° grid boxes) at standard depth levels of the World Ocean Atlas 1994 (WOA94), and; (2) heterogeneous randomly distributed in situ ARGO, ocean station data (OSD) and profiling (PFL) floats. A further numerical experiment was conducted with these two distinct datasets to train the neural network model. Nonlinear regression mapping utilizing a multilayer perceptron (MLP) is employed to tackle nonlinearity in the data. This study reveals that a feed-forward type of network with a resilient backpropagation algorithm is best suited for deriving T and S fields; this is demonstrated by independently using WOA94 and in situ data, which thus tests the robustness of the ANN model. The suppleness of the T and S fields derived from the ANN model provides the freedom to generate a new grid at any desired level with a high degree of accuracy. Comprehensive training, testing and validation exercises were performed to demonstrate the robustness of the model and the consistency of the derived fields. The study points out that the parameters derived from the ANN model using scattered, inhomogeneous in situ data show very good agreement with state-of-the-art WOA climatological data. Using this approach, improvements in ocean climatology can be expected to occur in a synergistic manner with in situ observations. Our investigation of the Indian Ocean reveals that this approach can be extended to model global oceans.     

Air quality impact assessment of NOx and PM due to diesel vehicles in Delhi

The impact of diesel vehicles on NO_x and PM emissions at various locations in Delhi is assessed using two line source models; the California line source version 4 and the Indian Institute of Technology Line Source. The models offer comparable results but both under predicting the observed values with the Indian Institute of Technology model predictions being slightly better. The analysis also identifies hotspots due to concentrations of NO_x and PM and their diurnal variations is found to be greater in at night hours.     

A decentralized control strategy for freeway regulation

A multilevel decentralized control scheme, the cascading technique, with application to the regulation of traffic on an urban freeway is presented. Performance of the decentralized system is compared to the performance of a centralized and a fixed time control structure. It is shown that the decentralized structure performs better than the centralized structure when incidents (lane closures) occur on the freeway. The freeway is modeled in terms of the aggregate variables section density and section speed, and is considered as a system of interconnected subsystems.