Growing population, increasing urbanization, and rural to urban migration, coupled with the ongoing climate change, threaten the sustainability of cities, particularly in developing countries. Previous studies indicate numerous deficiencies in the water supply and sewage systems of Islamabad; however, a comprehensive insight into the water security assessment has not been carried out. Therefore, this study is aimed at assessing the urban water security of Islamabad by taking both human and environmental aspects into consideration. In principle, we achieve this objective by implementing the Water Security Assessment Framework, using five distinct parameters to calculate an urban water security index. The water supply dimension incorporates availability, accessibility, affordability, and the quality of drinking water in the city, whereas, sanitation and health dimension measures access to improved drainage systems as well as the state of overall hygiene of the city inhabitants. Furthermore, the water economy dimension includes water productivity and investment aspects in the study area, while the environment and ecosystem dimension looks into the current state of natural water bodies. Similarly, overall management and public support for freshwater resources are measured in the society and governance dimension. In general, we attempt to better comprehend water-security nexus in the federal capital considering it as a prerequisite to ensure a sustainable future for the city dwellers.

For autonomous robots, 3D perception of environment is an essential tool, which can be used to achieve better navigation in an obstacle rich environment. This understanding requires a huge amount of computational resources; therefore, the real-time 3D reconstruction of surrounding environment has become a topic of interest for countless researchers in the recent past. Generally, for the outdoor 3D models, stereo cameras and laser depth measuring sensors are employed. The data collected through the laser ranging sensors is relatively accurate but sparse in nature. In this paper, we propose a novel mechanism for the incremental fusion of this sparse data to the dense but limited ranged data provided by the stereo cameras, to produce accurate dense depth maps in real-time over a resource limited mobile computing device. Evaluation of the proposed method shows that it outperforms the state-of-the-art reconstruction frameworks which only utilizes depth information from a single source.

HIGHLIGHTS. Examining the applicability of TRMM and CFSR daily precipitation products in simulating runoff from a large watershed.Calibrating a hydrological mod

We study online sequential regression with nonlinearity and time varying statistical distribution when the regressors lie in a high dimensional space. We escape the curse of dimensionality by tracking the subspace of the underlying manifold using a hierarchical tree structure. We use the projections of the original high dimensional regressor space onto the underlying manifold as the modified regressor vectors for modeling of the nonlinear system. By using the proposed algorithm, we reduce the computational complexity to the order of the depth of the tree and the memory requirement to only linear in the intrinsic dimension of the manifold. The proposed techniques are specifically applicable to high dimensional streaming data analysis in a time varying environment. We demonstrate the significant performance gains in terms of mean square error over the other state of the art techniques through simulated as well as real data.

We analyze damage propagation modeling of turbo-engines in a data-driven approach. We investigate subspace tracking assuming a low dimensional manifold structure and a static behavior during the healthy state of the machines. Our damage propagation model is based on the deviation of the data from the static behavior and uses the notion of health index as a measure of the condition. Hence, we incorporate condition-based maintenance and estimate the remaining useful life based on the current and previous health indexes. This paper proposes an algorithm that adapts well to the dynamics of the data and underlying system, and reduces the computational complexity by utilizing the low dimensional manifold structure of the data. A significant performance improvement is demonstrated over existing methods by using the proposed algorithm on CMAPSS Turbo-engine datasets.