Satellite-based estimation and evaluation of urban heat islands (UHI) are latest in the field of urban micro climate and environmental management. UHI is one of the serious upcoming climatological issues regarding the development of cities. Conversion of the vegetative area into the impervious surface is the root cause of this problem of development of urban heat. Large-area coverage, quick process, more economical, less energy and other requirements are the attractive features of the satellite-based studies. The present study deals with the formation of UHI in the new capital region of Andhra Pradesh, a recently formed state in India in the year 2015. Satellite images of Landsat-8 are procured and processed to develop LULC and land surface temperature (LST) images. Field data of about 100 points, collected in the study area is also used in this work and the classification accuracy obtained is about 93%. From LULC and LST images it was concluded that the capital region is experiencing severe UHI phenomenon. The two big cities Vijayawada and Guntur are emerged as hot spots. High and low LST obtained are 58 0 C and 23 0 C respectively. The corresponding areas of hot and cold regions were estimated and presented. The outcome of this research can be used as a scientific basis for urban planners in urban planning and management as well as to increase the community awareness in urban heating effect. Urban greening is an essential measure to be adopted by the urban planners to protect the citizens from the ill effects of UHI.
A fault reduction system for software applications is put forward in direct random testing which includes two phases i.e., test case generation and false reduction. The system's primary components are a database, a false reduction mechanism, a test case creation mechanism, and an application selection mechanism. By using feature values from the input application, the test case generation method sets up an Object Behaviour Dependence Model (OBDM) to produce test cases. In addition to the indistinguishable inputs, the false reduction method configures an Adaptive Genetic Algorithm (AGA) to minimise the banned inputs. The AGA graciously accepts the exposure measurements of the experiment circumstances in order to considerably reduce the tendency to make mistakes.
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