Titlebook: Smart Master Planning for Cities; Case Studies on Digi T. M. Vinod Kumar Book 2022 The Editor(s) (if applicable) and The Author(s), under e

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肿块

Application of Artificial Neural Network in Master Planning—A Case of Simulating the Land Use and Lan complex ways, the conventional planning methods are generally linear and rational. This leads to an incongruity between the actual structure and the methods used to solve the resulting issues. Moreover, the accuracy and efficiency of plans prepared by conventional methods are often very less and t

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开头

FLORA

腼腆

Smarter Master Planning cities so far, were not using Master Plan and detailed town planning schemes for its implementation for integrated area development but is based on some other non-statutory limited time and limited scope plans which by design is short-lived and intend to die soon after the project is over with no c

Arteriography

Application of Artificial Neural Network in Master Planning—A Case of Simulating the Land Use and Lat biological neurons and their networks. It is a black-box model, which means that the programme gets executed but the equations governing it cannot be extracted and examined. Using ANN can help in simulating various factors that are required for the preparation of a Master plan, thus improving its

exceptional

Smart Master Plan and 3D GIS Planning Support system—A Case of Chennai City, Tamil Nadu, Indiam data collecting tactics through analysis and display. The research described in this chapter has aided in the expansion of 3D city model applications in urban planning toward a smart master plan approach. Although 3D city models improve analytical powers, adopting them into daily planning procedur

moribund

Book 2022pects of a city to realize the coordinated, effective and efficient planning, management, development and conservation that improve ecological, social, biophysical, psychological and economic wellbeing in an equitable manner without compromising the sustainability of development ecosystems and stake

demote

ld cross-validation ROC_AUC score of 0.903, the random forest model showed great ability and prospects in future discovery of novel phases of HEAs. Based on this analysis, the input parameters such as the mixing enthalpy (Δ.) and valence electron concentration (VEC) were identified most influential