Optimal school site selection in Urban areas using deep neural networks

Finding an optimal location for a future facility amidst existing sites is a challenging task-and potentially has numerous applications in resource planning. Formally, given a set of candidate sites S and existing sites E, the Optimal Site Selection (OSS) problem aims to find the optimal location s E S that serves maximum customers. In this paper, we study the OSS problem for predicting school location from a set of available sites in the Urban areas. We leverage deep neural networks to solve this problem in a resource and time-efficient way-using a dataset of 47,926 schools across 36 districts of the Punjab province. The dataset includes numerous features such as no. of teachers, no. of classrooms, and no. of sections associated with each school site. We also incorporate a novel feature called site influence that gives the expected number of potential students for a particular school. Our proposed model performs exceedingly well with the addition of this feature. We approach this problem by defining class labels based on a given school's capacity-and further train a deep neural network using the aforementioned features to achieve accuracy values up to 82%. Overall, this study facilitates optimal school site selection in the Urban areas, thereby, adding to communal prosperity and growth.