Multidimensional binary layered model for census prediction using machine learning techniques

The development of the resource allocation process is based on essential factors, such as population size, which can be considered one of the most critical ones. Estimating the population size for a particular region, especially in developing countries, indicates the government’s authority to invest and utilize the resources efficiently. As a result, forecasting population size is one of the most essential requirements that guide the entire development process and, as a result, affects human well-being. This paper presents a novel technique for census seven prediction of a specific region of interest in the context of satellite images. The Multidimensional Binary Layered Model (MDBL) is a representation technique founded on identifying a given region of interest in terms of the geographical nature of the local surrounding regions. The MDBL is then employed to generate an "effective" classifier using different machine learning techniques such as the Convolutional Neural Network (CNN), k-nearest Neighbor (k-NN), and Support Vector Machine (SVM). The Black Widow Optimization (BWO) method has been used to maximize performance and provide the best machine learning model. In order to produce the best prediction model, this optimization method ensures the models are tuned with the most appropriate hyperparameters. To this end, Al-Karak City, in Jordan, has been used as a case study to evaluate the proposed model. The evaluation of the classifiers has been reported in terms of the accuracy and the Area Under Curve (AUC) measurements. The CNN achieved the “best” overall AUC result of 88%, indicating the significant impact of using the proposed MDBL to generate an accurate, effective classifier. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.