Suitability model using support vector machine for land use planning scenarios in Java Island, Indonesia

Abstract

Java Island has experienced rapid population growth in the past four decades. Along with population growth, human needs are also increasing. This is also driven by economic and social growth. However, land resources to meet population needs are becoming increasingly limited. Human needs change ecosystems by creating ecological pressures, such as land cover change, resource extraction and depletion, and emissions pollution. Thus, sustainable land use planning is needed to meet human needs in the future. Furthermore, It is necessary to consider the suitability of the land in land use planning. In this paper, a suitability evaluation method was developed, which synthetically considered the topographic, meteorological, ecoregion, and water supply conditions. The suitability evaluation model is developed using Support Vector Machine (SVM) to classify various designations. SVM training algorithm aims to find a hyperplane that separates the dataset into a discrete predefined number of classes in a fashion consistent with the training examples. The term optimal hyperplane is used to refer to the decision boundary that minimizes misclassifications, obtained in the training step. Model calibration and validation were performed based on the land-use status in 2016. Subsequently, the validated simulations were conducted based on the planning scenarios. This model is expected to be implemented in the short-term and long-term land planning scenarios, respectively. This study provides a synthetic suitability evaluation method for creating a land-use planning scenario, which overcomes the shortcoming of the traditional way of assigning land-use scenarios that being lack of objectivity.