Spatial Simulation Modeling of Settlement Distribution Driven by Random Forest: Consideration of Landscape Visibility

Abstract

Settlement models help to understand the social–ecological functioning of landscape and associated land use and land cover change. One of the issues of settlement modeling is that models are typically used to explore the relationship between settlement locations and associated influential factors (e.g., slope and aspect). However, few studies in settlement modeling adopted landscape visibility analysis. Landscape visibility provides useful information for understanding human decision-making associated with the establishment of settlements. In the past years, machine learning algorithms have demonstrated their capabilities in improving the performance of the settlement modeling and particularly capturing the nonlinear relationship between settlement locations and their drivers. However, simulation models using machine learning algorithms in settlement modeling are still not well studied. Moreover, overfitting issues and optimization of model parameters are major challenges for most machine learning algorithms. Therefore, in this study, we sought to pursue two research objectives. First, we aimed to evaluate the contribution of viewsheds and landscape visibility to the simulation modeling of - settlement locations. The second objective is to examine the performance of the machine learning algorithm-based simulation models for settlement location studies. Our study region is located in the metropolitan area of Oyo Empire, Nigeria, West Africa, ca. AD 1570–1830, and its pre-Imperial antecedents, ca. AD 1360–1570. We developed an event-driven spatial simulation model enabled by random forest algorithm to represent dynamics in settlement systems in our study region. Experimental results demonstrate that viewsheds and landscape visibility may offer more insights into unveiling the underlying mechanism that drives settlement locations. Random forest algorithm, as a machine learning algorithm, provide solid support for establishing the relationship between settlement occurrences and their drivers.

Optimization of Three-Dimensional Model of Landscape Space Based on Big Data Analysis

“…For example, Shan and Sun and Yang et al designed a new landscape planning effect simulation system based on virtual reality technology to preprocess 3D landscape images, remove noise information and redundant information, and adopt the parametric description rules of plant spatial layout [6,7]. Zheng et al used the spatial simulation of settlement distribution driven by random forest to conduct three-dimensional simulation in order to improve landscape visibility [8]. Tastan et al compared and studied the availability and constraints of two modeling methods for 3D modeling in immersive virtual reality (IVR).…”

Machine Learning Arrives in Archaeology

“…The construction of the ML models can help to identify the contribution of different variables that are useful predictors of where sites are found across landscapes (Sharafi et al 2016; Zheng et al 2020). The different scales in which these models can operate empower archaeologists when cataloguing heritage by thematic choices, morphology, and environmental context, which in turn makes for both better heritage management (e.g., Castiello and Tonini 2019; Davis, Seeber, et al 2020; Jones and Bickler 2017) and more detailed research around the world (e.g., Caspari and Crespo 2019; Freeland et al 2016).…”

A Novel Prediction Method Based on Artificial Intelligence and Internet of Things for Detecting Coronavirus Disease (COVID-19)

“…When the sample size is small, this method is considered useful. If the traditional method is used for verifications and segmentations, the sample size will be even smaller, resulting in a larger deviation and a nonoptimal solution [20]. e self-service method not only fails to reduce the training sample size but also leaves.…”