PyGRF:改进的 Python 地理随机森林模型及公共卫生和自然灾害案例研究

IF 2.1 3区 地球科学 Q2 GEOGRAPHY
Kai Sun, Ryan Zhenqi Zhou, Jiyeon Kim, Yingjie Hu
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引用次数: 0

摘要

地理随机森林(GRF)是最近开发的一种空间明确的机器学习模型。由于能够提供更准确的预测和局部解释,GRF 已被许多研究采用。然而,当前的 GRF 模型在确定本地模型权重和带宽超参数方面存在局限性,可能存在本地训练样本数量不足的问题,有时本地预测误差较高。此外,GRF 是作为 R 软件包实现的,目前还没有 Python 版本,这限制了它在偏好 Python 的机器学习从业者中的应用。本研究通过引入基于理论的超参数确定、局部训练样本扩展和空间加权局部预测来解决这些局限性。我们还开发了基于 Python 的 GRF 模型和软件包 PyGRF,以方便模型的使用。我们在一个示例数据集上评估了 PyGRF 的性能,并在公共卫生和自然灾害的两个案例研究中进一步展示了其用途。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PyGRF: An Improved Python Geographical Random Forest Model and Case Studies in Public Health and Natural Disasters
Geographical random forest (GRF) is a recently developed and spatially explicit machine learning model. With the ability to provide more accurate predictions and local interpretations, GRF has already been used in many studies. The current GRF model, however, has limitations in its determination of the local model weight and bandwidth hyperparameters, potentially insufficient numbers of local training samples, and sometimes high local prediction errors. Also, implemented as an R package, GRF currently does not have a Python version which limits its adoption among machine learning practitioners who prefer Python. This work addresses these limitations by introducing theory‐informed hyperparameter determination, local training sample expansion, and spatially weighted local prediction. We also develop a Python‐based GRF model and package, PyGRF, to facilitate the use of the model. We evaluate the performance of PyGRF on an example dataset and further demonstrate its use in two case studies in public health and natural disasters.
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来源期刊
Transactions in GIS
Transactions in GIS GEOGRAPHY-
CiteScore
4.60
自引率
8.30%
发文量
116
期刊介绍: Transactions in GIS is an international journal which provides a forum for high quality, original research articles, review articles, short notes and book reviews that focus on: - practical and theoretical issues influencing the development of GIS - the collection, analysis, modelling, interpretation and display of spatial data within GIS - the connections between GIS and related technologies - new GIS applications which help to solve problems affecting the natural or built environments, or business
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