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所在平台: Udemy |
课程主页: https://www.udemy.com/course/prediction-maps-using-xgboost-knn-nb-ensemble-rf-in-gis/
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**课程名称：** 使用GIS数据和高级机器学习算法进行预测制图 **课程概述：** 本课程将介绍四种基于监督分类的机器学习技术，并结合遥感和地理空间资源数据，应用于两种不同类型的预测任务： **项目一：多标签目标预测（多类别问题）** * **目标：** 预测具有三个标签类别（名称、描述、序数值，如“小”、“大”、“特大”）的目标。 * **输出：** 生成多个预测地图。 * **应用示例：** * 预测特定区域内某种物种的增长情况及其与周边环境条件的关系。 * 预测空气污染水平（如：良好、中等、不健康、危险）。 * 研究复杂疾病的潜在风险因素及其影响，以制定预防或干预策略。 * **课程应用案例：** 预测直径小于10微米的颗粒物（PM10）浓度。该项目成果已发表于《Environmental Science and Pollution Research》期刊，论文题为“Demystifying uncertainty in PM10 susceptibility mapping using variable drop-off in extreme-gradient boosting (XGB) and random forest (RF) algorithms”。 **项目二：二元标签目标预测** * **目标：** 预测具有两个类别（是/否、幻灯片/无幻灯片、发生/未发生、污染/清洁）的目标。 * **应用示例：** * 预测洪水区域及其影响因素（如地形和气候数据）。 * 研究气候变化相关后果及其拖累因素（如城市热岛效应及其与土地利用的关系）。 * 识别受石油泄漏污染和未受污染的区域。 * **课程应用案例：** 在易发区进行滑坡易感性制图。如果您之前学习过使用人工神经网络（ANN）的课程，可以在此课程中进行结果对比，因为我们将使用相同的滑坡数据。 **最终成果：** 所有测量数据（训练和测试数据）将用于生成预测地图，这些地图可用于后续的GIS分析，或直接呈现给决策者，或用于撰写SCI期刊的研究论文。 **课程特色：** 本课程在分析模型和输出地图方面被认为是当前最先进的。它成功地融合了（1）机器学习算法与地理空间领域，以及（2）在数据稀缺环境中利用免费的可用遥感数据。 **重要提示：** * **LaGriSU V2023_03_09** 可通过Github链接下载（在Github搜索“/Althuwaynee/LaGriSU_Landslide-Grid-and-Slope-Units-QGIS_ToolPack”）。 * **LaGriSU** 可用于通过网格和坡度单元自动提取训练/测试专题数据。 **讲师：** Omar AlThuwaynee
In this course, four machine learning supervised classification based techniques used with remote sensing and geospatial resources data to predict two different types of applications: Project 1: Data of Multi-labeled target prediction via multi-label classification (multi class problem). Target (Y) that has 3 labeled classes (instead of Numbers): Names, description, ordinal value (small, large, X-large)..Multiple output maps. Like:Increase specific type of species in certain areas and its relationship with surrounding conditions.Air pollution limits prediction (Good, moderate, unhealthy, Hazardous..)Complex diseases types: potential risk factors and their effects on the disease are investigated to identify risk factors that can be used to develop prevention or intervention strategies.Course application: Prediction of concentration of particulate matter of less than 10 µm diameter (PM10)This project was published as research articles using similar materials and with major part of analysis (with slight modification to the code). "Demystifying uncertainty in PM10 susceptibility mapping using variable drop-off in extreme-gradient boosting (XGB) and random forest (RF) algorithms" in Environmental Science and Pollution Research journal.Project 2: Data of Binary labeled target prediction. Target with 2 classes: Yes and No, Slides and No slide, Happened -Not happened, Contaminated- Clean.Flooded areas and it contribution factors like topographic and climate data.Climate change related consequences and its dragging factors like urban heat islands and it relationship with land uses.Oil spills: polluted and non polluted.Course application: Landslide susceptibility mapping in prone area.If you are previously enrolled in my previous course using ANN, then you have the chance to compare the outcomes, as we used the same landslide data here.Eventually, all the measured data (training and testing), were used to produce the prediction map to be used in further GIS analysis or directly to be presented to decision makers or writing research article in SCI journals.This course considered the most advanced, in terms of analysis models and output maps that successfully invested in the (1) machine learning algorithm and geospatial domains; (2) free available data of remote sensing in data scarce environment.IMPORTANT:LaGriSU Version 2023_03_09 is available (Free) to download using Github link(search for /Althuwaynee/LaGriSU_Landslide-Grid-and-Slope-Units-QGIS_ToolPack)*LaGriSU (automatic extraction of training / testing thematic data using Grid and Slope units)Best regardsOmar AlThuwaynee
