Learning with Geographical Sensitivity: Place-Based Education and Its Praxis     

Yi Sun  Roger C. K. Chan  Huiwei Chen 《The Professional geographer》2016,68(4):574-583

This study presents pragmatic evidence to make learning a geographical process. It investigates how place-based education (PBE) can deepen the sense of place and vice versa. The study first reviews the meaning of PBE and continues with an ontological discussion of place. As place is theorized, learning practices in the course Sustainable Urban Development and Hong Kong are outlined. The submissions of students are analyzed, and selected reflections are presented to interface with the ontological construct of place. We examine how PBE can enrich student awareness of place and in what way student appreciations of place can add values to the geographical reasoning on sustainability- and urbanism-related topics. Results show that site selection is important and place in PBE is both real and imagined. Heritage conservation and place revitalization are potential reflective topics to design a PBE-based teaching praxis.  相似文献   

基于机器学习算法的县域台风灾害损失风险评估     

杨绚  张立生  王铸 《热带气象学报》2022,38(5):651-661

基于2008—2019年我国台风县(区)灾情的直接经济损失数据,根据经济损失率将台风灾害经济损失风险分为五类,考虑台风灾害的致灾因子和孕灾环境因子共选取10个解释变量,采用五种经典的机器学习算法,包括支持向量机(Support Vector Machine,SVM)、随机森林(Random Forest,RF)、AdaBoost、XGBoost(Extreme Gradient Boosting Machine)和LightGBM(Light Gradient Boosting Machine),分别构建台风灾害经济损失风险评估模型,选出准确率最高的模型,应用于经典台风过程并进行检验评估。结果表明:基于RF算法的台风灾害经济损失风险模型的准确率最高;利用RF、XGBoost、LightGBM、AdaBoost和SVM算法构建模型的准确率依次为0.69、0.63、0.62、0.45和0.41。选择RF算法构建的台风灾害经济损失风险模型的解释变量表明,致灾因子是最主要的解释变量,其中,降雨导致损失的重要性超过风速。该模型在训练集和测试集上对风险分类的TS评分为0.55和0.51,但对每种风险类别的辨别能力存在差异,对于最低风险和最高风险的分类效果较好,对于较高风险和中等风险的分类能力不足。利用该模型对2017年第13号台风“天鸽”的经济损失进行检验评估,评估结果与实际台风灾害经济损失的风险等级较一致,各风险等级的准确率均达到0.7以上,TS评分在0.58以上,空报率和漏报率分别在0.31和0.25以下。   相似文献   

Linking model design and application for transdisciplinary approaches in social-ecological systems     

《Global Environmental Change》2021

As global environmental change continues to accelerate and intensify, science and society are turning to transdisciplinary approaches to facilitate transitions to sustainability. Modeling is increasingly used as a technological tool to improve our understanding of social-ecological systems (SES), encourage collaboration and learning, and facilitate decision-making. This study improves our understanding of how SES models are designed and applied to address the rising challenges of global environmental change, using mountains as a representative system. We analyzed 74 peer-reviewed papers describing dynamic models of mountain SES, evaluating them according to characteristics such as the model purpose, data and model type, level of stakeholder involvement, and spatial extent/resolution. Slightly more than half the models in our analysis were participatory, yet only 21.6% of papers demonstrated any direct outreach to decision makers. We found that SES models tend to under-represent social datasets, with ethnographic data rarely incorporated. Modeling efforts in conditions of higher stakeholder diversity tend to have higher rates of decision support compared to situations where stakeholder diversity is absent or not addressed. We discuss our results through the lens of appropriate technology, drawing on the concepts of boundary objects and scalar devices from Science and Technology Studies. We propose four guiding principles to facilitate the development of SES models as appropriate technology for transdisciplinary applications: (1) increase diversity of stakeholders in SES model design and application for improved collaboration; (2) balance power dynamics among stakeholders by incorporating diverse knowledge and data types; (3) promote flexibility in model design; and (4) bridge gaps in decision support, learning, and communication. Creating SES models that are appropriate technology for transdisciplinary applications will require advanced planning, increased funding for and attention to the role of diverse data and knowledge, and stronger partnerships across disciplinary divides. Highly contextualized participatory modeling that embraces diversity in both data and actors appears poised to make strong contributions to the world’s most pressing environmental challenges.  相似文献   

深度学习网络在降水相态判识和预报中的应用     

黄骄文  蔡荣辉  姚蓉  王胜春  滕志伟 《气象》2021,(3):317-326

利用1996—2015年中国的高空探测资料和地面观测数据,挑选发生降水的数十万个样本将其分为降雨和降雪两类事件,抽象为二分类问题,采用深度学习网络技术构建降水相态判识模型,并用2016—2017年的数据进行测试检验,针对2018年1月下旬中国一次大范围雨雪天气过程进行个例检验,在此基础上探讨了深度学习网络在降水相态判识和预报中的应用。主要结论如下:基于深度学习网络判识模型的判识准确率为98.2%,雨、雪的TS评分分别为97.4%和94.4%,相应空报率为1.7%和2.0%,漏报率为1.0%和3.7%,较传统指标阈值法的判识准确率有较大提高;个例检验显示,基于实况探空数据的模型判识结果与降水相态实况在全国基本保持一致,欧洲中期数值预报中心(ECMWF)的降水相态预报产品和模型的预报结果对全国的降水相态都表现出较好的预报能力,而对雨雪分界线的预报,模型的预报结果较ECMWF总体上更接近实况。测试结果表明,模型较好地提取了雨、雪降水相态的结构特征,深度学习网络在降水相态判识和预报中的应用具有可行性和一定的优势,可为降水相态的客观判识和预报提供重要技术支撑。  相似文献   

基于卷积神经网络的京津冀地区高分辨率格点预报偏差订正试验          下载免费PDF全文

张延彪  宋林烨  陈明轩  韩雷  杨璐 《大气科学学报》2022,45(6):850-862

为了进一步提高RISE系统高分辨率网格化预报产品的准确率,同时考虑到深度学习近年来在地学领域的有效应用,采用2019—2021年高分辨率RISE系统数据,设计出卷积神经网络模型Rise-Unet,实现了未来4～12 h地面2 m温度、2 m相对湿度、10 m-U风速以及10 m-V风速预报结果的订正。订正试验结果表明,采用均方根误差和平均绝对误差作为评分标准,与RISE原始预报结果相比,基于Rise-Unet模型可以有效提高温湿风预报结果的准确率。该基于深度学习的Rise-Unet偏差订正技术可应用于RISE系统的后处理模块,对提升RISE系统百米级分辨率或其他高分辨率模式系统格点预报水平具有重要的科学意义和应用价值。  相似文献   

A Deep Learning Method for Bias Correction of ECMWF 24–240 h Forecasts   总被引：1，自引：0，他引：1  

Lei HAN  Mingxuan CHEN  Kangkai CHEN  Haonan CHEN  Yanbiao ZHANG  Bing LU  Linye SONG  Rui QIN 《大气科学进展》2021,38(9):1444-1459

Correcting the forecast bias of numerical weather prediction models is important for severe weather warnings. The refined grid forecast requires direct correction on gridded forecast products, as opposed to correcting forecast data only at individual weather stations. In this study, a deep learning method called CU-net is proposed to correct the gridded forecasts of four weather variables from the European Centre for Medium-Range Weather Forecast Integrated Forecasting System global model(ECMWF-IFS): 2-m temperature, 2-m relative humidity, 10-m wind speed, and 10-m wind direction, with a forecast lead time of 24 h to 240 h in North China. First, the forecast correction problem is transformed into an image-toimage translation problem in deep learning under the CU-net architecture, which is based on convolutional neural networks.Second, the ECMWF-IFS forecasts and ECMWF reanalysis data(ERA5) from 2005 to 2018 are used as training,validation, and testing datasets. The predictors and labels(ground truth) of the model are created using the ECMWF-IFS and ERA5, respectively. Finally, the correction performance of CU-net is compared with a conventional method, anomaly numerical correction with observations(ANO). Results show that forecasts from CU-net have lower root mean square error, bias, mean absolute error, and higher correlation coefficient than those from ANO for all forecast lead times from 24 h to 240 h. CU-net improves upon the ECMWF-IFS forecast for all four weather variables in terms of the above evaluation metrics, whereas ANO improves upon ECMWF-IFS performance only for 2-m temperature and relative humidity. For the correction of the 10-m wind direction forecast, which is often difficult to achieve, CU-net also improves the correction performance.  相似文献   

Quantitative Precipitation Forecast Experiment Based on Basic NWP Variables Using Deep Learning     

Kanghui ZHOU  Jisong SUN  Yongguang ZHENG  Yutao ZHANG 《大气科学进展》2022,39(9):1472-1486

The quantitative precipitation forecast (QPF) performance by numerical weather prediction (NWP) methods depends fundamentally on the adopted physical parameterization schemes (PS). However, due to the complexity of the physical mechanisms of precipitation processes, the uncertainties of PSs result in a lower QPF performance than their prediction of the basic meteorological variables such as air temperature, wind, geopotential height, and humidity. This study proposes a deep learning model named QPFNet, which uses basic meteorological variables in the ERA5 dataset by fitting a non-linear mapping relationship between the basic variables and precipitation. Basic variables forecasted by the highest-resolution model (HRES) of the European Centre for Medium-Range Weather Forecasts (ECMWF) were fed into QPFNet to forecast precipitation. Evaluation results show that QPFNet achieved better QPF performance than ECMWF HRES itself. The threat score for 3-h accumulated precipitation with depths of 0.1, 3, 10, and 20 mm increased by 19.7%, 15.2%, 43.2%, and 87.1%, respectively, indicating the proposed performance QPFNet improved with increasing levels of precipitation. The sensitivities of these meteorological variables for QPF in different pressure layers were analyzed based on the output of the QPFNet, and its performance limitations are also discussed. Using DL to extract features from basic meteorological variables can provide an important reference for QPF, and avoid some uncertainties of PSs.  相似文献   

支持向量机及其在地震预报中的应用前景   总被引：2，自引：0，他引：2       下载免费PDF全文

王炜  林命週  马钦忠  赵利飞 《地震工程学报》2006,28(1):78-84

统计学习理论(SLT)是研究小样本情况下机器学习规律的理论。支持向量机(SVM)基于统计学习理论,可以处理高度非线性分类和回归等问题,不但较好地解决了小样本、过学习、高维数、局部最小等实际难题,而且具有很强的泛化(预测)能力。本文介绍了支持向量机的分类、回归方法,分析了这一方法的特点,讨论了该方法在地震预报中的应用前景。  相似文献   

Youth transitions and going to university: the perceptions of students attending a geography summer school access programme     

Peter E Hopkins 《Area》2006,38(3):240-247

This paper contributes to understandings of youth transitions in childhood and youth geographies through the use of a participatory diagramming exercise with students studying geography in a summer school access programme at a Scottish university. In particular, the paper explores young people's perceptions about adult/child binaries and their hopes and fears in applying to university. The discussion highlights the extended nature of youth transitions: the usefulness of participatory diagramming as a research method and teaching technique for this group; and the consequences that the increasing interest in childhood and youth geographies might have on teaching and learning in human geography.  相似文献   

Learning Conservation: the role of conservation covenants in landscape redesign at Project Hindmarsh,Victoria     

Chris Harrington  Ruth Lane 《The Australian geographer》2006,37(2):187-209

Biodiversity decline continues apace across the Australian landscape with a pressing need to redesign land use to address this situation. The significance of private land increasingly is recognised for the protection and enhancement of biodiversity as landholders inevitably make decisions that affect environmental quality. Biodiversity conservation is as much a social process as a physical one. Conservation covenants are perpetual agreements under which landholders choose to conserve land voluntarily, primarily for conservation purposes. The role covenants might play in landscape-scale conservation was investigated in north-western Victoria. In-depth interviews with a range of participants were undertaken, with an emphasis on the role covenantors might play as social learning and cultural change agents. Analysis of these interviews offers useful perspectives for understanding socio-cultural dimensions of landscape change and exploring the differing values of production farmers and nature conservation landholders. Consideration is then given to approaches to engaging local production farmers in nature covenants and promoting communication between this group and the largely non-production conservationists who currently form the mainstay of conservation covenants.  相似文献