Integrating farmers’ adaptive knowledge into flood management and adaptation policies in the Vietnamese Mekong Delta: A social learning perspective     

《Global Environmental Change》2019

Flood management and adaptation are important elements in sustaining farming production in the Vietnamese Mekong Delta (VMD). While over the past decades hydraulic development introduced by the central government has substantially benefited the rural economy, it has simultaneously caused multiple barriers to rural adaptation. We investigate the relational practices (i.e., learning interactions) taking place within and across the flood management and adaptation boundaries from the perspective of social learning. We explore whether and how adaptive knowledge (i.e., experimental and experiential knowledge) derived from farmers’ everyday adaptation practices contributes to local flood management and adaptation policies in the selected areas. We collected data through nine focus groups with farmers and thirty-three interviews with government officials, environmental scientists, and farmers. Qualitative analysis suggests that such processes are largely shaped by the institutional context where the boundary is embedded. This study found that while the highly bureaucratic operation of flood management creates constraints for feedback, the more informal arrangements set in place at the local level provide flexible platforms conducive to open communication, collaborative learning, and exchange of knowledge among the different actors. This study highlights the pivotal role of shadow systems that provide space for establishing and maintaining informal interactions and relationships between social actors (e.g., interactions between farmers and extension officials) in stimulating and influencing, from the bottom-up, the emergence of adaptive knowledge about flood management and adaptation in a local context.  相似文献   

飞机积冰预报算法对比及其集成预报模型研究     

卞双双  何宏让  安豪  潘晓滨  张云 《气象》2019,45(10):1352-1362

以人工增雨作业获取的飞机积冰实例资料为基础,利用WRF模式对51次飞机积冰过程进行数值模拟,对比分析了常用七种积冰预报算法对积冰潜势区和强度的预报效果,进而采用评分权重集成法建立了飞机积冰强度集成预报模型,并检验了其预报效果。结果表明:(1)假霜点温度经验法对2002年4月4日积冰个例的预报效果与实况一致,而其他积冰算法预报效果均与实况相差较大;(2)对51次飞机积冰预报效果进行统计检验发现,假霜点温度经验法的预报效果最好,积冰强度预报准确率为72.55%,其次是RAOB法,IC指数法和I积冰指数法次之,改进的IC指数法预报准确率最差,只有19.61%;(3)对比不同积冰算法建立的集成预报模型的预报效果发现,选用IC指数法、假霜点温度经验法、RAOB法进行集成预报时,预报准确率最高,且漏报率、偏弱率及偏强率均能控制在10%以内,比单一预报算法中的最高预报准确率提高了8%,且漏报率降低了4%,偏强率降低了8%。  相似文献   

GRAPES区域集合预报两种侧边界扰动方法对比试验     

范宇恩  陈静  邓国  陈法敬  刘雪晴  徐致真 《气象》2019,45(12):1629-1641

中国气象局数值预报中心自2014年建立了区域集合预报业务系统,其使用的侧边界扰动由全球集合预报系统动力降尺度得到。为深入了解侧边界扰动对区域集合预报的影响,基于15 km水平分辨率的区域集合预报模式,使用动力降尺度方法和尺度化滞后平均法(scaled lagged average forecasting,SLAF)设计构造了两种侧边界扰动方案,并开展了2015年7月共6天的集合预报试验,利用集合均方根误差、集合离散度、连续分级概率评分、离群值、Brier Score及相对作用特征曲线面积等概率预报检验方法进行了多方面检验,分析了两种侧边界扰动方案对区域集合预报质量的影响。结果表明:动力降尺度侧边界扰动方案(DOWN)的扰动总能量在各垂直层次均大于SLAF方案,使得边界上前者的离散度大于后者,集合扰动增长更为合理;对于等压面要素和地面要素,DOWN方案的离散度、Outlier、CRPS等评分优于SLAF方案,反映了DOWN方案构造的侧边界扰动更加合理;在降水概率预报技巧方面,SLAF方案在评分上具有一定优势,但评分的提高没有通过显著性水平检验,因此认为两种方案对降水预报的改进基本相当。  相似文献   

循环神经网络在雷达临近预报中的应用          下载免费PDF全文

韩丰  龙明盛  李月安  薛峰  王建民 《应用气象学报》2019,30(1):61-69

该文将循环神经网络（recurrent neural network,RNN）应用于雷达临近预报。使用预测循环神经网络（predictive RNN）架构,利用雷达历史组合反射率因子建模,给出雷达组合反射率因子未来1 h的预报结果。预测循环神经网络的核心是在长短时记忆单元（long short-term memory,LSTM）中增加时空记忆模块,能够提取雷达回波不同尺度的空间特征,配合循环神经网络架构,可以有效解决反射率因子预测问题。北京大兴雷达和广州雷达长时间序列的独立检验结果和2个强对流天气个例检验结果表明:该方法和传统的基于交叉相关法的1 h雷达外推临近预报相比,在20 dBZ和30 dBZ检验项目内,临界成功指数（CSI）可以提升0.15~0.30,命中率（POD）提高0.15~0.25,虚警率（FAR）降低0.15~0.20,该方法对反射率因子强度变化有一定预报能力。  相似文献   

基于A- CGAN的深反射地震数据随机噪声压制方法研究     

韩建光  王卿  许媛  刘志伟 《地质论评》2024,70(1):228-238

基于深度学习的地震数据噪声压制方法是当前地震数据去噪处理的重要方向。深度学习方法突破了传统滤波处理的局限,在对常规地震数据的噪声压制中表现出效率高、信噪分离效果好的特点。但针对深部弱有效反射数据,当前的深度学习方法特征提取能力有限,难以取得较好的去噪效果。笔者等结合深反射地震数据特点,针对当前深度学习噪声压制方法在特征提取及对数据集依赖上的局限,提出了基于注意力循环生成对抗网络(Attention Cycle- Consistent Generative Adversarial Networks,A- CGAN)的深反射地震数据随机噪声压制方法。借助循环一致生成对抗网络(Cycle- Consistent Generative Adversarial Networks,Cycle- GAN)的域映射思想,降低对数据集的要求。为了构建适用于深反射地震数据的去噪网络,从3个方面对Cycle- GAN进行改进：在Cycle- GAN的生成器（去噪器）中加入残差结构和注意力机制,用于加深网络深度和提高其特征提取能力;在Cycle- GAN的鉴别器中使用块判决,提高鉴别精度和准确度;在损失函数部分加入感知一致性损失函数,提升网络模型恢复纹理细节信息的能力。通过合成地震数据和实际深反射地震数据测试,验证了优化算法的有效性,体现了良好的应用价值。  相似文献   

Comparing Human and Neural Network Learning of Climate Categories     

Robert Lloyd  Gregory Carbone 《The Professional geographer》1995,47(3):237-250

This study compares how humans and neural networks classify climate types. Human subjects were asked to classify climates from monthly temperature and precipitation patterns. To model their learning process, the same data were used to produce input vectors that trained a pattern associator neural network. Both human subjects and the neural network classified climates accurately after 10 rounds of supervised learning. The neural network successfully modeled the rate of human learning and the ability to learn specific climate categories. Moreover, the neural network weights used to classify climates correspond to distinct visual characteristics in temperature and precipitation. These results suggest that neural networks can model the formation of visual categories.  相似文献   

用切氏多项式作月降水量的双向时间序列预报试验   总被引：1，自引：0，他引：1  

丁裕国  牛涛 《南京气象学院学报》1993,16(1):36-40

  相似文献   

Learning about learning: lessons from public engagement and deliberation on urban river restoration   总被引：1，自引：0，他引：1  

JUDITH PETTS 《The Geographical journal》2007,173(4):300-311

This paper provides a new discussion of how people learn through deliberative processes, drawing upon empirical analysis of a novel public engagement process for urban river restoration. Such critical evaluation is rare and yet will be crucial to both theoretical development and learning about engagement practice, not least in a policy area subject to strong regulatory drivers for public participation. The analysis supports two important learning mechanisms – the use of 'gatekeepers' of knowledge, interests and values, and the privileging of narrative. It provides new evidence of instrumental and communicative learning about shared priorities and criteria for effective river restoration that evolved through the deliberative process and directly informed the restoration scheme. It is important to question whether and how such site or context-specific learning might inform other restoration schemes. Finally, the paper questions the often ignored issue of expert learning, not least the issue of the link between individual and organizational learning.  相似文献   

A One-dimensional Ensemble Forecast and Assimilation System for Fog Prediction     

M. D. Müller  C. Schmutz  E. Parlow 《Pure and Applied Geophysics》2007,164(6-7):1241-1264

A probabilistic fog forecast system was designed based on two high resolution numerical 1-D models called COBEL and PAFOG. The 1-D models are coupled to several 3-D numerical weather prediction models and thus are able to consider the effects of advection. To deal with the large uncertainty inherent to fog forecasts, a whole ensemble of 1-D runs is computed using the two different numerical models and a set of different initial conditions in combination with distinct boundary conditions. Initial conditions are obtained from variational data assimilation, which optimally combines observations with a first guess taken from operational 3-D models. The design of the ensemble scheme computes members that should fairly well represent the uncertainty of the current meteorological regime. Verification for an entire fog season reveals the importance of advection in complex terrain. The skill of 1-D fog forecasts is significantly improved if advection is considered. Thus the probabilistic forecast system has the potential to support the forecaster and therefore to provide more accurate fog forecasts.  相似文献   

Deriving rules from activity diary data: A learning algorithm and results of computer experiments   总被引：1，自引：0，他引：1  

Theo A. Arentze  Frank Hofman  Harry J.P. Timmermans 《Journal of Geographical Systems》2001,3(4):325-346

 Activity-based models consider travel as a derived demand from the activities households need to conduct in space and time. Over the last 15 years, computational or rule-based models of activity scheduling have gained increasing interest in time-geography and transportation research. This paper argues that a lack of techniques for deriving rules from empirical data hinders the further development of rule-based systems in this area. To overcome this problem, this paper develops and tests an algorithm for inductively deriving rules from activity-diary data. The decision table formalism is used to exhaustively represent the theoretically possible decision rules that individuals may use in sequencing a given set of activities. Actual activity patterns of individuals are supplied to the system as examples. In an incremental learning process, the system progressively improves on the selection of rules used for reproducing the examples. Computer experiments based on simulated data are performed to fine-tune rule selection and rule value update functions. The results suggest that the system is effective and fairly robust for parameter settings. It is concluded, therefore, that the proposed approach opens up possibilities to derive empirically tested rule-based models of activity scheduling. Follow-up research will be concerned with testing the system on empirical data. Received: 31 January 2001 / Accepted: 13 September 2001  相似文献