This paper outlines the application of the multi-layer perceptron artificial neural network (ANN), ordinary kriging (OK), and inverse distance weighting (IDW) models in the estimation of local scour depth around bridg...This paper outlines the application of the multi-layer perceptron artificial neural network (ANN), ordinary kriging (OK), and inverse distance weighting (IDW) models in the estimation of local scour depth around bridge piers. As part of this study, bridge piers were installed with bed sills at the bed of an experimental flume. Experimental tests were conducted under different flow conditions and varying distances between bridge pier and bed sill. The ANN, OK and IDW models were applied to the experimental data and it was shown that the artificial neural network model predicts local scour depth more accurately than the kriging and inverse distance weighting models. It was found that the ANN with two hidden layers was the optimum model to predict local scour depth. The results from the sixth test case showed that the ANN with one hidden layer and 17 hidden nodes was the best model to predict local scour depth. Whereas the results from the fifth test case found that the ANN with three hidden layers was the best model to predict local scour depth.展开更多
为了解决工业发展导致的灌区土壤投入品残留污染问题,给出一种基于地理信息系统(geographic information system,GIS)的土壤污染监测预警系统。该系统结合VOC-PF1型传感器、STM32主控芯片和GSM通信模块,实现了高效的数据采集和通信功能...为了解决工业发展导致的灌区土壤投入品残留污染问题,给出一种基于地理信息系统(geographic information system,GIS)的土壤污染监测预警系统。该系统结合VOC-PF1型传感器、STM32主控芯片和GSM通信模块,实现了高效的数据采集和通信功能。通过反距离加权(inverse distance weighted,IDW)插值法进行空间分析,并设立预警阈值,实现对灌区土壤投入品残留污染的实时监测和预警。实验结果表明:该系统的监测精度高达98%,监测时长最高为49 s,具有很高的实用性和效率。研究结果不仅为灌区土壤投入品残留污染监测提供了有效手段,也为环境保护和农业可持续发展提供有力支持。展开更多
文摘This paper outlines the application of the multi-layer perceptron artificial neural network (ANN), ordinary kriging (OK), and inverse distance weighting (IDW) models in the estimation of local scour depth around bridge piers. As part of this study, bridge piers were installed with bed sills at the bed of an experimental flume. Experimental tests were conducted under different flow conditions and varying distances between bridge pier and bed sill. The ANN, OK and IDW models were applied to the experimental data and it was shown that the artificial neural network model predicts local scour depth more accurately than the kriging and inverse distance weighting models. It was found that the ANN with two hidden layers was the optimum model to predict local scour depth. The results from the sixth test case showed that the ANN with one hidden layer and 17 hidden nodes was the best model to predict local scour depth. Whereas the results from the fifth test case found that the ANN with three hidden layers was the best model to predict local scour depth.
文摘反距离加权插值方法(Inverse Distance Weighted,IDW)是生成数字高程模型(Digital Elevation Model,DEM)的常用内插手段之一,不同的地形应使用合适的IDW距离指数进行插值。本文选取了平原、丘陵、小起伏山地、中起伏山地和大起伏山地5种地形,设计了2组试验,从地形宏观形态和地形微观形态2个方面研究了地形对IDW插值中最优距离指数(Optimal order of distances,OOD)的影响。首先使用狼群算法(Wolf pack algorithm,WPA)计算不同地形区下IDW插值的OOD,分析不同地形之间OOD的分布差异;其次选取坡度、坡向、曲率3个地形因子,计算各采样点的OOD,分析不同地形因子对采样点OOD的影响。结果表明,从平原地区到大起伏山地地区,随着区域内地形起伏度的增加,OOD减小。采样点的OOD在高值区的占比随坡度增大而减小;OOD随坡向变化差异不大;随着地形曲率的增大,OOD在高值区的占比增加,在低值区的占比减小。在较为平坦的地区,例如平原地区,丘陵地区建议使用OOD在3≤a≤4范围内取值进行IDW插值,而在小起伏山地、中起伏山地和大起伏山地等山地区建议采用OOD在1≤a≤2范围内取值进行IDW插值。
