Ecosystem health assessment of Honghu Lake Wetland of China using artificial neural network approach

Honghu Lake, located in the southeast of Hubei Province, China, has suffered a severe disturbance during the past few decades. To restore the ecosystem, the Honghu Lake Wetland Protection and Restoration Demonstration Project (HLWPRDP) has been implemented since 2004. A back propagation (BP) artificial neural network (ANN) approach was applied to evaluatinig the ecosystem health of the Honghu Lake wetland. And the effectiveness of the HLWPRDP was also assessed by comparing the ecosystem health before and after the project. Particularly, 12 ecosystem health indices were used as evaluation parameters to establish a set of three-layer BP ANNs. The output is one layer of ecosystem health index. After training and testing the BP ANNs, an optimal model of BP ANNs was selected to assess the ecosystem health of the Honghu Lake wetland. The result indicates that four stages can be identified based on the change of the ecosystem health from 1990 to 2008 and the ecosystem health index ranges from morbidity before the implementation of HLWPRDP (in 2002) to middle health after the implementation of the HLWPRDP (in 2005). It demonstrates that the HLWPRDP is effective and the BP ANN could be used as a tool for the assessment of ecosystem health.     

丽水地区地质灾害的人为因素分析与非工程性防治措施

丽水地区是浙江省地质灾害最严重的地区之一,近年来有多个县（市）发生了多起重大地质灾害事故,并遗留有多处灾害隐患点。从统计汇总上看,人为因素在丽水地区地质灾害发育及形成中起着十分重要的作用。本文分析了人为因素在地质灾害形成中的作用方式和途径,地质灾害的社会特性,并提出了非工程性防治措施。     

滑坡变形预测灰色神经网络耦合模型的构建及适用性分析

结合灰色模型和神经网络的数据处理特点,提出串联、并联和混联式3种结构的灰色神经网络滑坡变形预测模型。串联式将滑坡变形位移时序分解为趋势项和随机项,采用灰色模型提取滑坡位移时序趋势,利用神经网络逼近随机波动;并联式以灰色模型和神经网络分别对滑坡预测,采用智能非线性组合,按照预测目标精度动态调整权重,从而获取最终组合预测结果;混联式通过增加灰白化层及灰模型群,对神经网络拓扑结构进行优化,达到弱化滑坡原始监测数据随机性、提高预测模型稳健性的目的。将3种模型应用于古树屋滑坡变形预测,并对其适用性进行讨论。结果表明,3种结构的灰色神经网络耦合模型均提高了预测精度,适用于复杂状况下滑坡体的变形预测。     

不同形状和材料的鱼礁模型对短蛸诱集效果的初步研究

采用鱼类行为学方法,观测了短蛸(Octopus ocellatus)对同为PVC材质的3种形状的有孔和无孔模型礁以及对同为管状的3种不同材料的单体和叠加模型礁的行为反应,并对各组内模型礁的诱集效果进行了比较.实验结果表明:未投放模型礁条件下短蛸在水槽四角分布率较高,在鱼礁标志区分布率只有0.5%;PVC材质3种形状的模型礁放入后对短蛸的聚集率均在10%以上,其中内部空间最大的正方体模型礁聚集率最大,每种形状的模型礁中有孔模型礁聚集率高于无孔;3种不同材料的管状模型礁放入后诱集效果均较明显,其中陶瓷材质的管状模型礁效果最好,每种材料的模型礁中3单体叠加后的组合礁聚集率明显高于单体,其中叠加后的陶瓷模型礁对短蛸的聚集率达到39.3%;研究发现短蛸的领域行为对鱼礁模型的诱集效果影响较大.     

根据渐进谱的统计规律生成地震加速度时程

基于美国西部80条基岩上的近场强震记录, 采用Nakayama方法生成记录的渐进谱, 并参照Kameda方式,用统计方法建立了根据震级、距离等地震参数预测渐进谱的统计模型. 提出一种以渐进谱为目标谱的生成幅值和频率非平稳地震加速度时程的迭代方法. 由于考虑了渐进谱幅值和相位的相互影响,所生成的时程的相位也是时频非平稳的,并在相位调整中识别了相位谱增量符号以加速迭代收敛进程. 最后根据统计回归的目标渐进谱模型和本文提出的拟合目标渐进谱的方法,可生成不同震级、距离条件下的幅值和频率均非平稳的地震加速度时程.      

西安高陵人工林土壤干层与含水量季节变化研究

通过野外调查和室内测定,利用烘干称重法对高陵地区丰水年前后不同人工林下0~6 m土壤含水量及土壤水分的季节变化进行研究。结果表明,2002年高陵田家村中国梧桐林和杨树林下160~400 cm范围内均已发育了土壤干层。经过2003年丰水年充沛的降水补给,2004年高陵团庄槐树林、杏树林0~6 m土层均未出现土壤干层,说明水分在丰水年得到很好恢复。丰水年后梨、杏、槐三种人工林160~400、410~600 cm层位土壤含水量均显示春季最高,夏季次之,秋季降到最低或略微上升。     

基于注意力机制与自适应时序分解的气温预报模型

针对传统方法在捕捉气象序列长期依赖关系及泛化性能上的不足,提出了一种基于稀疏注意力与自适应时序分解的气温预报模型（ATFSAS）。该模型整体采用编码器 解码器架构,结合稀疏注意力机制以有效捕捉气象观测数据间的长期依赖性。为减少编码过程中造成的冗余,提出了一种信息蒸馏方法。通过结合多层解码器与自适应时序分解单元,逐步细化预报信号中的周期性和趋势性分量,实现了较为精准的气温预报。基于德国耶拿气象数据集,进行24 h精细化气温预报,其平均绝对误差为1.7108℃。基于中国地面气候资料日值数据集,进行中短期日平均气温预报和多地区单日平均气温预报,相比传统模型LSTM,ATFSAS模型预报结果的平均绝对误差分别提升了35.56%和23.66%。     

Impact of artificial subsurface drainage on groundwater travel times and baseflow discharge in an agricultural watershed,Iowa (USA)

Pollutant delivery through artificial subsurface drainage networks to streams is an important transport mechanism, yet the impact of drainage tiles on groundwater hydrology at the watershed scale has not been well documented. In this study, we developed a two‐dimensional, steady‐state groundwater flow model for a representative Iowa agricultural watershed to simulate the impact of tile drainage density and incision depth on groundwater travel times and proportion of baseflow contributed by tile drains. Varying tile drainage density from 0 to 0.0038 m^?1, while maintaining a constant tile incision depth at 1.2 m, resulted in the mean groundwater travel time to decrease exponentially from 40 years to 19 years and increased the tile contribution to baseflow from 0% to an upper bound of 37%. In contrast, varying tile depths from 0.3 to 2.7 m, while maintaining a constant tile drainage density of 0.0038 m^?1, caused mean travel times to decrease linearly from 22 to 18 years and increased the tile contribution to baseflow from 30% to 54% in a near‐linear manner. The decrease in the mean travel time was attributed to decrease in the saturated thickness of the aquifer with increasing drainage density and incision depth. Study results indicate that tile drainage affects fundamental watershed characteristics and should be taken into consideration when evaluating water and nitrate export from agricultural regions. Copyright © 2011 John Wiley & Sons, Ltd.     

Are artificial neural network techniques relevant for the estimation of longitudinal dispersion coefficient in rivers? / Les techniques de réseaux de neurones artificiels sont-elles pertinentes pour estimer le coefficient de dispersion longitudinale en rivières?

Abstract

Abstract Accurate application of the longitudinal dispersion model requires that specially designed experimental studies are performed in the river reach under consideration. Such studies are usually very expensive, so in order to quantify the longitudinal dispersion coefficient, as an alternative approach, various researchers have proposed numerous empirical formulae based on hydraulic and morphometric characteristics. The results are presented of the application of artificial neural networks as a parameter estimation technique. Five different cases were considered with the network trained for different arrangements of input nodes, such as channel depth, channel width, cross-sectionally averaged water velocity, shear velocity and sinuosity index. In the case where the sinuosity index is included as an input node, the results turned out to be better than those presented by other authors.     

Neuro-fuzzy models employing wavelet analysis for suspended sediment concentration prediction in rivers

Abstract

The study of sediment load is important for its implications to the environment and water resources engineering. Four models were considered in the study of suspended sediment concentration prediction: artificial neural networks (ANNs), neuro-fuzzy model (NF), conjunction of wavelet analysis and neuro-fuzzy (WNF) model, and the conventional sediment rating curve (SRC) method. Using data from a US Geological Survey gauging station, the suspended sediment concentration predicted by the WNF model was in satisfactory agreement with the measured data. Also the proposed WNF model generated reasonable predictions for the extreme values. The cumulative suspended sediment load estimated by this model was much higher than that predicted by the other models, and is close to the observed data. However, in the current modelling, the ANN, NF and SRC models underestimated sediment load. The WNF model was successful in reproducing the hysteresis phenomenon, but the SRC method was not able to model this behaviour. In general, the results showed that the NF model performed better than the ANN and SRC models.

Citation Mirbagheri, S. A., Nourani, V., Rajaee, T. & Alikhani, A. (2010) Neuro-fuzzy models employing wavelet analysis for suspended sediment concentration prediction in rivers. Hydrol. Sci. J. 55(7), 1175–1189.