从河流网系整体出发开发整治长江流域

长江是我国最大的河流,干流长6300km,流域面积180万 km~2,占全国国土面积的1/5。整个流域大部分位于湿润亚热带地区,具有丰富的自然资源,工农业产值占全国的45%,是我国最发达的地区。但流域内地区发展很不平衡,下游地区的发展超过中游和上游,干流沿岸城市的发展超过丘陵山区。因此,从河流网系整体出发,均衡开发长江流域很有必要。     

江苏台网尾波持续时间震级的初步研究

利用江苏台网15个地震台的84个地震DD-1短周期记录资料,通过m_(Lg)震级与持续时间τ和震中距Δ相关,得出该区持续时间震级M_D公式:M_D=a_0+0.44(lgτ)~2+0.00036Δ+DM这里,a_0为与记录分向和尾波截止水平有关的常数;DM为台站校正值。M_D标度内部单台标准差为0.09～0.10震级单位,M_D相对m_(Lg)的单台标准差为0.16～0.17震级单位。     

论可持续发展中的循环经济

未来经济发展模式应当是知识经济和循环经济。循环经济是一种以资源的循环利用为内核 ,以环境生产为前提 ,以自然资源、经济、社会协调发展为目的的新型经济发展模式     

基于BP神经网络的儿童卧室内灰尘PAEs浓度预测

在实测数据的基础上,以邻苯二甲酸酯(PAEs)的各类影响因素为自变量,PAEs浓度为因变量,采用Back-propagation(BP)神经网络建立儿童卧室内PAEs浓度预测模型.结果表明,该模型的预测效果较理想,其中,STD比值均＞0.5,NMB均接近0,EMR均＜19％.以室内环境与儿童健康(CCHH)课题组天津地...     

公路网规划环境影响评价中的困难和不确定性分析

首先分析了公路网规划环境影响评价中的困难,即评价方法的不成熟和获取资料的困难;然后重点分析了评价中的公路网规划方案的不确定、环境信息的不确定、空间信息的不确定和环境影响程度的不确定;最后提出了 4种解决困难和降低不确定的方法,即制定公路网规划环境影响评价技术导则,使用基于情景分析的预测方法,广泛开展公众参与以及通过多方协作的方式开展环境影响评价工作.     

条带开采下沉系数计算与优化设计的神经网络模型

在综合分析条带开采地表下沉系数影响因素的基础上,采用神经网络方法建立了条带开采地表下沉系数的计算模型。模型以国内外成功的条带开采实例为学习训练样本和测试样本,对模型的计算结果与实测值进行了对比分析,分析结果表明,该模型的计算值更接近于实测值。在上述研究的基础上,在给定条带开采采出率的条件下,以条带开采的地表下沉系数最小为原则,运用该模型实现了对条带开采尺寸的优化设计。该研究的成果,为条带开采地表下沉系数的理论计算及条带开采尺寸的优化设计探索出了一种新的方法。     

Scenario derivation and consequence evaluation of dust explosion accident based on dynamic Bayesian network

In order to clarify the correlation between the evolution path of dust explosion accidents and emergency decision-making, and to accurately predict the disaster damage levels of various disaster bearing bodies. This paper extracts 56 key scenario elements from four aspects, namely state, answer, goal and environment, based on the analysis of typical dust explosion accident cases. At the same time, a general scenario evolution path of dust explosion accident is constructed. Using fuzzy number set theory and dynamic Bayes joint probability model, the accurate solution of scenario state probability was realized. With the help of accident cases and dynamic Bayes approach, the dust explosion consequence prediction index system and evaluation criteria were constructed, covering factors such as dust explosion intensity, casualties, direct economic losses, equipment damage, building damage, environmental damage and other factors. A polyethylene wax dust explosion accident in a city of China was used to verify the dust explosion accident scenario evolution model and consequences prediction model. The predicted results were in good agreement with the actual damage of various carriers of the accident, which indicated that the model could be used for dust explosion accident prediction and disaster loss prediction. The research results provided reference and technical support for the prediction of dust explosion accident evolution direction, emergency aid measures decision and deployment, disaster damage prediction and evaluation.     

Pipeline leakage detection and isolation: An integrated approach of statistical and wavelet feature extraction with multi-layer perceptron neural network (MLPNN)

Leakage diagnosis of hydrocarbon pipelines can prevent environmental and financial losses. This work proposes a novel method that not only detects the occurrence of a leakage fault, but also suggests its location and severity. The OLGA software is employed to provide the pipeline inlet pressure and outlet flow rates as the training data for the Fault Detection and Isolation (FDI) system. The FDI system is comprised of a Multi-Layer Perceptron Neural Network (MLPNN) classifier with various feature extraction methods including the statistical techniques, wavelet transform, and a fusion of both methods. Once different leakage scenarios are considered and the preprocessing methods are done, the proposed FDI system is applied to a 20-km pipeline in southern Iran (Goldkari-Binak pipeline) and a promising severity and location detectability (a correct classification rate of 92%) and a low False Alarm Rate (FAR) were achieved.