Application of artificial neural network approach and remotely sensed imagery for regional eco-environmental quality evaluation

Eco-environment quality evaluation is an important research theme in environment management. In the present study, Fuzhou city in China was selected as a study area and a limited number of 222 sampling field sites were first investigated in situ with the help of a GPS device. Every sampling site was assessed by ecological experts and given an Eco-environment Background Value (EBV) based on a scoring and ranking system. The higher the EBV, the better the ecological environmental quality. Then, three types of eco-environmental attributes that are physically-based and easily-quantifiable at a grid level were extracted: (1) remote sensing derived attributes (vegetation index, wetness index, soil brightness index, surface land temperature index), (2) meteorological attributes (annual temperature and annual precipitation), and (3) terrain attribute (elevation). A Back Propagation (BP) Artificial Neural Network (ANN) model was proposed for the EBV validation and prediction. A three-layer BP ANN model was designed to automatically learn the internal relationship using a training set of known EBV and eco-environmental attributes, followed by the application of the model for predicting EBV values across the whole study area. It was found that the performance of the BP ANN model was satisfactory and capable of an overall prediction accuracy of 82.4%, with a Kappa coefficient of 0.801 in the validation. The evaluation results showed that the eco-environmental quality of Fuzhou city is considered as satisfactory. Through analyzing the spatial correlation between the eco-environmental quality and land uses, it was found that the best eco-environmental areas were related to forest lands, whereas the urban area had the relatively worst eco-environmental quality. Human activities are still considered as a major impact on the eco-environmental quality in this area.     

青藏高原生态资产地域划分中的SOFM网络技术

针对目前地域划分中存在的问题,论文尝试以人工神经网络技术作为区划工作的理论支撑,构建了自组织特征映射SOFM网络,以青藏高原环境与生态系统资产作为待分客体,探索了新技术和方法在生态资产地域划分中的应用。结果表明,对于自然界中广泛存在的非线性问题,SOFM网络具有比聚类分析等线性分类器更强的适应性。应用SOFM网络在对待分客体生态资产进行类型划分的基础上,使用策略性循环尺度转换(SCS)范式对其进行了区域转换,最终完成了青藏高原范围内生态资产的地域划分。     

Progress in developing an ANN model for air pollution index forecast

An air pollution index (API) reporting system is introduced to selected cities of China for public communication on air quality data. Shanghai is the first city in China providing daily average API reports and forecasts. This paper describes the development of an artificial neural network (ANN) model for the API forecasting in Shanghai. It is a multiple layer perceptron (MLP) network, with meteorological forecasting data as the main input, to output the next day average API values. However, the initial version of the MLP model did not work well. To improve the model, a series of tests were conducted with respect to the training method and structure optimization. Based on the test results, the training algorithm was modified and a new model was built. The new model is now being used in Shanghai for API forecasting. Its performance is shown reasonably well in comparison with observation. The application of the old model was only weakly correlated with observation. In 1-year application, the correlation coefficients were 0.2314, 0.1022 and 0.1710 for TSP, SO₂ and NO_x, respectively. But for the new model, for over 8 months application, the correlation coefficients are raised to 0.6056, 0.6993 and 0.6300 for PM₁₀, SO₂, and NO₂. Further, the new algorithm does not rely on manpower intervention so that it is now being applied in several other Chinese cities with quite different meteorological conditions. The structure of the model and the application results are presented in this paper and also the problems to be further studied.     

人工神经网络模型和公路复垦土地适宜性评价

提出了人工神经网络用于公路临时用地复垦土地适宜性评价，利用该模型对京大高速公路山西段复垦山西段复垦土地进行了适宜性评价，探讨了人工神经网络在土地复垦适宜性评价时的特点和注意问题。     

基于神经网络分类器的城市污水处理厂水力冲击预警系统

提出了一套基于神经网络分类器的城市污水处理厂水力负荷冲击预警系统,以期对进水水量骤增现象进行提前1天的预报,使污水处理厂可根据预报结果提前采取水力冲击防护措施,从而保证各单元的平稳运行.根据进水水量的涨幅将某污水处理厂12年日进水水量监测数据分为\"常规\"和\"冲击\"两类,重点对\"冲击\"数据进行提前1天的预测,并采用冲击漏报率、冲击误报率和报准率对模型的预测精度进行评价;同时,基于同样的建模方法和不同的训练、验证样本建立了N(1)、N(2)和N(3) 3个平行模型,以对模型的鲁棒性和建模方法的可重复性进行考察.结果显示,3个模型对2010年、2011年和2012年3年测试样本的预测效果良好,冲击漏报率和报准率两项指标数值均较为稳定,分别在0~0.167和0.981~0.995之间浮动,冲击误报率虽然在数值上的浮动较大,最低为0.143,最高为0.500,平均为0.310,但仍在工程上的可承受范围内.该结果表明,本研究基于神经网络分类器所建立的3个神经网络模型预测精度高、鲁棒性好,显示出良好的性能,有望为污水处理厂水力冲击防护工作提供有力参考.     

基于神经网络的温度预测

室内温度与诸多影响因素之间的非线性、复杂性等关系 ,给建模、预测带来了难度 ,引入了人工神经网络 ;利用人工神经网络的非线性、并行计算和自学习特性进行建模 ,实现了对温度模拟     

小波网络在大气污染物浓度预测中的应用

提出一种“分解-重构-预测”小波网络的大气污染物浓度的预测方法。通过小波分解,把浓度序列分解为不同频段的小波系数序列,再对各层的小波系数子序列重构到原尺度上,然后对小波系数序列采用相匹配的BP神经网络模型进行预测,最后合成浓度序列的最终预测结果。经对二氧化硫浓度预测证明,该方法预测模型推广能力强,预测精度高。     

基于人工神经网络理论的建筑物火灾安全评价研究

依据建筑物火灾危险性的影响因素,应用人工神经网络理论及系统安全方法,建立了建筑物火灾危险性的评价指标体系,该方法摆脱了评价过程中的随机性和参评人员主观上的不确定性及其认识上的模糊性等缺点,大大提高了准确性.为了验证评价模型的准确性,将该理论应用到某高校图书馆火灾危险性评价中,快速、准确地得到了安全评价结果,取得了满意效果,为建筑物防火设计以及安全管理提供了可行的依据.     

基团贡献法对取代苯类化合物生物降解性的预测

测定了47种取代苯类化合物在松花江水中的5日生化需氧量(BOD5).分别采用线性基团贡献法和非线性基团贡献法(人工神经网络法)对化合物的生物降解性BOD5ThOD(ThOD:理论需氧量)进行QSBR研究.得到不同基团对生物降解性的贡献为:C6H5>COOH>OH>CH3O/CH3>NH2>Cl>NO2.线性基团贡献法对于训练组和测试组的定性预测正确率分别为72%和86%;而人工神经网络法的预测正确率分别为92%和86%.预测结果表明线性和非线性基团贡献法的预测效果都很好,相比而言,非线性方法对生物降解性的预测更准确.     

西江年最高水位的神经网络预报模型

对西江洪水发生的特征进行分析表明，洪水发生频率高，具有明显阶段性特征，并与流域面雨量密切相关。利用前期环流场、海表温度（SST）场及环流特征量资料选择初选预报因子，然后对初选预报因子作EOF分解构造综合预报因子，结合人工神经网络方法建立了西江年最高水位预报模型，并对预报模型进行独立样本试验。结果表明，该预报模型对历史样本拟合精度高，试报效果明显好于传统的逐步回归模型，可在汛期预测业务中应用。