基于人工神经网络的城市用水需求组合预测

城市用水需求预测是涉及到诸多要素的复杂系统预测问题。为了减少简单外推法预测所带来的误差,通过在训练BP神经网络时自动调整学习步长和添加动量项修正神经单元之间的权重,既提高了神经网络的收敛速度,又抑制了神经网络限于局部极小现象的发生;然后使用改进的BP神经网络寻找多元回归预测、径向基函数(RBF)神经网络和改进BP神经网络3个单项预测的最佳组合,来综合各项独立预测所包含的信息,并以条件假设按照参考、高、低3个方案预测分析某城市的用水需求情况,说明这种基于人工神经网络的组合预测方法在预测城市用水需求量时是一个准确高效的方法。     

应用于水文预报的优化BP神经网络研究

利用广东省滨江流域的水文观测资料，建立了以前期降水量为预报因子、以水位为输出的BP人工神经网络水文预报模型。首先采用了合理的方法进行样本组织，进而利用最优子集回归技术进行输入因子的确定，然后进行了不同隐层节点数、不同转移函数、不同训练算法的组合试验，确定了应用于水文预报中的优化BP神经网络：网络结构为8-9-1；转移函数的组合方式为tansig-线性函数；训练算法为采用evenberg-Marquardt(Lm)算法。为便于精度分析，还采用了最优子集回归模型作了研究。结果表明，优化BP网络模型无论在拟合精度还是在预测精度上都高于最优子集模型。总的来说BP网络是一种精度较高的水文预测模型。     

A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia,China

Two models, artificial neural network (ANN) and multiple linear regression (MLR), were developed to estimate typical grassland aboveground dry biomass in Xilingol River Basin, Inner Mongolia, China. The normalized difference vegetation index (NDVI) and topographic variables (elevation, aspect, and slope) were combined with atmospherically corrected reflectance from the Landsat ETM+ reflective bands as the candidate input variables for building both models. Seven variables (NDVI, aspect, and bands 1, 3, 4, 5 and 7) were selected by the ANN model (implemented in Statistica 6.0 neural network module), while six (elevation, NDVI, and bands 1, 3, 5 and 7) were picked to fit the MLR function after a stepwise analysis was executed between the candidate input variables and the above ground dry biomass. Both models achieved reasonable results with RMSEs ranging from 39.88% to 50.08%. The ANN model provided a more accurate estimation (RMSE_r = 39.88% for the training set, and RMSE_r = 42.36% for the testing set) than MLR (RMSE_r = 49.51% for the training, and RMSE_r = 53.20% for the testing). The final above ground dry biomass maps of the research area were produced based on the ANN and MLR models, generating the estimated mean values of 121 and 147 g/m², respectively.     

取代芳烃的生物降解性与结构相关性研究

采用量子化学MOPAC－AM1法计算了42种取代芳烃的生成热Hf、分子最高占有轨道能EHOMO、分子量MW、分子总表面积TSA及偶极矩μ。分别采用线性回归分析法和人工神经网络法对所研究化合物的生物降解性参数BOD进行QSBR研究。对训练组而言,线性方法和神经网络法的平均预测误差分别为15．9％和11．4％;而测试组化合物的平均百分误差分别为14．5％和13．0％。无论对于测试组还是训练组,神经网络法的预测都更精确。     

Neural network modeling of dissolved oxygen in the Gru?a reservoir, Serbia

The objective of this study is to develop a feedforward neural network (FNN) model to predict the dissolved oxygen in the Gru?a Reservoir, Serbia. The neural network model was developed using experimental data which are collected during a three years. The input variables of the neural network are: water pH, water temperature, chloride, total phosphate, nitrites, nitrates, ammonia, iron, manganese and electrical conductivity. Sensitivity analysis is used to determine the influence of input variables on the dependent variable. The most effective inputs are determined as pH and temperature, while nitrates, chloride and total phosphate are found to be least effective parameters. The Levenberg-Marquardt algorithm is used to train the FNN. The optimal FNN architecture was determined. The FNN architecture having 15 hidden neurons gives the best choice. Results of FNN models have been compared with the measured data on the basis of correlation coefficient (r), mean absolute error (MAE) and mean square error (MSE). Comparing the modelled values by FNN with the experimental data indicates that neural network model provides accurate results.     

人工神经网络优化电活化硫酸盐降解微囊藻毒素

微囊藻毒素-LR(microcystin-LR,MC-LR)是一种威胁饮用水安全且难降解的蓝藻毒素.掺硼金刚石(boron-doped diamond,BDD)阳极电化学氧化水中MC-LR的反应机理和电解质种类密切相关.本研究通过比较两种电解质(Na2SO4和惰性的NaNO3)条件下,电流密度、循环流速、电解质浓度、反...     

A heuristic approach to predicting water beetle diversity in temporary and fluctuating waters

An understanding of the causal mechanisms and processes that shape macroinvertebrate communities at a local scale has important implications for the management and conservation of freshwater biodiversity. Here we compare the performance of linear and non-linear statistics to explore diversity-environment relationships using data from 76 temporary and fluctuating ponds in two regions of southern England. We focus on aquatic beetle assemblages, which have been shown to be excellent surrogates of wider freshwater macroinvertebrate diversity. Ponds in the region contained a rich coleopteran fauna, totaling 68 species, which provided an excellent model system with which to compare the performance of two non-linear procedures (artificial neural networks—ANNs and generalised additive models—GAMs) and one more traditional linear approach (Multiple linear regression—MLR) to modelling diversity-environment relationships. Of all approaches employed, the best fit was obtained using an ANN model with only four input variables (conductivity, turbidity, magnesium concentration and depth). This model accounted for 82% of the observed variability in Shannon diversity index across ponds. In contrast, the best GAM and MLR models only explained 50% and 14% of this variation, respectively. Contribution profile analysis of conductivity, turbidity, magnesium concentration and depth, obtained from the best fit ANN through a hierarchical cluster analysis, allowed the identification of direct and proxy effects in relation to the environmental variables measured in this study. In each case, distinct clusters of ponds were identified in contribution profile analysis, suggesting that ponds across the two regions fall into a number of discrete groups, whose beetle faunas respond in subtly yet significantly different ways to key environmental variables. Aquatic coleopteran diversity in ponds in the two regions appears to be driven at a local scale by changes in relatively few physicochemical gradients, which are related to diversity in a clearly non-linear manner.     

Artificial neural network modeling of the river water quality—A case study

The paper describes the training, validation and application of artificial neural network (ANN) models for computing the dissolved oxygen (DO) and biochemical oxygen demand (BOD) levels in the Gomti river (India). Two ANN models were identified, validated and tested for the computation of DO and BOD concentrations in the Gomti river water. Both the models employed eleven input water quality variables measured in river water over a period of 10 years each month at eight different sites. The performance of the ANN models was assessed through the coefficient of determination (R²) (square of the correlation coefficient), root mean square error (RMSE) and bias computed from the measured and model computed values of the dependent variables. Goodness of the model fit to the data was also evaluated through the relationship between the residuals and model computed values of DO and BOD. The model computed values of DO and BOD by both the ANN models were in close agreement with their respective measured values in the river water. Relative importance and contribution of the input variables to the model output was evaluated through the partitioning approach. The identified ANN models can be used as tools for the computation of water quality parameters.     

A simulation model for Japanese sardine (Sardinops melanostictus) migrations in the western North Pacific

A two-dimensional individual-based model coupled with fish bioenergetics was developed to simulate migration and growth of Japanese sardine (Sardinops melanostictus) in the western North Pacific. In the model, fish movement is controlled by feeding and spawning migrations with passive transport by simulated ocean current. Feeding migration was assumed to be governed by search for local optimal habitats, which is estimated by the spatial distribution of net growth rate of a sardine bioenergetics model. The forage density is one of the most important factors which determines the geographical distributions of Japanese sardine during their feeding migrations. Spawning migration was modeled by an artificial neural network (ANN) with an input layer composed of five neurons that receive environmental information (surface temperature, temperature change experienced, current speed, day length and distance from land). Once the weight of the ANN was determined, the fish movement was solved by combining with the feeding migration model. To obtain the weights of the ANN, three experiments were conducted in which (1) the ANN was trained with back propagation (BP) method with optimum training data, (2) genetic algorithm (GA) was used to adjust the weights and (3) the weights of the ANN were decided by the GA with BP, respectively. BP is a supervised learning technique for training ANNs. GA is a search technique used in computing to find approximate solutions, such as optimization of parameters. Condition factor of sardine in the model is used as a factor of optimization in the GA works. The methods using only BP or GA did not work to search the appropriate weights in the ANN for spawning migration. In the third method, which is a combined approach of GA with BP, the model reproduced the most realistic spawning migration of Japanese sardine. The changes in temperature and day length are important factors for the orientation cues of Japanese sardine according to the sensitivity analysis of the weights of the ANN.     

Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests

Forestry science has a long tradition of studying the relationship between stand productivity and abiotic and biotic site characteristics, such as climate, topography, soil and vegetation. Many of the early site quality modelling studies related site index to environmental variables using basic statistical methods such as linear regression. Because most ecological variables show a typical non-linear course and a non-constant variance distribution, a large fraction of the variation remained unexplained by these linear models. More recently, the development of more advanced non-parametric and machine learning methods provided opportunities to overcome these limitations. Nevertheless, these methods also have drawbacks. Due to their increasing complexity they are not only more difficult to implement and interpret, but also more vulnerable to overfitting. Especially in a context of regionalisation, this may prove to be problematic. Although many non-parametric and machine learning methods are increasingly used in applications related to forest site quality assessment, their predictive performance has only been assessed for a limited number of methods and ecosystems.In this study, five different modelling techniques are compared and evaluated, i.e. multiple linear regression (MLR), classification and regression trees (CART), boosted regression trees (BRT), generalized additive models (GAM), and artificial neural networks (ANN). Each method is used to model site index of homogeneous stands of three important tree species of the Taurus Mountains (Turkey): Pinus brutia, Pinus nigra and Cedrus libani. Site index is related to soil, vegetation and topographical variables, which are available for 167 sample plots covering all important environmental gradients in the research area. The five techniques are compared in a multi-criteria decision analysis in which different model performance measures, ecological interpretability and user-friendliness are considered as criteria.When combining these criteria, in most cases GAM is found to outperform all other techniques for modelling site index for the three species. BRT is a good alternative in case the ecological interpretability of the technique is of higher importance. When user-friendliness is more important MLR and CART are the preferred alternatives. Despite its good predictive performance, ANN is penalized for its complex, non-transparent models and big training effort.     

酚类物质臭氧氧化降解的定量构效关系

测定了23种酚的臭氧氧化速率, 分别采用遗传算法(GA)结合偏最小二乘法(PLS)、遗传算法结合人工神经网络(ANN)建立了酚类物质臭氧氧化速率的定量构效关系(QSAR)模型.研究表明, 臭氧氧化酚的速率可用伪一级反应速率模型描述, 苯环上取代基得失电子的能力对酚的氧化速率影响较大.基于GA-PLS算法建立的QSAR模型为lgk=3.439-0.206lgP(辛醇-水分配系数对数值)+0.122×pKa(解离常数)+0.3464χpc(四阶路径/簇分子连接性指数)- 0.0236qC-(碳原子所带最大负电荷).基于GA-ANN算法建立的QSAR模型含有参数lgP、4χpc、pKa和α(平均分子极化率).留一法交叉验证结果表明, 基于GA-ANN算法建立的模型比基于GA-PLS算法建立的模型具有更好的稳健性.QSAR研究表明, 酚的臭氧氧化速率与电子云分布以及苯环上取代基的性质密切相关, 另外, 水的溶剂化作用对酚的氧化速率也有显著影响.     

化学品厌氧生物降解QSBR预测模型研究

收集了155种有机化学品厌氧生物降解数据,以随机抽取的109种物质作为训练集,另外46种物质作为验证集,通过结构式拆分得到各基团,分别采用多元线性回归和BP人工神经网络2种算法对有机化合物结构与生物降解性定量关系(QSBR)进行研究。结果表明,多元线性回归模型验证集正确率为78.26%,总正确率为84.52%;BP人工神经网络模型验证集正确率为82.61%,总正确率为90.32%。可见,BP人工神经网络算法相对优于多元线性回归算法。     

BP神经网络和遗传算法在红发夫酵母培养基优化中的应用

运用BP神经网络对红发夫酵母发酵培养基组成进行建模以及预测类胡萝卜素产量,在此基础上采用遗传算法对此模型进行全局寻优.得到红发夫酵母发酵培养基的最佳配比为:蔗糖45.10 g/L,硫酸铵3.00 g/L,硫酸镁0.80 g/L,磷酸二氢钾1.40 g/L,酵母膏3.00 g/L,氯化钙0.50 g/L,类胡萝卜素产量达到8.20 mg/L,干重达到9.47 g/L.采用上述方法优化后的培养基使类胡萝卜素的产量比起始培养基提高了95.90%.     

Identification of important factors for water vapor flux and CO2 exchange in a cropland

Water vapor flux and carbon dioxide (CO₂) exchange in croplands are crucial to water and carbon cycle research as well as to global warming evaluation. In this study, a standard three-layer feed-forward back propagation neural network technique associated with the Bayesian technique of automatic relevance determination (ARD) was employed to investigate water vapor and CO₂ exchange between the canopy of summer maize and atmosphere in responses to variations of environmental and physiological factors. These factors, namely the photosynthetically active radiation (PAR), air temperature (T), vapor pressure deficient (VPD), leaf-area index (LAI), soil water content in root zone (W), and friction velocity (U*), were used as inputs in neural network analysis. Results showed that PAR, VPD, T and LAI were the primary factors regulating both water vapor and CO₂ fluxes with VPD and W more critical to water vapor flux and PAR and T more crucial to CO₂ exchange. Furthermore, two time variables “day of the year (DOY)” and “time of the day (TOD)” could also improve the simulation results of neural network analysis. The important factors identified by the neural network technique used in this study were in the order of PAR > T > VPD > LAI > U* > TOD for water vapor flux and in the order of VPD > W > LAI > T > PAR > DOY for CO₂ exchange. This study suggests that neural network technique associated with ARD could be a useful tool for identifying important factors regulating water vapor and CO₂ fluxes in terrestrial ecosystem.