Mapping the biomass of Bornean tropical rain forest from remotely sensed data

The biomass and biomass dynamics of forests are major uncertainties in our understanding of tropical environments. Remote sensing is often the only practical means of acquiring information on forest biomass but has not always been used successfully. Here the conventional approaches to the estimation of forest biomass from remotely sensed data were evaluated relative to techniques based on the application of artificial neural networks. Together these approaches were used to estimate and map the biomass of tropical forests in north‐eastern Borneo from Landsat TM data. The neural networks were found to be particularly suited to the application. A basic multi‐layer perceptron network, for example, provided estimates of biomass that were strongly correlated with those measured in the field (r = 0.80). Moreover, these estimates were more strongly correlated with biomass than those derived from 230 conventional vegetation indices, including the widely used normalized difference vegetation index (NDVI).     

人工神经网络与相容性生物量模型预测单木地上生物量的比较

森林生物量是林业生产经营和森林资源监测的重要指标,为探索高效低偏的单木生物量估测方法,引入人工神经网络.本研究采用黑龙江省东折棱河林场的101株长白落叶松地上生物量数据,基于不同变量(胸径、树高、冠幅)组合建立了4个聚合模型体系(AMS),采用加权回归消除模型的异方差.然后,基于最优的变量组合建立人工神经网络(ANN)...     

On-line estimation of biomass concentration using a neural network and information about metabolic state

This paper deals with the design of a neural network-based biomass concentration estimation system. This system is enhanced by the incorporation of information about the actual metabolism of the microorganism cultivated, which is taken from an on-line knowledge-based system. Two different design approaches have been investigated using the fed-batch cultivation of bakers yeast as the model process. In the first, metabolic state (MS) data were passed as additional input to the neural network; in the second, these data were used to select a neural network suitable for the specific MS. Two neural network types—feed-forward (Levenberg-Marquardt) and cascade correlation—were applied to this system and tested, and the performances of these neural networks were compared.     

遥感在森林地上生物量估算中的应用

生物量是地表C循环研究的重要组成部分,生物量研究有助于深入认识区域乃至全球的C平衡。森林作为地球最重要的陆地生态系统,区域乃至全球尺度的森林地上生物量估算一直是生态学研究的难点之一。在大的空间尺度上,遥感技术是估算森林地上生物量的有效手段。TM、AVHRR、SAR等数据以及多源数据的融合在森林生物量估算方面广泛应用,并取得了显著效果。运用遥感技术进行森林生物量估算时,所采用的数据源不同,分析方法也不相同,主要分析方法有:相关分析、多元回归分析、神经网络和数学模型模拟等。随着测定不同空间、时间和波谱分辨率的各种传感器的广泛使用,以及生物量遥感估算模型的进一步发展和完善,大尺度森林生物量的遥感估算研究必将向前迈进一大步。     

On-line biomass estimation in biosurfactant production process by <Emphasis Type="Italic">Candida lipolytica</Emphasis> UCP 988

Biomass is an important variable in biosurfactant production process. However, such bioprocess variable, usually, is collected by sampling and determined by off-line analysis, with significant time delay. Therefore, simple and reliable on-line biomass estimation procedures are highly desirable. An artificial neural network model (ANN) is presented for the on-line estimation of biomass concentration, in biosurfactant production by Candida lipolytica UCP 988, as a nonlinear function of pH and dissolved oxygen. Several configurations were evaluated while developing the optimal ANN model. The optimal ANN model consists of one hidden layer with four neurons. The performance of the ANN was checked using experimental data. The results obtained indicate a very good predictive capacity for the ANN-based software sensor with values of R2 of 0.969 and RMSE of 0.021 for biomass concentration. Estimated biomass using the ANN was proved to be a simple, robust and accurate method.     

Soft sensors for on-line biomass measurements

One of the difficulties encountered in control and optimisation of bioprocesses is the lack of reliable on-line sensors for their key state variables. This paper investigates the suitability of using on-line recurrent neural networks to predict biomass concentrations. Input variables of the proposed recurrent neural network are feed rate, liquid volume and dissolved oxygen. Experimental results revealed that the proposed neural network is able to predict biomass concentrations with an accuracy of ±11%.     

Simulation of biomass gasification with a hybrid neural network model

Gasification of several types of biomass has been conducted in a fluidized bed gasifier at atmospheric pressure with steam as the fluidizing medium. In order to obtain the gasification profiles for each type of biomass, an artificial neural network model has been developed to simulate this gasification processes. Model-predicted gas production rates in this biomass gasification processes were consistent with the experimental data. Therefore, the gasification profiles generated by neural networks are considered to have properly reflected the real gasification process of a biomass. Gasification profiles identified by neural network suggest that gasification behavior of arboreal types of biomass is significantly different from that of herbaceous ones.     

General description of Lake Peipsi-Pihkva

Neural networks and multiple linear regression models of the abundance of brown trout (Salmo trutta L.) on the mesohabitat scale were developed from combinations of physical habitat variables in 220 channel morphodynamic units (pools, riffles, runs, etc.) of 11 different streams in the central Pyrenean mountains. For all the 220 morphodynamic units, the determination coefficients obtained between the estimated and observed values of density or biomass were significantly higher for the neural network (r ² adjusted= 0.93 and r ² adjusted=0.92 (p<0.01) for biomass and density respectively with the neural network, against r ² adjusted=0.69 (p<0.01) and r ² adjusted = 0.54 (p<0.01) with multiple linear regression). Validation of the multivariate models and learning of the neural network developed from 165 randomly chosen channel morphodynamic units, was tested on the 55 other channel morphodynamic units. This showed that the biomass and density estimated by both methods were significantly related to the observed biomass and density. Determination coefficients were significantly higher for the neural network (r ² adjusted =0.72 (p<0.01) and 0.81 (p<0.01) for biomass and density respectively) than for the multiple regression model (r ² adjusted=0.59 and r ² adjusted=0.37 for biomass and density respectively). The present study shows the advantages of the backpropagation procedure with neural networks over multiple linear regression analysis, at least in the field of stochastic salmonid ecology.     

Neural network prediction of biomass digestibility based on structural features

Plots of biomass digestibility are linear with the natural logarithm of enzyme loading; the slope and intercept characterize biomass reactivity. The feed-forward back-propagation neural networks were performed to predict biomass digestibility by simulating the 1-, 6-, and 72-h slopes and intercepts of glucan, xylan, and total sugar hydrolyses of 147 poplar wood model samples with a variety of lignin contents, acetyl contents, and crystallinity indices. Regression analysis of the neural network models indicates that they performed satisfactorily. Increasing the dimensionality of the neural network input matrix allowed investigation of the influence glucan and xylan enzymatic hydrolyses have on each other. Glucan hydrolysis affected the last stage of xylan digestion, and xylan hydrolysis had no influence on glucan digestibility. This study has demonstrated that neural networks have good potential for predicting biomass digestibility over a wide range of enzyme loadings, thus providing the potential to design cost-effective pretreatment and saccharification processes.     

Modeling of polygalacturonase enzyme activity and biomass production by <Emphasis Type="Italic">Aspergillus sojae</Emphasis> ATCC 20235

Aspergillus sojae, which is used in the making of koji, a characteristic Japanese food, is a potential candidate for the production of polygalacturonase (PG) enzyme, which of a major industrial significance. In this study, fermentation data of an A. sojae system were modeled by multiple linear regression (MLR) and artificial neural network (ANN) approaches to estimate PG activity and biomass. Nutrient concentrations, agitation speed, inoculum ratio and final pH of the fermentation medium were used as the inputs of the system. In addition to nutrient conditions, the final pH of the fermentation medium was also shown to be an effective parameter in the estimation of biomass concentration. The ANN parameters, such as number of hidden neurons, epochs and learning rate, were determined using a statistical approach. In the determination of network architecture, a cross-validation technique was used to test the ANN models. Goodness-of-fit of the regression and ANN models was measured by the R ² of cross-validated data and squared error of prediction. The PG activity and biomass were modeled with a 5-2-1 and 5-9-1 network topology, respectively. The models predicted enzyme activity with an R ² of 0.84 and biomass with an R ² value of 0.83, whereas the regression models predicted enzyme activity with an R ² of 0.84 and biomass with an R ² of 0.69.     

三种森林生物量估测模型的比较分析

森林生物量的定量估算为全球碳储量、碳循环研究提供了重要的参考依据。该研究采用黑龙江长白山地区的TM影像和133块森林资源一类清查样地的数据, 选取地学参数、遥感反演参数等71个自变量分别构建多元逐步回归模型、传统BP (back propagation)神经网络模型和基于高斯误差函数的BP神经网络改进模型(Gaussian error function, Erf-BP), 进而估算该地区的森林生物量, 并进行比较分析。结果表明, 多元逐步回归模型估测的森林生物量预测精度为75%, 均方根误差为26.87 t·m^-2; 传统BP神经网络模型估测森林生物量的预测精度为80.92%, 均方根误差为21.44 t·m^-2; Erf-BP估测森林生物量的预测精度为82.22%, 均方根误差为20.83 t·m^-2。可见, 改进后的Erf-BP能更好地模拟生物量与各个因子之间的关系, 估算精度更高。     

Genome‐derived minimal metabolic models for Escherichia coli MG1655 with estimated in vivo respiratory ATP stoichiometry

Metabolic network models describing growth of Escherichia coli on glucose, glycerol and acetate were derived from a genome scale model of E. coli. One of the uncertainties in the metabolic networks is the exact stoichiometry of energy generating and consuming processes. Accurate estimation of biomass and product yields requires correct information on the ATP stoichiometry. The unknown ATP stoichiometry parameters of the constructed E. coli network were estimated from experimental data of eight different aerobic chemostat experiments carried out with E. coli MG1655, grown at different dilution rates (0.025, 0.05, 0.1, and 0.3 h^?1) and on different carbon substrates (glucose, glycerol, and acetate). Proper estimation of the ATP stoichiometry requires proper information on the biomass composition of the organism as well as accurate assessment of net conversion rates under well‐defined conditions. For this purpose a growth rate dependent biomass composition was derived, based on measurements and literature data. After incorporation of the growth rate dependent biomass composition in a metabolic network model, an effective P/O ratio of 1.49 ± 0.26 mol of ATP/mol of O, K_X (growth dependent maintenance) of 0.46 ± 0.27 mol of ATP/C‐mol of biomass and m_ATP (growth independent maintenance) of 0.075 ± 0.015 mol of ATP/C‐mol of biomass/h were estimated using a newly developed Comprehensive Data Reconciliation (CDR) method, assuming that the three energetic parameters were independent of the growth rate and the used substrate. The resulting metabolic network model only requires the specific rate of growth, µ, as an input in order to accurately predict all other fluxes and yields. Biotechnol. Bioeng. 2010;107: 369–381. © 2010 Wiley Periodicals, Inc.     

甘草细胞放大培养中搅拌式反应器操作策略优化

为获得甘草细胞在反应器中放大培养的最佳条件,在建立稳定的甘草细胞搅拌式生物反应器放大培养体系的基础上,分别以单因素和正交实验获得的数据为样本,以细胞净增长生物量为考察指标,运用BP神经网络耦合遗传算法对反应器操作策略进行优化。结果表明,接种量6.4%、摇床转速89r/min、通气速率0.1vvm是甘草细胞进行反应器培养的最优条件;与传统的正交实验方法相比,这种基于神经网络耦合遗传算法的优化方法使反应器中细胞生物量的积累提高了6.9%。     

Monitoring of sequencing batch reactor for nitrogen and phosphorus removal using neural networks

The information of nutrient dynamics is essential for the precise control of effluent quality discharged from biological wastewater treatment processes. However, these variables can usually be determined with a significant time delay. Although the final effluent quality can be analyzed after this delay, it is often too late to make proper adjustments. In this paper, a neural network approach, a software sensor, was proposed for the real-time estimation of nutrient concentrations and overcoming the problem of delayed measurements. In order to improve the neural network performance, a split network structure applied separately for anaerobic and aerobic conditions was employed with dynamic modeling methods such as auto-regressive with exogenous inputs. The proposed methodology was applied to a bench-scale sequencing batch reactor (SBR) for biological nutrient removal. The extrapolation problem of neural networks was possible to be partially overcome with the aid of multiway principal component analysis because of its ability of detecting of abnormal situations which could generate extrapolation. Real-time estimation of PO₄³⁻, NO₃⁻ and NH₄⁺ concentrations based on neural network was successfully carried out with the simple on-line information of the SBR system only.     

湿地翅碱蓬生物量遥感估算模型

以黄河三角洲HJ-1A CCD遥感数据和滨海湿地翅碱蓬生物量实测数据为数据源,通过对比分析参数回归模型（单变量线性和非线性回归模型,多元线性逐步回归模型）和人工神经网络模型（BP网络、RBF网络、GRNN网络）,构建黄河三角洲湿地翅碱蓬生长初期的生物量湿重遥感估算最优模型。研究表明：基于遥感信息变量能够建立生长初期翅碱蓬生物量湿重估算模型。尽管基于RDVI、MSAVI和PC2的3个变量的多元线性回归模型的拟合效果较优,但是以SAVI、MSAVI、RVI、DVI、RDVI和PC2等7个遥感信息变量构建的BP神经网络模型的精度更高,平均相对误差为12.73%,估算效果最优,能够满足较高精度的生物量湿重估算需求。翅碱蓬生长初期生物量湿重最优估算模型的建立,为滨海地区植被生物量监测、区域翅碱蓬生物量季节动态模拟以及黄河三角洲生态系统功能评价提供技术支持与基础。     

Modeling <Emphasis Type="Italic">Euglena</Emphasis> sp. growth under different conditions using an artificial neural network

Microalgae are considered as the future source of biofuels because of their high biomass productivity and neutral lipid content as triacylglycerides (TAG). Microalgae have high photosynthetic efficiency and the possibility of being cultivated in different wastewaters. The isolation of potential microalgae followed by the optimization of cultivation conditions is prerequisite for successful cultivation and accumulation of high lipid content. In the present work, a three-layer artificial neural network (ANN) model is developed to predict the essential parameters (such as pH, temperature, light intensity, photoperiod, and medium composition) based on 156 sets of laboratory experiments for achieving maximum biomass from Euglena sp. The independent parameters (viz., temperature, light intensity, photoperiod and number of days at fixed pH, and media composition) were fed as input to the ANN, and biomass yield was investigated. The comparison of the simulated environmental conditions using the ANN model and experimental results are found to have an excellent correlation coefficient of about 0.97 for the model variables used in this study. The model results established that artificial neural network design may be judiciously employed for optimization of different environmental conditions for this isolated microalga.     

Aedes-AI: Neural network models of mosquito abundance

We present artificial neural networks as a feasible replacement for a mechanistic model of mosquito abundance. We develop a feed-forward neural network, a long short-term memory recurrent neural network, and a gated recurrent unit network. We evaluate the networks in their ability to replicate the spatiotemporal features of mosquito populations predicted by the mechanistic model, and discuss how augmenting the training data with time series that emphasize specific dynamical behaviors affects model performance. We conclude with an outlook on how such equation-free models may facilitate vector control or the estimation of disease risk at arbitrary spatial scales.     

A theory for the use of visual orientation information which exploits the columnar structure of striate cortex

A neural model is constructed based on the structure of a visual orientation hypercolumn in mammalian striate cortex. It is then assumed that the perceived orientation of visual contours is determined by the pattern of neuronal activity across orientation columns. Using statistical estimation theory, limits on the precision of orientation estimation and discrimination are calculated. These limits are functions of single unit response properties such as orientation tuning width, response amplitude and response variability, as well as the degree of organization in the neural network. It is shown that a network of modest size, consisting of broadly orientation selective units, can reliably discriminate orientation with a precision equivalent to human performance. Of the various network parameters, the discrimination threshold depends most critically on the number of cells in the hypercolumn. The form of the dependence on cell number correctly predicts the results of psychophysical studies of orientation discrimination. The model system's performance is also consistent with psychophysical data in two situations in which human performance is not optimal. First, interference with orientation discrimination occurs when multiple stimuli activate cells in the same hypercolumn. Second, systematic errors in the estimation of orientation can occur when a stimulus is composed of intersecting lines. The results demonstrate that it is possible to relate neural activity to visual performance by an examination of the pattern of activity across orientation columns. This provides support for the hypothesis that perceived orientation is determined by the distributed pattern of neural activity. The results also encourage the view of neural activity. The results also are determined by the responses of many neurons rather than the sensitivity of individual cells.     

Influences of vegetation,model, and data parameters on forest aboveground biomass assessment using an area-based approach

Although aboveground biomass (AGB) estimation using area-based approaches (ABAs) and its application to forestry have been actively researched through three decades, this technology has been little operationalized in the Central European forest sector. That means specific recommendations are needed in order to apply ABA for forest biomass modelling in this region. The present study was directed to filling such gaps while examining the effect of input ABA parameters on AGB model quality in conditions of mixed mountainous forests in Central Europe. Specific objectives were to assess whether the strength of the AGB model can be impacted by 1) canopy conditions (leaf-on and leaf-off), 2) airborne LiDAR point density (2.5, 5.0, 7.5, 10.0 points/m²), 3) field methods to estimate AGB (with regeneration components or without), and 4) machine learning methods (AdaBoost, Random decision forest, multilayer neural network, and Bayesian ridge regression). The results show that canopy conditions and airborne LiDAR point densities did not affect the strength of the AGB model, but that model's strength was affected by the vegetation regeneration component in the field biomass reference and by the machine learning method tested for modelling. AdaBoost and random decision forest were the most successful methods. To evaluate the quality of an AGB model it is recommended to combine several individual evaluation functions into the model score. The study highlights several recommendations to follow when estimating AGB from ALS using an ABA in Central European forests.