Learning Machines Applied to Potential Forest Distribution

The clearing of forests to obtain land for pasture and agriculture and the replacement of autochthonous species by other faster-growing varieties of trees for timber have both led to the loss of vast areas of forest worldwide. At present, many developed countries are attempting to reverse these effects, establishing policies for the restoration of older woodland systems. Reforestation is a complex matter, planned and carried out by experts who need objective information regarding the type of forest that can be sustained in each area. This information is obtained by drawing up feasibility models constructed using statistical methods that make use of the information provided by morphological and environmental variables (height, gradient, rainfall, etc.) that partially condition the presence or absence of a specific kind of forestation in an area. The aim of this work is to construct a set of feasibility models for woodland located in the basin of the River Liébana (NW Spain), to serve as a support tool for the experts entrusted with carrying out the reforestation project. The techniques used are multilayer perceptron neural networks and support vector machines. Their results will be compared to the results obtained by traditional techniques (such as discriminant analysis and logistic regression) by measuring the degree of fit between each model and the existing distribution of woodlands. The interpretation and problems of the feasibility models are commented on in the Discussion section.     

WATERSHED AND INSTREAM IMPACTS ON THE FISH POPULATION IN THE SOUTH FORK OF THE CLEARWATER RIVER,IDAHO1

ABSTRACT: This paper presents the results of a statistical analysis performed for the watershed and stream corridor in the South Fork of the Clearwater River (SFCR) basin, in north central Idaho. The analysis was performed for 61 six‐field hydrological unit codes (HUCs) of the SFCR basin using an extensive record (up to 100 years) for 50 watershed and in‐stream parameters, including hydrologic, flow, fish, anthropogenic, and natural activity data. The objective of this research was twofold: first, the development of quantitative relations that describe the Index of Fish Density (IFD) of particular fish species as a function of watershed and instream parameters; and second, to provide a robust confirmation for the effects of some of these parameters, previously recorded by the fisheries profession, by using well established statistical techniques. The uniqueness of this work is the compilation and statistical analysis of large data sets to quantitatively describe the impacts of watershed and instream parameters on the IFD of all salmonids and specific fish species. Factor analysis was employed to regroup parameters that are highly correlated to each other into a set of single factors and to relate the IFD to these factors. Using factor extraction, 12 factors were developed from the 50 watershed and instream parameters. Multiple regression diagnostic tests indicated that only 7 of the 12 factors are strong predictors offish indicators. The strongest predictors are longitude, latitude, elevation, watershed gradient, and water temperature. The analysis indicated that the present model has reasonable predictive power, considering the uncertainty involved in estimating the interdependence of IFD with watershed parameters.     

对几种大气环境预测方法的评估

本文根据石家庄市的常规气象资料和混合层高度,用不同的计算模式和计算方法对本市特征污染因子SO_4的长期平均浓度进行计算,把计算结果与实测浓度比较并进行误差分析,对每种计算方法进行评价,最后讨论了各种计算方法的特点及在城市大气环境预测中的实用性.     

Evaluation of the effect of meteorological data resolution on Lagrangian particle dispersion simulations using the ETEX experiment

This paper presents results from a series of numerical experiments designed to evaluate operational long-range dispersion model simulations, and to investigate the effect of different temporal and spatial resolution of meteorological data from numerical weather prediction models on these simulations. Results of Lagrangian particle dispersion simulations of the first tracer release of the European Tracer Experiment (ETEX) are presented and compared with measured tracer concentrations. The use of analyzed data of higher resolution from the European Center for Medium-Range Weather Forecasts (ECMWF) model produced significantly better agreement between the concentrations predicted with the dispersion model and the ETEX measurements than the use of lower resolution Navy Operational Global Atmospheric Prediction System (NOGAPS) forecast data. Numerical experiments were performed in which the ECMWF model data with lower vertical resolution (4 instead of 7 levels below 500 mb), lower temporal resolution (12 h instead of 6 h intervals), and lower horizontal resolution (2.5° instead of 0.5°) were used. Degrading the horizontal or temporal resolution of the ECMWF data resulted in decreased accuracy of the dispersion simulations. These results indicate that flow features resolved by the numerical weather prediction model data at approximately 45 km horizontal grid spacing and 6 h time intervals, but not resolved at 225 km spacing and 12 h intervals, made an important contribution to the long-range dispersion.     

Quantifying The Rainfall-Water Level Fluctuation Process in a Geologically Complex Lake Catchment

Abstact Simulating hydrologic processes in geologically complex environments is a difficult scientific task since it incorporates high level of uncertainty. Many studies have attempted to accurately quantify the rainfall-water level elevation relationship in freshwater bodies so as to predict flooding and drought events. For this purpose several types of models have been implemented including distributed, black box and conceptual models that often provide efficient results, depending on the availability of reliable data as well as on the level of understanding of the system. Nevertheless, in the particular effort, three different models have been used to describe the relationship between rainfall and water level elevation in Trichonis Lake during the period 1951–1997. A Transfer Function model, a Dynamic Linear Regression and a physically based model, consisting of the lake's water budget equation, its Digital Bathymetric Model and GIS algorithms. These models have been tested to assess their efficiency and applicability in a karstic environment and the aim of the study was to find the best modeling option for developing sustainable water management plans and establishing a flooding/drought warning system in the particular lake catchment. The results indicated that in areas with geologically complex conditions, simple, physically-based models operate better than mechanistic models which usually cannot describe adequately the complexity of the system     

A Comparison of Statistical Approaches for Predicting Stream Temperatures Across Heterogeneous Landscapes1

Abstract: Estimating stream temperatures across broad spatial extents is important for regional conservation of running waters. Although statistical models can be useful in this endeavor, little information exists to aid in the selection of a particular statistical approach. Our objective was to compare the accuracy of ordinary least‐squares multiple linear regression, generalized additive modeling, ordinary kriging, and linear mixed modeling (LMM) using July mean stream temperatures in Michigan and Wisconsin. Although LMM using low‐rank thin‐plate smoothing splines to measure the spatial autocorrelation in stream temperatures was the most accurate modeling approach; overall, there were only slight differences in prediction accuracy among the evaluated approaches. This suggests that managers and researchers can select a stream temperature modeling approach that meets their level of expertise without sacrificing substantial amounts of prediction accuracy. The most accurate models for Michigan and Wisconsin had root mean square errors of 2.0‐2.3°C, suggesting that only relatively coarse predictions can be produced from landscape‐based statistical models at regional scales. Explaining substantially more variability in stream temperatures likely will require the collection of finer‐scale hydrologic and physiographic data, which may be cost prohibitive for monitoring and assessing stream temperatures at regional scales.     

城市景观生态网络规划的空间模式应用探讨

城市景观生态网络空间模式研究是生态城市和景观规划设计现今研究的重要领域之一,目的在于依据景观生态网络模式的应用建立起生态城市规划建设的基本框架和技术途径。在探讨城市景观生态系统特征和城市景观生态网络结构的基础上,总结景观生态网络空间模式并重点分析了水景树的空间生态模式、森林 道路 住宅复合网络模式、平原城市农田 灌木丛 河流交叉网络模式、岛屿城市的绿地 道路生态网络模式4种城市景观生态网络典型模式的图示特征及其生态意义。依此为理论指导基础,以都江堰为例,在继承都江堰大地景观格局并结合都江堰市灾后重建需求的前提下,研究都江堰市城市景观生态格局和生态过程,探讨在不同尺度和不同维度上运用4种网络图示进行都江堰城市景观生态网络优化调整的空间模式和对策。(1)依据“水景树图式”优化调整纵向维度景观生态格局;(2)依据“森林 道路 住宅”和“农田 灌木丛 河流”网络调整横向维度景观生态格局;(3)依据岛屿城市的“绿地 道路”景观生态网络调整城市组团景观生态格局.     

事故预测数学模型的研究与实践

依据收集统计的、真实可靠的事故数据,运用数学方法及建立的多种事故预测模型,在工作中理论与实际相结合,提出并建立了事故预测数学模型,给出具体方法与步骤,以及应用中应遵循的原则和规律,从而做到对将来发生的事故未卜先知,用以指导安全生产活动,预防事故发生。同时,根据我国近18年来各类事故死亡人员的数据建立事故预测数学模型,并运用确立的最佳事故预测数学模型而预测出的事故数据,探讨我国伤亡事故发生的趋势。     

The Use of Atmospheric Dispersion Models in Risk Assessment Decision Support Systems for Pesticides

In the evaluation of potentially adverse effects oforganic chemicals such as pesticides on theenvironment the atmosphere may play an important role.After its release to the atmosphere the chemical willbe transported/dispersed in the atmosphere and finallyit will be removed either by atmospheric-chemicaldestruction or by deposition to the underlying soil orsurface water. In a risk assessment decision supportsystem both ambient concentrations and depositionfluxes must be known to evaluate the risk of directexposure (inhalation) or the risk of soil and watercontamination caused by deposition. This paperdiscusses the use of atmospheric dispersion models insuch risk assessment decision support systems.