Predicting land cover using GIS, Bayesian and evolutionary algorithm methods

Modelling land cover change from existing land cover maps is a vital requirement for anyone wishing to understand how the landscape may change in the future. In order to test any land cover change model, existing data must be used. However, often it is not known which data should be applied to the problem, or whether relationships exist within and between complex datasets. Here we have developed and tested a model that applied evolutionary processes to Bayesian networks. The model was developed and tested on a dataset containing land cover information and environmental data, in order to show that decisions about which datasets should be used could be made automatically. Bayesian networks are amenable to evolutionary methods as they can be easily described using a binary string to which crossover and mutation operations can be applied. The method, developed to allow comparison with standard Bayesian network development software, was proved capable of carrying out a rapid and effective search of the space of possible networks in order to find an optimal or near-optimal solution for the selection of datasets that have causal links with one another. Comparison of land cover mapping in the North-East of Scotland was made with a commercial Bayesian software package, with the evolutionary method being shown to provide greater flexibility in its ability to adapt to incorporate/utilise available evidence/knowledge and develop effective and accurate network structures, at the cost of requiring additional computer programming skills. The dataset used to develop the models included GIS-based data taken from the Land Cover for Scotland 1988 (LCS88), Land Capability for Forestry (LCF), Land Capability for Agriculture (LCA), the soil map of Scotland and additional climatic variables.     

提高炼油废水处理场的氨氮去除率

对炼油废水处理场现有工艺进行流程优化,将MBR装置调整在接触氧化池之后运行.优化流程后氨氮平均去除率可从近于0提高至71.70％；COD平均去除率可从50.00％提高至72.00％.通过对工艺流程的对比,分析了现有工艺氨氮质量浓度难以降低的原因,指出废水中存在的某些污染物对硝化细菌起到抑制作用,减少了反应器中硝化细菌的...     

Probability of intellectual disability is associated with soil concentrations of arsenic and lead

Background

The association between metals in water and soil and adverse child neurologic outcomes has focused on the singular effect of lead (Pb), mercury (Hg), and arsenic (As). This study describes the complex association between soil concentrations of As combined with Pb and the probability of intellectual disability (ID) in children.

Methods

We used a retrospective cohort design with 3988 mother child pairs who were insured by Medicaid and lived during pregnancy and early childhood in South Carolina between 1/1/97 and 12/31/02. The children were followed until 6/1/08, using computerized service files, to identify the diagnosis of ID in medical records and verified by either school placement or disability service records. The soil was sampled using a uniform grid and analyzed for eight metals. The metal concentrations were interpolated using Bayesian Kriging to estimate concentration at individual residences.

Results

The probability of ID increased for increasing concentrations of As and Pb in the soil. The Odds Ratio for ID, for one unit change in As was 1.130 (95% confidence interval 1.048-1.218) for Pb was 1.002 (95% confidence interval 1.000-1.004). We identified effect modification for the infants based on their birth weight for gestational age status and only infants who were normal size for their gestational age had increased probability of ID based on the As and Pb soil concentrations (OR for As at normal weight for gestational age = 1.151 (95% CI: 1.061-1.249) and OR for Pb at normal for gestational age = 1.002 (95% CI: 1.002-1.004)). For normal weight for gestational age children when As = 22 mg kg⁻¹ and Pb = 200 mg kg⁻¹ the risk for ID was 11% and when As = 22 mg kg⁻¹and Pb = 400 mg kg⁻¹ the probability of ID was 65%.

Conclusion

The probability of ID is significantly associated with the interaction between Pb and As for normal weight for gestational age infants.     

Ethanol mediated As(III) adsorption onto Zn-loaded pinecone biochar: Experimental investigation, modeling, and optimization using hybrid artificial neural network-genetic algorithm approach

Organic matters (OMs) and their oxidization products often influence the fate and transport of heavy metals in the subsurface aqueous systems through interaction with the mineral surfaces. This study investigates the ethanol (EtOH)-mediated As(III) adsorption onto Zn-loaded pinecone (PC) biochar through batch experiments conducted under Box–Behnken design. The effect of EtOH on As(III) adsorption mechanism was quantitatively elucidated by fitting the experimental data using artificial neural network and quadratic modeling approaches. The quadratic model could describe the limiting nature of EtOH and pH on As(III) adsorption, whereas neural network revealed the stronger influence of EtOH (64.5%) followed by pH (20.75%) and As(III) concentration (14.75%) on the adsorption phenomena. Besides, the interaction among process variables indicated that EtOH enhances As(III) adsorption over a pH range of 2 to 7, possibly due to facilitation of ligand–metal(Zn) binding complexation mechanism. Eventually, hybrid response surface model–genetic algorithm (RSM–GA) approach predicted a better optimal solution than RSM, i.e., the adsorptive removal of As(III) (10.47 μg/g) is facilitated at 30.22 mg C/L of EtOH with initial As(III) concentration of 196.77 μg/L at pH 5.8. The implication of this investigation might help in understanding the application of biochar for removal of various As(III) species in the presence of OM.     

生物滴滤塔装置脱硝效率优化改进实验

针对生物滴滤塔系统脱硝效率偏低的问题,在原实验室装置基础上,对滴滤塔系统进行改进,并模拟燃煤烟气的特性,考察了重金属离子、氧气含量以及强化剂对净化效果的影响,并探究了生物滴滤塔系统对锅炉非稳定运行工况的适应性。结果显示:烟气内的重金属离子对微生物具有较强的抑制作用,其中As离子和Pb离子对体系微生物组成的毒性影响最大,Zn离子的影响最小;氧气含量对脱硝效率影响的研究结果表明,系统含氧量越低,脱硝效率降低幅度越大;强化剂组合结果表明,亚硝酸钠+Fe(II)EDTA的组合,强化效果最为明显;生物滴滤塔脱硝系统对锅炉非稳定运行工况适应能力较差,系统停运再启动,停运1周之内,系统经过10 d可以恢复至原来的性能,如果停运12 d以上,系统恢复则长达30 d。根据研究结果,为中试研究提出优化及研究方向建议。     

基于非概率可靠性的离散变量结构优化设计

目的针对工程结构设计中因材料供应规范限制使得设计变量不能连续取值以及材料性能波动、外界载荷偏差等不确定性问题,探索研究非概率可靠性在离散变量结构优化设计中的应用。方法以桁架结构杆件截面直径为设计变量、结构质量极小化为目标,考虑材料强度极限与外部载荷的不确定性,建立具有非概率可靠性指标约束的离散变量结构可靠性优化模型,并采用遗传算法求解离散变量结构优化设计问题。结果求解获得了三杆、十杆超静定桁架杆件截面直径最优组合,得到的优化结果相较确定性优化结果更具鲁棒性。结论非概率可靠性模型对于解决工程中设计变量具有离散性和不确定性的优化设计问题具有可行性。     

ABC算法优化SVR的磨损故障预测模型

目的为了提高故障预测的精度,针对支持向量回归SVR(Support vector machine for regression,SVR)参数选择困难的问题,提出一种采用人工蜂群(artificial bee colony,ABC)算法优化支持向量回归(SVR)的故障预测模型(ABC-SVR)。方法该模型先对样本数据进行重构,然后将故障预测误差(适应度)作为优化目标,通过ABC算法寻优找到最优的SVR参数,建立故障预测模型。最后通过实例仿真验证模型的优越性。结果采用ABC算法优化的SVR故障预测模型进行时间序列预测,能够较好地跟踪发动机滑油金属元素浓度的变化过程,并且能够提前2个取样时间预测异常情况的出现。结论 ABC-SVR模型有效解决了SVR参数选择难题,能够更加准确地表现故障变化规律,提高了故障预测精度。     

绿色会计视角下能源企业的成本核算优化研究

针对绿色会计视角下能源企业的成本核算优化,介绍了绿色会计视角下能源企业的成本核算,由于能源企业的发展,对环境产生了巨大危害,也导致能源企业由于本身成本过高,这才提出绿色会计,这样才能优化能源企业的成本,可以保证能源企业的可持续发展;同时,也保障了能源企业生态的稳定建设.基于绿色会计视角,探讨了能源企业的绿色成本优化研究,主要包括能源企业坚持绿色会计,确定绿色成本原则,能源企业绿色成本优化条件和能源企业加强绿色会计实践.     

城市建筑周边绿地景观格局优化方法研究

现有绿地景观格局优化方法,存在梯度变化不规律、边缘斑块密度不均等缺陷.针对该问题,提出基于窗口移动法的新型城市建筑周边绿地景观格局优化方法,通过确定研究区域与数据来源,以边缘斑块密度、梯度、绿地景观格局化指数为绿地景观格局化指数,确立研究方法.模拟研究环境设计实验结果表明,采用改进格局优化方法可改善城市建筑周边绿地景观格局梯度变化不规律问题;在特定空间分布状态下,还可避免边缘斑块密度不均的现象发生.     

摩擦清洗修复铅污染土壤的参数优化及清洗效率评价

采用摩擦清洗法修复铅污染场地土壤,主要目的是将细颗粒和铅污染物从砂粒表面去除.通过正交实验优化清洗参数,并对3份不同铅含量土壤进行摩擦清洗实验.优化的清洗参数为:水土比70%干物质、温度25℃、搅拌时间30 min、搅拌速率1 200 r.mim-1.摩擦清洗前土壤经过筛分化验分析表明,3份土壤铅主要富集在砂粒和粉黏粒中;铅的分布与有机质有极大相关性.结果表明,经过摩擦清洗后,3份原状铅污染土壤砂粒的清洗效率分别为:67.61%、31.71%、41.01%,摩擦清洗从砂粒表面去除一部分细粒土壤和铅污染物,使铅浓缩至粉黏粒中;扫描电镜(SEM)分析表明,摩擦清洗后砂粒表面变光滑.以上结论说明摩擦清洗对于铅污染场地的砂粒土壤具有较好的清洗效果.