南四湖人工湿地建设示范工程

人工湿地具有高效率、低投资、低运转费、低维持技术、处理量灵活、低能耗、处理效果好等优点。济宁市为了更好地治理南四湖水污染,在流入南四湖境内的重点河流开展了人工湿地建设工程。南四湖人工湿地的建设产生了极大的社会、经济和生态效益。总结了南四湖人工湿地建设措施,提出了相关的建议。     

人工林生态采运研究进展

针对如何推动人工林可持续经营,扼要归纳国内外在人工林生态采运理论及其相关技术的研究与应用现状,分析其存在问题,提出进一步探索适合我国人工林生态采运理论和作业技术的对策,展望我国今后人工林生态采运研究的前景。     

自然湿地与人工湿地生态占补平衡研究

自然湿地与人工湿地的功能与效益并不完全相同。实现湿地生态占补平衡,必须考虑占补湿地的替代性与同质性。人工湿地与自然湿地的替代性是解决湿地生态占补平衡的关键问题之一。建立自然湿地与人工湿地占补平衡的约束机制,占用自然湿地时,可以新建人工湿地,还可以恢复自然湿地。通过确保自然湿地生态量、生态功能与生态效益的均衡,减少湿地的生态损失。尽可能在同一水文单位或同流域内实现湿地生态占补平衡。资金投入要兼顾现有自然湿地的生态恢复与新建人工湿地。     

南四湖人工湿地建设与管理

采用生态滞留塘+多级表面流湿地系统的工艺方案建设南四湖人工湿地,使出水水质基本达到《地表水环境质量标准》Ⅲ类水质要求。利用人工湿地的生态经济价值,采取市场化运营的模式,探索"以湿养湿,以收养管"的管理机制,使南四湖人工湿地既能保障南水北调安全顺利进行,又能自身循环发展,取得"保水创收"的双赢效果。     

城市生态住宅小区分散式雨水人工湿地处理研究

以小区为单位的城市雨水分散式收集利用有利于减轻城市洪涝灾害,减小城市排水管网长度和管径,缓解城市供水压力,对改善城市水环境和生态环境具有重要意义。本文提出利用人工湿地处理小区地表径流并回用于城市地下水补给、绿化的思路,论述了城市雨水人工湿地再生处理利用系统中主要工艺的设计计算方法,为缓解城市用水压力提供有效途径。     

A Novel Artificial Intelligence Approach to Predict Blast-Induced Ground Vibration in Open-Pit Mines Based on the Firefly Algorithm and Artificial Neural Network

Natural Resources Research - The primary purpose of this study was to develop a novel hybrid artificial intelligence model, with a robust performance, to predict ground vibration induced by bench...     

Artificial Maturation of Organic Matter from Recent Venezuelan Sediments

Laboratory simulation of the catagenesis of organic matter in sedimentary rocks has been used to provide an understanding of the processes involved in petroleum generation. Several of these studies have focused on the thermal evolution of organic matter (OM) present in Recent sediments. This study examines the geochemical characteristics and experimental thermal evolution of primary organic matter from two organic facies that are thought to be major contributors to Venezuelan hydrocarbon source rocks. A third facies, generally considered unimportant for petroleum formation, is used to contrast the experimental results. Hydrous pyrolysis maturation experiments were performed for three intermediate temperatures. The products of the final 330°C stage are shown in this paper because they best illustrate the changes in the OM during catagenesis. Results from the hydrous pyrolysis experiments show that at 280°C and higher all three samples yield liquid hydrocarbons similar in composition to natural crudes and the transformed organic matter is similar to kerogen that occurs in natural source rocks. Chromatograms from the saturated fraction of extracts at 330°C are similar to natural crudes with respect to n-alkane distribution and abundance of beta; and beta;alpha; hopanes. The only difference seems to be the relative abundance of 22R over 22S isomers, which indicates immature oil. This is in contrast to indications from the R _o and T _max parameters measured on the accompanying kerogen.     

吐鲁番盆地人工增水作业区域与作业时机选择

利用吐哈盆地2011-2015年逐时FY-2E静止气象卫星红外云图资料,吐鲁番市1976-2015年5个国家气象站和2013-2015年26个区域气象站降水资料,采用卫星资料反演和统计分析方法,首次定义T_K（地面气温与云顶亮温的差值）来规避地面辐射对卫星接收辐射的影响,分析吐鲁番盆地各级别TBB（-10~-20℃、-20~-30℃、-30~-40℃、-40~-50℃）云的分布状况及其与降水的关系、降水的时空分布特征和变化趋势。结果表明,吐鲁番盆地TBB各级别云覆盖度与海拔高度显著正相关,云量从盆地平原区向山区递增;T_K的月变化同月降水具有较好的正相关性,TK正值时段4~8月与盆地汛期相对应,T_K极大值对应月降水量最大的6月;降水与海拔高度显著正相关,降水先随海拔高度增加而增多,1 400~1 900 m区域是降水量和降水垂直变率最大的区域,之后降水随海拔高度增加而减少;降水高度集中在夏季与秋季,6月降水最多（占3~4成）;降水集中出现在白天,平原地区集中在早晨至中午,山区集中在下午至傍晚。综合分析得出吐鲁番盆地人工增水作业区域、作业月份、作业"时间窗"选择的参考依据,其中最佳作业区域在1 400~1 900 m,最佳作业月份为6月,最佳作业"时间窗"为上午的06~10时与下午的14~18时。     

Artificial neural networks and cluster analysis in landslide susceptibility zonation

A landslide susceptibility analysis is performed by means of Artificial Neural Network (ANN) and Cluster Analysis (CA). This kind of analysis is aimed at using ANNs to model the complex non linear relationships between mass movements and conditioning factors for susceptibility zonation, in order to identify unstable areas. The proposed method adopts CA to improve the selection of training, validation, and test records from data, managed within a Geographic Information System (GIS). In particular, we introduce a domain-specific distance measure in cluster formation. Clustering is used in data pre-processing to select non landslide records and is performed on the whole dataset, excluding the test set landslides. Susceptibility analysis is carried out by means of ANNs on the so-generated data and compared with the common strategy to select random non-landslide samples from pixels without landslides. The proposed method has been applied in the Brembilla Municipality, a landslide-prone area in the Southern Alps, Italy. The results show significant differences between the two sampling methods: the classification of the test set, previously separated and excluded from the training data, is always better when the non-landslide patterns are obtained using the proposed cluster sampling. The case study validates that, by means of a domain-specific distance measure in cluster formation, it is possible to introduce expert knowledge into the black-box modelling method, implemented by ANNs, to improve the predictive capability and the robustness of the models obtained.     

Application of Artificial Neural Networks to Complex Groundwater Management Problems

As water quantity and quality problems become increasingly severe, accurate prediction and effective management of scarcer water resources will become critical. In this paper, the successful application of artificial neural network (ANN) technology is described for three types of groundwater prediction and management problems. In the first example, an ANN was trained with simulation data from a physically based numerical model to predict head (groundwater elevation) at locations of interest under variable pumping and climate conditions. The ANN achieved a high degree of predictive accuracy, and its derived state-transition equations were embedded into a multiobjective optimization formulation and solved to generate a trade-off curve depicting water supply in relation to contamination risk. In the second and third examples, ANNs were developed with real-world hydrologic and climate data for different hydrogeologic environments. For the second problem, an ANN was developed using data collected for a 5-year, 8-month period to predict heads in a multilayered surficial and limestone aquifer system under variable pumping, state, and climate conditions. Using weekly stress periods, the ANN substantially outperformed a well-calibrated numerical flow model for the 71-day validation period, and provided insights into the effects of climate and pumping on water levels. For the third problem, an ANN was developed with data collected automatically over a 6-week period to predict hourly heads in 11 high-capacity public supply wells tapping a semiconfined bedrock aquifer and subject to large well-interference effects. Using hourly stress periods, the ANN accurately predicted heads for 24-hour periods in all public supply wells. These test cases demonstrate that the ANN technology can solve a variety of complex groundwater management problems and overcome many of the problems and limitations associated with traditional physically based flow models.     

人为补水对扎龙河漫滩湿地植被的影响

通过对扎龙河漫滩湿地植被定位观测,对比研究人为补水对湿地植被的影响。人为补水后,湿地植被发生了湿生化演替趋势。湿草甸植物生活型谱中,地面芽植物有所增加,挺水植物沼泽则无明显变化;湿地与草地过渡带植物生活型组成对水位增加反应敏感,典型湿地植被生活型组成变化不大。研究表明,水位波动强烈的河漫滩湿地中,水文学的微弱改变可以导致湿地植被的明显变化。湿地的人为补水应结合准确的气候预测,进行湿地生态需水量研究,确定合理的补水量和补水时间。     

基于神经网络的西藏拉萨地区生态环境分类

根据生态环境分类指标的科学性、完备性、简洁性和数据的可获取性,选取了影响拉萨地区生态环境的主要地形和气候因子高程、坡向、≥0℃积温、年平均温度、年平均降水量、潜在蒸散量和湿润度等7个有代表性的指标,利用GIS的空间内插方法将所有这些指标转成100 m×100 m的空间珊格数据,再根据每个指标特定的地理和环境意义进行指标的分带,对7个指标进行主成分分析后提取主要信息。通过选择4个典型样区作为训练区,对拉萨地区的生态环境进行了分类。结果表明,拉萨地区的主要生态环境类型包括河谷农业类型、山地草原类型、高山草甸类型及高山裸岩及冰雪类型。其中,高山草甸和山地草原生态环境类型占主导,分别为10 768.52 km2和10 646.6 km2,各占总面积的36.61%和36.20%,而河谷农业类型占总面积的10.75%。此外,拉萨地区分布有较大面积的高山裸岩及冰雪区生态环境类型,面积为总面积的14.16%。作为特殊类型的生态环境类型,拉萨地区境内的纳木错的湖泊面积是668.76 km2,占该湖面积的近一半和拉萨地区总面积的2.27%。     

污水处理型人工湿地构建中几个问题的探讨

污水处理型人工湿地是利用基质、湿生植物协同净化水体污染物,具有良好的景观价值与生态功能。目前,人工湿地的实际构建中,在基质和植物选择、运行管理中存在一系列问题,就这几方面问题进行分析并探讨解决方案和思路。     

四川邛海人工湿地综合评价

运用层次分析法及综合指标评价法,提出并建立了人工湿地综合评价指标体系,选取选址适宜性、构建、水质净化功能和经济社会效益4个一级指标及对应的15个二级指标作为评价指标。以邛海湿地五期内小河沟人工湿地作为探究对象,结果显示:小河沟人工湿地的综合评指数为7.38,处于"良",邛海人工湿地具有较好的水质净化功能,在选址及构建上处于良好水平,在运行管理上应加强垃圾清理和植被管理。     

蓝田、长安人工林地土层含水量研究

根据蓝田孟村杨树林和梧桐林、长安县韦曲镇南杨树林地与西安市南郊麦地土壤含水量的测定,研究了0～600cm土壤含水量的变化和土壤干层特点与分布。实验表明,蓝田、长安地区在正常降雨年份人工林地土壤含水量从地表向地下呈现由高到低再到高的变化：12龄杨树林、14龄梧桐林、13龄和10龄杨树林地180～360cm深度范围内土壤含水量平均为9．5％、9．3％、9．0和9．2％,发育了明显的土壤干层：麦地土层含水量较梧桐林和杨树林地明显高,无土壤干化的显示;在丰水年蓝田、长安地区人工林土层含水量与正常降雨年份林地土壤含水量显著不同,12龄杨树林、14龄梧桐林、13龄和10龄杨树林地180～360cm深度范围内土壤含水量平均为22．5％、23．2％、22．5％和22．4％,土壤干层消失。这表明在降水量增加的条件下,土壤干层中的水分完全有可能恢复。土壤干层的发育会影响植物的正常生长,植被类型,植树造林和生态环境恢复,因此土壤干层水分恢复有重要意义。