An application of artificial intelligence for rainfall-runoff modeling

This study proposes an application of two techniques of artificial intelligence (AI) for rainfall-runoff modeling: the artificial neural networks (ANN) and the evolutionary computation (EC). Two different ANN techniques, the feed forward back propagation (FFBP) and generalized regression neural network (GRNN) methods are compared with one EC method, Gene Expression Programming (GEP) which is a new evolutionary algorithm that evolves computer programs. The daily hydrometeorological data of three rainfall stations and one streamflow station for Juniata River Basin in Pennsylvania state of USA are taken into consideration in the model development. Statistical parameters such as average, standard deviation, coefficient of variation, skewness, minimum and maximum values, as well as criteria such as mean square error (MSE) and determination coefficient (R ²) are used to measure the performance of the models. The results indicate that the proposed genetic programming (GP) formulation performs quite well compared to results obtained by ANNs and is quite practical for use. It is concluded from the results that GEP can be proposed as an alternative to ANN models.     

Case-based system for slope design

A key part of slope design is the review of past examples of slopes in similar ground conditions. This paper details the development of the SlopeSafe computer program which uses case-based reasoning to formalise this process. The program, written in Visual Basic, draws on a case-base of nearly 3000 case histories of successful and failed slopes to give an indication of the likely success of a proposed slope by matching its geometry and ground conditions to the slopes held in the case-base. XML (Extensible Markup Language) has been used to store the data and a specific set of tags has been defined to provide a standard way of storing slope information. The system has been identified by practising engineers as having the potential to be a very useful design tool.     

Neural networks and landslide susceptibility: a case study of the urban area of Potenza

For those working in the field of landslide prevention, the estimation of hazard levels and the consequent production of thematic maps are principal objectives. They are achieved through careful analytical studies of the characteristics of landslide prone areas, thus, providing useful information regarding possible future phenomena. Such maps represent a fundamental step in the drawing up of adequate measures of landslide hazard mitigation. However, for a complete estimation of landslide hazard, meant as the degree of probability that a landslide occurs in a given area, within a given space of time, detailed and uniformly distributed data regarding their incidence and causes are required. This information, while obtainable through laborious historical research, is usually partial, incomplete and uneven, and hence, unsatisfactory for zoning on a regional scale. In order to carry this out effectively, the utilization of spatial estimation of the relative levels of landslide hazard in the various areas was considered opportune. These areas were classified according to their levels of proneness to landslide activity without taking recurrence periods into account. Various techniques were developed in order to obtain upheaval numerical estimates. The method used in this study, which was applied in the area of Potenza, is based on techniques derived from artificial intelligence (Artificial Neural Network—ANN). This method requires the definition of appropriate thematic layers, which parameterize the area under study. These are recognized by means of specific analyses in a functional relationship to the event itself. The parameters adopted are: slope gradient, slope aspect, topographical index, topographical shape, elevation, land use and lithology.     

基于GIS和AI的城市区域内最佳路径算法研究

针对传统Dijkstra算法效率低的特点,从GIS和人工智能的角度提出了一种对传统Dijkstra算法进行优化的方式。首先根据城市交通网络的特点建立了交通网络拓扑图,提出了地名节点和交通节点的概念,并用合理的数据结构存储此交通网络,然后在此基础上使用一种新的启发函数以提高搜索效率,使优化过的Dijkstra算法能用于复杂的交通网络。     

Stable isotope tracers as diagnostic tools in upscaling flow path understanding and residence time estimates in a mountainous mesoscale catchment

δ¹⁸O measurements of precipitation and stream waters were used as a natural tracer to investigate hydrological pathways and residence times in the River Feshie, a complex mesoscale (231 km²) catchment in the Cairngorm Mountains of Scotland. Precipitation δ¹⁸O exhibited strong seasonal variation over the 2001–02 hydrological year, ranging from −6·9‰ in the summer, to −12·0‰ during winter snowfalls (mean δ¹⁸O −9·59‰). Although damped, this seasonality was reflected in stream water outputs at seven sampling sites in the catchment, allowing δ¹⁸O variations to be used to infer hydrological source areas. Thus, stream water δ¹⁸O was generally controlled by a seasonally variable storm flow end member, mixing with groundwater of more constant isotopic composition. Periodic regression analysis allowed the differences in this mixing process between monitoring subcatchments to be assessed more quantitatively to provide a preliminary estimate of mean stream water residence time. This demonstrated the importance of responsive hydrological pathways associated with peat and shallow alpine soils in the headwater subcatchments in producing seasonally variable runoff with short mean residence times (33–113 days). In contrast, other tributaries with more freely draining soils and larger groundwater storage in shallow aquifers provided more effective mixing of variable precipitation inputs, resulting in longer residence time estimates (178–445 days). The mean residence time of runoff leaving the Feshie catchment reflected an integration of these contrasting influences (110–200 days). These insights from δ¹⁸O measurements extend the hydrological understanding of the Feshie catchment gained from other hydrochemical tracers, and demonstrate the utility of isotope tracers in investigating hydrological processes at the mesoscale. Copyright © 2005 John Wiley & Sons, Ltd.     

Groundwater–surface water interactions in a large semi‐arid floodplain: implications for salinity management

Flow regulation and water diversion for irrigation have considerably impacted the exchange of surface water between the Murray River and its floodplains. However, the way in which river regulation has impacted groundwater–surface water interactions is not completely understood, especially in regards to the salinization and accompanying vegetation dieback currently occurring in many of the floodplains. Groundwater–surface water interactions were studied over a 2 year period in the riparian area of a large floodplain (Hattah–Kulkyne, Victoria) using a combination of piezometric surface monitoring and environmental tracers (Cl⁻, δ²H, and δ¹⁸O). Despite being located in a local and regional groundwater discharge zone, the Murray River is a losing stream under low flow conditions at Hattah–Kulkyne. The discharge zone for local groundwater, regional groundwater and bank recharge is in the floodplain within ∼1 km of the river and is probably driven by high rates of transpiration by the riparian Eucalyptus camaldulensis woodland. Environmental tracers data suggest that the origin of groundwater is principally bank recharge in the riparian zone and a combination of diffuse rainfall recharge and localized floodwater recharge elsewhere in the floodplain. Although the Murray River was losing under low flows, bank discharge occurred during some flood recession periods. The way in which the water table responded to changes in river level was a function of the type of stream bank present, with point bars providing a better connection to the alluvial aquifer than the more common clay‐lined banks. Understanding the spatial variability in the hydraulic connection with the river channel and in vertical recharge following inundations will be critical to design effective salinity remediation strategies for large semi‐arid floodplains. Copyright © 2005 John Wiley & Sons, Ltd.     

小竹山岛人工鱼礁区大型底栖动物群落结构的初步研究

为探究小竹山岛海域投放人工鱼礁后大型底栖动物群落结构的变化, 于2017—2020年进行了8个航次的调查, 研究鱼礁区、邻近区与对照区的大型底栖动物种类组成、优势种、生物多样性、群落结构及群落稳定性。结果显示, 共鉴定出大型底栖动物72种, 其中多毛类45种, 甲壳类16种, 软体动物8种, 棘皮动物3种, 投礁后第4年(2020年)比投礁后第1年(2017年)种类数增加了15种, 并且鱼礁区的种类数多于邻近区与对照区。优势种组成以多毛类为主。调查海域大型底栖动物生物量和丰度呈逐年上升趋势, 年份间差异显著, 2017—2020年年均生物量从2.11 g/m²上升到4.38 g/m², 年均丰度从289.31 ind./m²上升到764.14 ind./m², 区域间无显著差异。多样性指数、丰富度指数和均匀度指数在不同年份和区域之间变化不显著。聚类分析结果显示, 大型底栖动物群落结构分布格局呈现年际变化; 双因素相似性分析结果表明, 调查海域大型底栖动物群落结构的年份差异显著(P<0.01), 区域间无显著差异(P>0.05); 相似性百分比分析结果表明, 深沟毛虫(Sigambra bassi)、长叶索沙蚕(Lumbrineris longiforlia)、中蚓虫(Mediomastus californiensis)和寡鳃齿吻沙蚕(Micronephthys oligobranchia)等是造成群落年际差异的主要贡献种。丰度/生物量曲线结果表明, 2017—2020年大型底栖动物群落均未受到扰动, 随着鱼礁建设时间的推移, 大型底栖动物群落更趋稳定。     

南丹大厂矿区人工爆破与天然地震的判定研究

利用南丹大厂矿区地震监测台网的数字波形记录资料,采用波形对比方法,分析大厂矿区人工爆破与天然地震波形特征。分析结果表明,爆破与地震在P波平均周期具有不同的特征,可为今后区分该大厂矿区爆破与天然地震提供判定判据。。     

Mineralogical alteration of artificial meteorites during atmospheric entry. The STONE-5 experiment

The generic concept of the artificial meteorite experiment STONE is to fix rock samples bearing microorganisms on the heat shield of a recoverable space capsule and to study their modifications during atmospheric re-entry. The STONE-5 experiment was performed mainly to answer astrobiological questions. The rock samples mounted on the heat shield were used (i) as a carrier for microorganisms and (ii) as internal control to verify whether physical conditions during atmospheric re-entry were comparable to those experienced by “real” meteorites. Samples of dolerite (an igneous rock), sandstone (a sedimentary rock), and gneiss impactite from Haughton Crater carrying endolithic cyanobacteria were fixed to the heat shield of the unmanned recoverable capsule FOTON-M2. Holes drilled on the back side of each rock sample were loaded with bacterial and fungal spores and with dried vegetative cryptoendoliths. The front of the gneissic sample was also soaked with cryptoendoliths.

The mineralogical differences between pre- and post-flight samples are detailed. Despite intense ablation resulting in deeply eroded samples, all rocks in part survived atmospheric re-entry. Temperatures attained during re-entry were high enough to melt dolerite, silica, and the gneiss impactite sample. The formation of fusion crusts in STONE-5 was a real novelty and strengthens the link with real meteorites. The exposed part of the dolerite is covered by a fusion crust consisting of silicate glass formed from the rock sample with an admixture of holder material (silica). Compositionally, the fusion crust varies from silica-rich areas (undissolved silica fibres of the holder material) to areas whose composition is “basaltic”. Likewise, the fusion crust on the exposed gneiss surface was formed from gneiss with an admixture of holder material. The corresponding composition of the fusion crust varies from silica-rich areas to areas with “gneiss” composition (main component potassium-rich feldspar). The sandstone sample was retrieved intact and did not develop a fusion crust. Thermal decomposition of the calcite matrix followed by disintegration and liberation of the silicate grains prevented the formation of a melt.

Furthermore, the non-exposed surface of all samples experienced strong thermal alterations. Hot gases released during ablation pervaded the empty space between sample and sample holder leading to intense local heating. The intense heating below the protective sample holder led to surface melting of the dolerite rock and to the formation of calcium-silicate rims on quartz grains in the sandstone sample.