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1.
《Marine pollution bulletin》1987,18(8):446-450
Short-term studies on man-made litter deposited on the beaches of the Edinburgh coastline of the Firth of Forth, Scotland, have demonstrated that containers of all kinds, plastic bags, plastic sheeting and clothing, comprise the main and dominant components of the nineteen categories of litter analysed. Most of the litter is of local origin either deposited in situ or washed ashore from neighbouring water's-edge tips. Very little evidence was found of litter washed ashore being of foreign origin or having been tipped overboard from ships at sea. Much of the smaller items of litter are discarded by visitors to the beach. The solution to the problem is seen as one of education at all levels rather than ineffective punitive measures. 相似文献
2.
A cobble beach (-6 diameter to -8 diameter) located on the South Wales coastline, UK, was studied over a three-month winter period to assess litter input levels. After total beach litter clearance, six surveys were conducted at consecutive spring tides which involved marking of previously unrecorded litter. The beach was soon inundated with debris, predominantly plastic beverage containers. Some marked litter was found to disappear from the beach surface, re-emerging weeks later which suggests that the potential for litter burial has been underestimated in litter research. Higher wave energies between surveys coincided with higher levels of previously unseen litter. These new inputs consisted of sea borne and exhumed litter. Items larger than the surrounding cobbles were found to work their way back to the surface of the beach after burial, smaller items remained buried. Pits dug into the cobble ridge confirmed the burial of mainly small items. 相似文献
3.
砂土地震液化问题是岩土地震工程学的重要研究课题之一。在分析模糊神经网络原理的基础上,利用减法聚类算法对自适应模糊推理系统进行优化,并建立了砂土地震液化的模糊神经网络模型。然后,将该模型用于实际工程的砂土液化判别中,并与传统砂土液化判别方法结果进行对比。判别结果表明:文中建立的模糊神经网络模型具有较强的学习功能,用于砂土地震液化判别中是可行的和有效的。 相似文献
4.
In this study, several types of adaptive network‐based fuzzy inference system (ANFIS) with different membership functions (MFs) and artificial neural network (ANN) were employed to predict hourly photochemical oxidants that were oxidizing substances such as ozone and peroxiacetyl nitrate produced by photochemical reactions. The results indicated that ANFIS statistically outperforms ANN in terms of hourly oxidant prediction. The minimum mean absolute percentage errors (MAPEs) of 4.99% could be achieved using ANFIS with bell shaped MFs. The maximum correlation coefficient, the minimum mean square errors, and the minimum root mean square errors were 0.99, 0.15, and 0.39, respectively. ANFIS's architecture consists of both ANN and fuzzy logic including linguistic expression of MFs and if‐then rules, so it can overcome the limitations of traditional neural network and increase the prediction performance. 相似文献
5.
Fuzzy neural network models for liquefaction prediction 总被引：1，自引：0，他引：1
Integrated fuzzy neural network models are developed for the assessment of liquefaction potential of a site. The models are trained with large databases of liquefaction case histories. A two-stage training algorithm is used to develop a fuzzy neural network model. In the preliminary training stage, the training case histories are used to determine initial network parameters. In the final training stage, the training case histories are processed one by one to develop membership functions for the network parameters. During the testing phase, input variables are described in linguistic terms such as ‘high’ and ‘low’. The prediction is made in terms of a liquefaction index representing the degree of liquefaction described in fuzzy terms such as ‘highly likely’, ‘likely’, or ‘unlikely’. The results from the model are compared with actual field observations and misclassified cases are identified. The models are found to have good predictive ability and are expected to be very useful for a preliminary evaluation of liquefaction potential of a site for which the input parameters are not well defined. 相似文献
6.
The present study aims to develop a hybrid multi‐model using the soft computing approach. The model is a combination of a fuzzy logic, artificial neural network (ANN) and genetic algorithm (GA). While neural networks are low‐level computational structures that perform well dealing with raw data, fuzzy logic deal with reasoning on a higher level by using linguistic information acquired from domain experts. However, fuzzy systems lack the ability to learn and cannot adjust themselves to a new environment. Moreover, experts occasionally make mistakes and thus some rules used in a system may be false. A network type structure of the present hybrid model is a multi‐layer feed‐forward network, the main part is a fuzzy system based on the first‐order Sugeno fuzzy model with a fuzzification and a defuzzification processes. The consequent parameters are determined by least square method. The back‐propagation is applied to adjust weights of network. Then, the antecedent parameters of the membership function are updated accordingly by the gradient descent method. The GA was applied to select the fuzzy rule. The hybrid multi‐model was used to forecast the flood level at Chiang Mai (under the big flood 2005) and the Koriyama flood (2003) in Japan. The forecasting results are evaluated using standard global goodness of fit statistic, efficient index (EI), the root mean square error (RMSE) and the peak flood error. Moreover, the results are compared to the results of a neuro‐genetic model (NGO) and ANFIS model using the same input and output variables. It was found that the hybrid multi‐model can be used successfully with an efficiency index (EI) more than 0·95 (for Chiang Mai flood up to 12 h ahead forecasting) and more than 0·90 (for Koriyama flood up to 8 h ahead forecasting). In general, all of three models can predict the water level with satisfactory results. However, the hybrid model gave the best flood peak estimation among the three models. Therefore, the use of fuzzy rule base, which is selected by GA in the hybrid multi‐model helps to improve the accuracy of flood peak. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献
7.
《Ground Water Monitoring & Remediation》1989,9(4):149-158
Ground water professionals within public and private sectors use well records as data sources. Both the availability and the technical content of domestic well records in the 50 states are of interest to them. Well record availability is dependent on legal requirements, filing systems, databases, and storage places. Forty-six states have statewide regulations or other legal requirements for filing completion reports for domestic wells. Fifty-one agencies across the country maintain domestic well records. Filing systems involve location, chronology, date, or number information. Thirty-one of the 51 agencies maintain varying types of databases containing record information or data related to the records. Overall, records are kept in central offices in 43 states and in regional offices in three states.
The technical content of the records was evaluated for general, location, hydrogeology, and well construction information to assess the relative value of the records for use in national pesticide surveys. Technical information tabulated from the well records collected for this paper included nine items in a general category, 28 items in a well-construction category, eight items in a hydrogeology category, and six items in a location category. Items in the general and location categories identified the well location and ownership. Construction category items include those describing well-construction parameters such as grout, casing, and screen. Hydrogeology category items include static water level, aquifer media, and estimated yield. The three items always requested were owner's name, driller's name, and static water level. The three least-requested items, ranging from 16 percent to 10 percent, were packers, drilling fluid, and geologic formation. 相似文献
The technical content of the records was evaluated for general, location, hydrogeology, and well construction information to assess the relative value of the records for use in national pesticide surveys. Technical information tabulated from the well records collected for this paper included nine items in a general category, 28 items in a well-construction category, eight items in a hydrogeology category, and six items in a location category. Items in the general and location categories identified the well location and ownership. Construction category items include those describing well-construction parameters such as grout, casing, and screen. Hydrogeology category items include static water level, aquifer media, and estimated yield. The three items always requested were owner's name, driller's name, and static water level. The three least-requested items, ranging from 16 percent to 10 percent, were packers, drilling fluid, and geologic formation. 相似文献
8.
基于模糊神经网络和符号的地震预报专家系统NGESEP 总被引：7，自引：0，他引：7
本文介绍了专家系统的发展、神经网络、模糊系统与专家系统相结合的优点以及新一代地震报专家系统的构成等。该系统除具有传统专家系统的特点外，还因使用模糊联想记忆神经网络模型而具有良好的学习功能。文中也对ＦＡＭ神经网络模型及其应用作了介绍。 相似文献
9.
Sequential monitoring of beach litter using webcams 总被引：1，自引：0，他引：1
This study attempts to establish a system for the sequential monitoring of beach litter using webcams placed at the Ookushi beach, Goto Islands, Japan, to establish the temporal variability in the quantities of beach litter every 90 min over a one and a half year period. The time series of the quantities of beach litter, computed by counting pixels with a greater lightness than a threshold value in photographs, shows that litter does not increase monotonically on the beach, but fluctuates mainly on a monthly time scale or less. To investigate what factors influence this variability, the time derivative of the quantity of beach litter is compared with satellite-derived wind speeds. It is found that the beach litter quantities vary largely with winds, but there may be other influencing factors. 相似文献
10.
Establishment of numerical beach-litter hindcast/forecast models: an application to Goto Islands, Japan 总被引：1，自引：0，他引：1
This study attempts to establish a system for hindcasting/forecasting the quantity of litter reaching a beach using an ocean circulation model, a two-way particle tracking model (PTM) to find litter sources, and an inverse method to compute litter outflows at each source. Twelve actual beach survey results, and satellite and forecasted wind data were also used. The quantity of beach litter was hindcasted/forecasted using a forward in-time PTM with the surface currents computed in the ocean circulation model driven by satellite-derived/forecasted wind data. Outflows obtained using the inverse method was given for each source in the model. The time series of the hindcasted/forecasted quantity of beach litter were found consistent with the quantity of beach litter determined from sequential webcam images of the actual beach. The accuracy of the model, however, is reduced drastically by intense winds such as typhoons which disturb drifting litter motion. 相似文献
11.
Theodore R. Merrell 《Marine pollution bulletin》1984,15(10):378-384
Ten 1 km beaches on Amchitka Island, Alaska, were surveyed once annually in 1972–1974 and in 1982 to determine weights and numbers of fish-net fragments and other plastic litter items. Most litter was from Japanese and Soviet fishing vessels. Litter rapidly increased during 1972–74 (from 122 to 345 kg km?1 of beach) but decreased 26% by 1982 to 255 kg km?1. There was a 37% reduction in weight of trawl web on Amchitka beaches, and the number of gill-net floats declined 47%. The decrease in litter between 1974 and 1982, attributed to fewer trawlers and gill-netters fishing off Alaska, shows that marine litter could be rapidly reduced if disposal of litter at sea were restricted. 相似文献
12.
13.
Applying active control systems to civil engineering structures subjected to dynamic loading has received increasing interest. This study proposes an active pulse control model, termed unsupervised fuzzy neural network structural active pulse controller (UFN‐SAP controller), for controlling civil engineering structures under dynamic loading. The proposed controller combines an unsupervised neural network classification (UNC) model, an unsupervised fuzzy neural network (UFN) reasoning model, and an active pulse control strategy. The UFN‐SAP controller minimizes structural cumulative responses during earthquakes by applying active pulse control forces determined via the UFN model based on the clusters, classified through the UNC model, with their corresponding control forces. Herein, we assume that the effect of the pulses on structure is delayed until just before the next sampling time so that the control force can be calculated in time, and applied. The UFN‐SAP controller also averts the difficulty of obtaining system parameters for a real structure for the algorithm to allow active structural control. Illustrative examples reveal significant reductions in cumulative structural responses, proving the feasibility of applying the adaptive unsupervised neural network with the fuzzy classification approach to control civil engineering structures under dynamic loading. Copyright © 2001 John Wiley & Sons, Ltd. 相似文献
14.
Application of experimental design techniques to structural simulation meta-model building using neural network 总被引：1，自引：0，他引：1
Neural networks are being used to construct meta-models in numerical simulation of structures. In addition to network structures and training algorithms, training samples also greatly affect the accuracy of neural network models. In this paper, some existing main sampling techniques are evaluated, including techniques based on experimental design theory,random selection, and rotating sampling. First, advantages and disadvantages of each technique are reviewed. Then, seven techniques are used to generate samples for training radial neural networks models for two benchmarks: an antenna model and an aircraft model. Results show that the uniform design, in which the number of samples and mean square error network models are considered, is the best sampling technique for neural network based meta-model building. 相似文献
15.
A simulation-based fuzzy chance-constrained programming model for optimal groundwater remediation under uncertainty 总被引：3，自引：0，他引：3
In this study a simulation-based fuzzy chance-constrained programming (SFCCP) model is developed based on possibility theory. The model is solved through an indirect search approach which integrates fuzzy simulation, artificial neural network and simulated annealing techniques. This approach has the advantages of: (1) handling simulation and optimization problems under uncertainty associated with fuzzy parameters, (2) providing additional information (i.e. possibility of constraint satisfaction) indicating that how likely one can believe the decision results, (3) alleviating computational burdens in the optimization process, and (4) reducing the chances of being trapped in local optima. The model is applied to a petroleum-contaminated aquifer located in western Canada for supporting the optimal design of groundwater remediation systems. The model solutions provide optimal groundwater pumping rates for the 3, 5 and 10 years of pumping schemes. It is observed that the uncertainty significantly affects the remediation strategies. To mitigate such impacts, additional cost is required either for increased pumping rate or for reinforced site characterization. 相似文献
16.
This paper explores the potential of using neural networks to identify the internal forces of typical systems encountered in the field of earthquake engineering and structural dynamics. After formulating the identification task as a neural network learning procedure, the method is applied to a representative chain-like system under deterministic and stochastic excitations. The neural network based identification method provides very good results for general classes of multi-degree-of-freedom structural systems. The range of validity of the approach is demonstrated, and some application issues are discussed for (a) partially known multi-degree-of-freedom systems and (b) completely unknown systems. 相似文献
17.
Chau KW 《Marine pollution bulletin》2006,52(7):726-733
With the development of computing technology, numerical models are often employed to simulate flow and water quality processes in coastal environments. However, the emphasis has conventionally been placed on algorithmic procedures to solve specific problems. These numerical models, being insufficiently user-friendly, lack knowledge transfers in model interpretation. This results in significant constraints on model uses and large gaps between model developers and practitioners. It is a difficult task for novice application users to select an appropriate numerical model. It is desirable to incorporate the existing heuristic knowledge about model manipulation and to furnish intelligent manipulation of calibration parameters. The advancement in artificial intelligence (AI) during the past decade rendered it possible to integrate the technologies into numerical modelling systems in order to bridge the gaps. The objective of this paper is to review the current state-of-the-art of the integration of AI into water quality modelling. Algorithms and methods studied include knowledge-based system, genetic algorithm, artificial neural network, and fuzzy inference system. These techniques can contribute to the integrated model in different aspects and may not be mutually exclusive to one another. Some future directions for further development and their potentials are explored and presented. 相似文献
18.
This study has demonstrated a reliable method of quantifying the total mass of litter on a beach. It was conducted on Ookushi beach, Goto-Islands, Japan, and uses a combination of balloon-assisted aerial photography and in situ mass measurements. The total mass of litter over the beach was calculated to be 716 ± 259 kg. This figure was derived by multiplying the litter-covered area (calculated using balloon-assisted aerial photography) by the mass of litter per unit area. Light plastics such as polyethylene made up 55% of all plastic litter on the beach, although more work is needed to determine whether lighter plastics are transported to beaches more readily by winds and ocean currents compared with heavier plastics, or whether lighter plastics comprise a greater percentage of marine litter. Finally, the above estimates were used to calculate the total mass of metals released into coastal ecosystems via plastic litter on beaches. 相似文献
19.
Determinants of sediment properties and organic matter in beach and dune environments based on boosted regression trees 
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下载免费PDF全文 Grain size properties and the variation of organic matter in coastal beach and dune environments are assumed to be controlled by the intensity of aeolian processes, time and the sediment source. However, assumptions are based on relatively limited empirical studies. In this study, we examined which environmental variables are the main predictors of multiple topsoil properties. To achieve this, we analysed an extensive dataset systematically collected across all beach zones and a large geographical area at the Finnish Baltic Sea coast characterized by post‐glacial land uplift. We included a comprehensive set of predictors in the analysis and applied boosted regression trees, a modern modelling technique particularly suited for analysis without prior assumptions of the data model. The results suggest that mean grain size and sorting are mainly determined by northing and fetch. Northing, disturbance and fetch predicted the variation of soil organic matter while litter cover was strongly related to disturbance. Based on the analyses, we were able to identify the main drivers of multiple topsoil properties on land uplift beaches. Parent material is suggested to determine sediment textural properties, which largely masks the effects of transient processes. Mean grain size and sorting are highly interdependent: grains become finer and sorting improves with increasing shore exposure. The intensity of momentary geomorphic processes controls the accumulation of litter whereas the slower accumulation of organic matter in the soil is influenced also by the static exposure setting. Skewness and kurtosis of the grain size distribution are mainly influenced by unmeasured processes, potentially relating to the geomorphological origin of the sediment. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
20.
The drivers (social) and pressures (physical) of marine debris have typically been examined separately. We redress this by using social and beach surveys at nine Tasmanian beaches, across three coastlines and within three categories of urbanisation, to examine whether people acknowledge that their actions contribute to the issue of marine debris, and whether these social drivers are reflected in the amount of marine debris detected on beaches. A large proportion (75%) of survey participants do not litter at beaches; with age, gender, income and residency influencing littering behaviour. Thus, participants recognise that littering at beaches is a problem. This social trend was reflected in the small amounts of debris that were detected. Furthermore, the amount of debris was not statistically influenced by the degree of beach urbanisation, the coastline sampled, or the proximity to beach access points. By linking social and physical aspects of this issue, management outcomes can be improved. 相似文献