Probabilistic Visibility Forecasting Using Neural Networks

Statistical methods are widely applied in visibility forecasting. In this article, further improvements are explored by extending the standard probabilistic neural network approach. The first approach is to use several models to obtain an averaged output, instead of just selecting the overall best one, while the second approach is to use deterministic neural networks to make input variables for the probabilistic neural network. These approaches are extensively tested at two sites and seen to improve upon the standard approach, although the improvements for one of the sites were not found to be of statistical significance.     

人工神经网络在地震中短期预报中的应用

本文将ＢＰ神经网络用于地震中短期预报。作者把一些常用的地震学指标作为神经网络的输入,而将ＢＰ神经网络的输出作为表征地震活动平静的特征参数Ｗｑ,井将其用于华北地区进行空间扫描,结果表明中强地震前１年左右或稍长时间,未来震中周围一般都开始出现Ｗｑ值的中短期异常区,证明本方法具有限好的中短期预报效果。     

Sub-basalt imaging problems and the application of Artificial Neural Networks

In many areas of the world, the presence of shallow high velocity, highly heterogeneous layers complicate seismic imaging of deeper reflectors. Of particular economic interest are areas where potentially hydrocarbon-bearing strata are obscured by layers of basalt. Basalt layers are highly reflective and heterogeneous. Using reflection seismic, top basalt is typified by a high-amplitude, coherent reflector with poor resolution of reflectors below the basalt, and even bottom basalt. Here, we present a new approach to the imaging problem using the pattern recognition abilities of a back-propagation Artificial Neural Network (ANN). ANNs are computational systems that attempt to mimic natural biological neural networks. They have the ability to recognize patterns and develop their own generalizations about a given data set. Back-propagation neural networks are trained on data sets for which the solution is known and tested on the data that are not previously presented to the ANN in order to validate the network result. We show that Artificial Neural Networks, due to their pattern recognition capabilities, can invert the medium statistics based on the seismic character. We produce statistically defined models involving a basalt analogous layer, and calculate full wavefield finite difference synthetic seismograms. We vary basalt layer thickness and source frequency to generate a synthetic model that produces seismic that is similar to real sub-basalt seismic, i.e. high amplitude top basalt reflector and the absence of base basalt and sub-basalt events. Using synthetic shot gathers, generated in a synthetic representation of the sub-basalt case, we can invert the velocity medium standard deviation by using an ANN. By inverting the velocity medium standard deviation, we successfully identified the transition from basalt to sub-basalt on the synthetic shot gathers. We also show that ANNs are capable of identifying the basalt to sub-basalt transition in the presence of incoherent noise. This is important for any future applications of this technique to the real-world seismic data, as this data is never completely noise-free. There is always a certain level of residual (noise remaining after initial noise filtering) environmental/ambient noise present on the recorded seismics, hence, neural network training with noise-free synthetic seismic is less than optimal.     

Artificial Neural Network Prediction of Tropospheric Ozone Concentrations in Istanbul,Turkey

Tropospheric (ground‐level) ozone has adverse effects on human health and environment. In this study, next day's maximum 1‐h average ozone concentrations in Istanbul were predicted using multi‐layer perceptron (MLP) type artificial neural networks (ANNs). Nine meteorological parameters and nine air pollutant concentrations were utilized as inputs. The total 578 datasets were divided into three groups: training, cross‐validation, and testing. When all the 18 inputs were used, the best performance was obtained with a network containing one hidden layer with 24 neurons. The transfer function was hyperbolic tangent. The correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), and index of agreement or Willmott's Index (d₂) for the testing data were 0.90, 8.78 µg/m³, 11.15 µg/m³, and 0.95, respectively. Sensitivity analysis has indicated that the persistence information (current day's maximum and average ozone concentrations), NO concentration, average temperature, PM₁₀, maximum temperature, sunshine time, wind direction, and solar radiation were the most important input parameters. The values of R, MAE, RMSE, and d₂ did not change considerably for the MLP model using only these nine inputs. The performances of the MLP models were compared with those of regression models (i.e., multiple linear regression and multiple non‐linear regression). It has been found that there was no significant difference between the ANN and regression modeling techniques for the forecasting of ozone concentrations in Istanbul.     

FAM人工神经网络模型在震后趋势判断中的应用

介绍了FAM模糊人工神经网络的原理、学习方法与规则,提出了用于地震震后序列3种震型的判断标准及现场预报规则,建立了基于FAM人工神经网络模型的地震现场预报手段与准则.     

Estimation of the Amount of Suspended Sediment in the Tigris River using Artificial Neural Networks

The amount of sediment should be taken into consideration in the planning of water structures for efficient use of limited water resources. It is important to estimate the amount of sediment for the successful operation of these structures in their future performances. Such estimations can be achieved by Artificial Neural Network (ANNs) with low error percentages as seen in many other disciplines. These networks also enable the modeling of nonlinear relationships between the parameters affecting the event. The purpose of this research is to establish models for sediment amounts in the Tigris River at the Diyarbakir measurement station in Turkey. Rainfall, temperature and discharge are taken as independent variables in the models, whereas sediment is taken as the dependent variable. Fourteen different models are generated using ANNs and Regression Analysis (RA). The results are compared with each other and with the observed data. The relative error and determination coefficient are used as comparison criteria. It is concluded that due to their nonlinear modeling capability, ANNs give better results than RA.     

人工神经网络及其在地震研究中的应用

简述了人工神经网络的发展历史,详述了Ｂ－Ｐ神经网络的基本原理,介绍了其在地震研究中的应用,文后对神经网络研究中应注意的问题进行了讨论。．     

人工神经网络法在烃源岩测井评价中的应用

运用有机地球化学方法分析岩芯、岩屑样品的有机炭含量存在着昂贵、时且不准确等问题。利用测井方法的优点是经济、准确。在测井评价中使用人工神经网络法具有极大的优越性和适用性。本文结合Kohonen和BP网络方法，完成了塔里木台盆区19口井的寒武、奥陶系烃源岩层段的识别与评价，并通过测井资料处理成果和岩芯有机地化资料、地质录井情况的相互检验，证实，其本上能够满足评价烃源岩的需要，从而为利用测井资料进行烃源岩评价做出了新的尝试。     

Mechanism of diapirism and episodic fluid injections in the Yinggehai Basin

The diapirism in the Yinggehai Basin is a combined result of strong overpressure caused by rapid sedimentation of fine-grain sediments and the tensile stress field resulting from right-lateral slip of the boundary-fault. The diapirism showed multiple-stage, episodic nature, and caused intermittent counter-direction onlaps and changes in the thickness of strata. The shallow gas reservoirs in the diapir structural zone displayed obvious inter-reservoir compositional heterogeneities, and their filling history could be divided into 4 stages, with gases injected during different stages having different hydrocarbon gas, CO₂ and N₂ contents and different stable isotope compositions. The multiple-episode, intermittent activities of the diapirs, multiple-stage, non-continuous injections of fluids, and the transient thermal effect of fluid flow as well as the strong migration fractionation reflected episodic fluid injection and natural gas accumulation.     

人工神经网络模型预测气候变化对博斯腾湖流域径流影响

温室气体排放量增加造成气候变化,对全球资源环境产生重要影响．本文利用人工神经网络模型建立月降水、气温与径流关系,利用开都河流域降水、气温、径流资料对模型进行训练和验证,通过试算法确定网络模型结构,气温升高和降水量增加对径流影响的敏感程度分析表明,气温升高和降水增加对该区域径流影响较大,且气温升高的影响更为显著,径流增加主要集中在夏季,根据区域气候模型（RCMs）推算的CO2加倍情况下西北地区气候的可能变化,预测位于博斯腾湖流域的开都河大山口站年径流量增加38．6％,其中夏季增加71．8％,冬季增加11．4％。     

Feed forward Artificial Neural Network model to predict the average summer-monsoon rainfall in India

In the present research, possibility of predicting average summer-monsoon rainfall over India has been analyzed through Artificial Neural Network model. In formulating the ANN — based predictive model, three-layer network has been constructed with sigmoid non-linearity. The monthly summer monsoon rainfall totals, tropical rainfall indices and sea surface temperature anomalies have been considered as predictors while generating the input matrix for the ANN. The data pertaining to the years 1950–1995 have been explored to develop the predictive model. Finally, the prediction performance of neural net has been compared with persistence forecast and Multiple Linear Regression forecast and the supremacy of the ANN has been established over the other processes.     

Using environmental isotopes to investigate the groundwater recharge mechanisms and dynamics in the North-eastern Basin,Palestine

ABSTRACT

This study aims to differentiate the potential recharge areas and flow mechanisms in the North-eastern Basin, Palestine. The results differentiate the recharge into three main groups. The first is related to springs and some of the deep wells close to the Anabta Anticline, through the Upper Aquifer (Turonian) formation, with depleted δ¹⁸O and δ²H. The second is through the Upper Cenomanian formation surrounding the Rujeib Monocline in the southeast, where the lineament of the Faria Fault plays an important role, with relatively enriched δ¹³C_DIC values of about ?4‰ (VPDB). The third is the Jenin Sub-series, which shows higher δ¹³C_DIC values, with enriched δ¹⁸O and δ²H and more saline content. The deep wells from the Nablus area in the south of the basin indicate low δ¹³C_DIC values due to their proximity to freshwater infiltrating faults. The deep wells located to the northwest of the basin have δ¹³C_DIC values from ?8 to ?9‰ (VPDB), with enriched δ¹⁸O signatures, indicating slow recharge through thick soil.     

Amelioration of Carbon Removal Prediction for an Activated Sludge Process using an Artificial Neural Network (ANN)

A dynamic simulation model of the Ankara central wastewater treatment plant (ACWTP) was evaluated for the prediction of effluent COD concentrations. Firstly, a mechanistic model of the municipal wastewater treatment process was developed based on Activated Sludge Model No. 1 (ASM1) by using a GPS‐X computer program. Then, the mechanistic model was combined with a feed‐forward back‐propagation neural network in parallel configuration. The appropriate architecture of the neural network models was determined through several iterative steps of training and testing of the models. Both models were run with the data obtained from the plant operation and laboratory analysis to predict the dynamic behavior of the process. Using these two models, effluent COD concentrations were predicted and the results were compared for the purpose of evaluation of treatment performance. It was observed that the ASM1 ANN model approach gave better results and better described the operational conditions of the plant than ASM1.     

Artificial neural networks and empirical equations to estimate daily evaporation: application to Lake Vegoritis,Greece

ABSTRACT

Evaporation is one of the most important components in the energy and water budgets of lakes and is a primary process of water loss from their surfaces. An artificial neural network (ANN) technique is used in this study to estimate daily evaporation from Lake Vegoritis in northern Greece and is compared with the classical empirical methods of Penman, Priestley-Taylor and the mass transfer method. Estimation of the evaporation over the lake is based on the energy budget method in combination with a mathematical model of water temperature distribution in the lake. Daily datasets of air temperature, relative humidity, wind velocity, sunshine hours and evaporation are used for training and testing of ANN models. Several input combinations and different ANN architectures are tested to detect the most suitable model for predicting lake evaporation. The best structure obtained for the ANN evaporation model is 4-4-1, with root mean square error (RMSE) from 0.69 to 1.35 mm d^?1 and correlation coefficient from 0.79 to 0.92.

EDITOR M.C. Acreman

ASSOCIATE EDITOR not assigned     

Using in situ REE analysis to study the origin and diagenesis of dolomite of Lower Paleozoic,Tarim Basin

In situ REE concentrations of various dolomites from Tarim Basin were obtained by LA-ICP-MS analysis,and the data were normalized to standard seawater(Seawater Normalized=SWN).Most of the samples have a ΣREE range of less than 20 ppm.All samples show similar REESWN distributions with heavy REE depletion,and positive Ce anomaly,which indicates that they have the same dolomitization fluids(seawater).According to the origin and diagenetic process of dolomite,two types of dolomite are determined and described a...     

A 24‐h Forecast of Oxidant Concentration in Tokyo Using Neural Network and Fuzzy Learning Approach

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.