Adaptive neural-based fuzzy inference system (ANFIS) approach for modelling hydrological time series

Abstract

The main aim of this study is to develop a flow prediction method, based on the adaptive neural-based fuzzy inference system (ANFIS) coupled with stochastic hydrological models. An ANFIS methodology is applied to river flow prediction in Dim Stream in the southern part of Turkey. Application is given for hydrological time series modelling. Synthetic series, generated through autoregressinve moving-average (ARMA) models, are then used for training data sets of the ANFIS. It is seen that the extension of input and output data sets in the training stage improves the accuracy of forecasting by using ANFIS.     

Optimizing artificial neural network-based indoor positioning system using genetic algorithm

Abstract

The Global Positioning System (GPS) is expected to play an integral role in the development of digital earth; however, the GPS cannot provide positioning information in regions where a majority of the population spends their time, that is, in urban and indoor environments. Hence, alternate positioning systems that work in indoor and urban environments should be developed to achieve the vision of digital earth. Wi-Fi-based positioning systems (WPS) stand out because of the near-ubiquitous presence of the associated infrastructure and signals in indoor environments. The WPS-based fingerprinting is the most widely adopted technique for position determination, but its accuracy is lower than that of techniques such as time of arrival and angle of arrival. Improving the accuracy is still a challenging task because of the complex nature of the propagation of Wi-Fi signals. Here, a novel server-based, genetic-algorithm-optimized, cascading artificial neural network-based positioning model is presented. The model is tested in 2D and 3D indoor environments under varying conditions. The model is thoroughly investigated on a real Wi-Fi network, and its accuracy is found to be better than that of other well-known techniques. A mean accuracy of 1.9 m is achieved with 87% of the distance error within the range of 0–3 m.     

Potentiality of feed-forward neural networks for classifying dark formations to oil spills and look-alikes

Radar backscatter values from oil spills are very similar to backscatter values from very calm sea areas and other ocean phenomena. Several studies aiming at oil spill detection have been conducted. Most of these studies rely on the detection of dark areas, which have high Bayesian probability of being oil spills. The drawback of these methods is a complex process, mainly because non-linearly separable datasets are introduced in statistically based decisions. The use of neural networks (NNs) in remote sensing has increased significantly, as NNs can simultaneously handle non-linear data of a multidimensional input space. In this article, we investigate the ability of two commonly used feed-forward NN models: multilayer perceptron (MLP) and radial basis function (RBF) networks, to classify dark formations in oil spills and look-alike phenomena. The appropriate training algorithm, type and architecture of the optimum network are subjects of research. Inputs to the networks are the original synthetic aperture radar image and other images derived from it. MLP networks are recognized as more suitable for oil spill detection.     

Model generalization of two different drainage patterns by self-organizing maps

In this study, we develop a new method using self-organizing maps (SOMs) for the selection of hydrographic model generalization. The most suitable attributes of the stream objects are used as input variables to the SOM. The attributes were weighted using Pearson’s chi-square independence test. We used the Radical Law to determine how many features should be selected, and an incremental approach was developed to determine which clusters should be selected from the SOM. Two drainage patterns (dendritic and modified basic) were obtained from the National Hydrography Datasets of United States Geological Survey at 1:24,000-scale (high resolution) and used in order to derive stream networks at 1:100,000-scale (medium resolution). The 1:100,000-scale stream networks, derived in accordance with the proposed approach, are similar to those in the original maps in both quantity and visual aspects. Stream density and pattern were maintained in each subunit, and continuous and semantically correct networks were obtained.     

Artificial neural networks and nonlinear regression techniques to assess the influence of slake durability cycles on the prediction of uniaxial compressive strength and modulus of elasticity for carbonate rocks

Understanding rock material characterizations and solving relevant problems are quite difficult tasks because of their complex behavior, which sometimes cannot be identified without intelligent, numerical, and analytical approaches. Because of that, some prediction techniques, like artificial neural networks (ANN) and nonlinear regression techniques, can be utilized to solve those problems. The purpose of this study is to examine the effects of the cycling integer of slake durability index test on intact rock behavior and estimate some rock properties, such as uniaxial compressive strength (UCS) and modulus of elasticity (E) from known rock index parameters using ANN and various regression techniques. Further, new performance index (PI) and degree of consistency (Cd) are introduced to examine the accuracy of generated models. For these purposes, intact rock dataset is established by performing rock tests including uniaxial compressive strength, modulus of elasticity, Schmidt hammer, effective porosity, dry unit weight, p‐wave velocity, and slake durability index tests on selected carbonate rocks. Afterward, the models are developed using ANN and nonlinear regression techniques. The concluding remark given is that four‐cycle slake durability index (I_d4) provides more accurate results to evaluate material characterization of carbonate rocks, and it is one of the reliable input variables to estimate UCS and E of carbonate rocks; introduced performance indices, both PI and Cd, may be accepted as good indicators to assess the accuracy of the complex models, and further, the ANN models have more prediction capability than the regression techniques to estimate relevant rock properties. Copyright © 2011 John Wiley & Sons, Ltd.     

Efficient collection of training data for sub-pixel land cover classification using neural networks

Artificial neural networks (ANNs) are a popular class of techniques for performing soft classifications of satellite images. They have successfully been applied for estimating crop areas through sub-pixel classification of medium to low resolution images. Before a network can be used for classification and estimation, however, it has to be trained. The collection of the reference area fractions needed to train an ANN is often both time-consuming and expensive. This study focuses on strategies for decreasing the efforts needed to collect the necessary reference data, without compromising the accuracy of the resulting area estimates. Two aspects were studied: the spatial sampling scheme (i) and the possibility for reusing trained networks in multiple consecutive seasons (ii). Belgium was chosen as the study area because of the vast amount of reference data available. Time series of monthly NDVI composites for both SPOT-VGT and MODIS were used as the network inputs. The results showed that accurate regional crop area estimation (R² > 80%) is possible using only 1% of the entire area for network training, provided that the training samples used are representative for the land use variability present in the study area. Limiting the training samples to a specific subset of the population, either geographically or thematically, significantly decreased the accuracy of the estimates. The results also indicate that the use of ANNs trained with data from one season to estimate area fractions in another season is not to be recommended. The interannual variability observed in the endmembers’ spectral signatures underlines the importance of using up-to-date training samples. It can thus be concluded that the representativeness of the training samples, both regarding the spatial and the temporal aspects, is an important issue in crop area estimation using ANNs that should not easily be ignored.     

区域似大地水准面精化模型算法的优选

建立高精度、高分辨率的区域似大地水准面模型是GPS高程测量在实际应用中必须解决的首要问题,分别介绍BP神经网络、曲面拟合的模型及算法,并基于焦作市GPS测量数据,通过MATLAB编程计算和分类实验,验证几种算法的有效性与可靠性,结果证明神经网络算法要优于其它算法,最后给出有益的结论.     

New friends in new places: Network formation during the migration process among Poles in the UK

This paper contributes to on-going work that seeks to understand the dynamic nature of immigrant social network formation. We explore three propositions, derived from the literature, that might be expected to characterise the ways in which migrant associational ties evolve during and immediately after arrival in their destination country. Evidence is drawn from 42 interviews conducted between January and December 2008 with predominantly Polish migrants to the UK (28) as well as domestic service providers (14). In agreement with the existing literature on immigrant social network formation we find that weak associational ties between migrants are locally dense and rapidly formed. More surprisingly, we also find that the Poles in our sample from lower socio-economic groups tended to rely heavily upon weak associational ties while higher socio-economic group Poles tended to rely on associations made through their employing institutions. This illustrates the importance of socio-economic status in framing co-ethnic migrant network formation. This is significant because we also find that weak associational ties are not unambiguously beneficial to lower socio-economic group migrants who tend to (have to be) more compromising about, and therefore more compromised by, the social ‘friendships’ that result.     

“This is a Montreal Issue”: Negotiating responsibility in global production and investment networks

While geographers have increasingly focused on how global commodity and production networks create new ‘geographies of responsibility’ there has been little empirical work considering how responsibility is worked into management systems and social activism in such networks. Drawing on literature from global production networks, geographies of responsibility and other literatures, this paper explores the dynamic and contested ways in which concepts of responsibility can play a role in network regulation. Both foreign direct investment and commodity networks (here referred to as ‘global production and investment networks’) are subject to complex negotiations and compromises involving corporate social responsibility and sustainability initiatives as well as shareholder activist, human rights, labor, and environmental activism. This is illustrated by reference to conflicts in Canada over Alcan, Inc.’s investments from 1993 to 2007 in the Utkal Alumina Project in Orissa, India. The project involved significant socio-environmental conflict. In Canada, Alcan’s investment was met by civil society campaigns that tested the company’s commitments to sustainability and corporate social responsibility. The case study suggests revising theories of geographies of responsibility. While foreign direct investment can create new relationships between distant others, these are fluid and contingent and not necessarily desirable. Rather than see networks as a source of responsibility we should work to ensure that the relationships that networks foster be structured to ensure our deeper values are respected.     

全球原油贸易网络视角下的亚洲溢价与中日印韩原油潜在竞争关系

随着全球原油贸易关系的复杂化和亚洲原油需求的上升,全球能源市场的“亚洲溢价”现象成为需要关注的议题。本文基于全球石油贸易网络复杂性视角,选取1993—2018年国际原油贸易数据进行样本数据考察,通过构建复杂网络模型和潜在竞争指数,对全球原油贸易网络中的亚洲溢价现象和中日印韩四国潜在竞争力进行分析。结果表明：全球原油贸易集团分化交融,中日印韩呈现出贸易集团化的趋势。从全球原油贸易网络指标来看,中日印韩四国在全球原油贸易网络地位和影响力不断提升,有力推动了全球原油消费中心向亚太地区转移。潜在竞争指数表明,中国与日本、印度、韩国的潜在原油竞争关系呈现出不同特征,中日表现出“先强后弱”的特点,而中印、中韩表现为整体持续增强的特征。