Suspended load estimation using L1-fuzzy regression,L2-fuzzy regression and MARS-fuzzy regression models

ABSTRACT

The problem of estimation of suspended load carried by a river is an important topic for many water resources projects. Conventional estimation methods are based on the assumption of exact observations. In practice, however, a major source of natural uncertainty is due to imprecise measurements and/or imprecise relationships between variables. In this paper, using the Multivariate Adaptive Regression Splines (MARS) technique, a novel fuzzy regression model for imprecise response and crisp explanatory variables is presented. The investigated fuzzy regression model is applied to forecast suspended load by discharge based on two real-world datasets. The accuracy of the proposed method is compared with two well-known parametric fuzzy regression models, namely, the fuzzy least-absolutes model and the fuzzy least-squares model. The comparison results reveal that the MARS-fuzzy regression model performs better than the other models in suspended load estimation for the particular datasets. This comparison is done based on four goodness-of-fit criteria: the criterion based on similarity measure, the criterion based on absolute errors and the two objective functions of the fuzzy least-absolutes model and the fuzzy least-squares model. The proposed model is general and can be used for modelling natural phenomena whose available observations are reported as imprecise rather than crisp.

Editor D. Koutsoyiannis; Associate editor H. Aksoy     

Multi-criteria modelling and clustering of spatial information

To improve the formal integration of verbally given spatial information in a geographical information system, a methodology for converting such information to a geographical representation was developed. This was applied to examples of a spatial-related analysis of natural language text messages in the domain of disaster management. This article presents an approach for such a conversion by using a suitable knowledge representation as well as formal modelling structures. The structures are provided by an ontology-supported knowledge base. With respect to spatial uncertainty, a formal representation based on possibility theory was defined. Moreover, a cluster algorithm was developed for handling information of different sources and building a spatial context.     

Prediction of Aquifer Vulnerability to Pesticides Using Fuzzy Rule-Based Models at the Regional Scale

Contamination of ground water has been a major environmental concern in recent years. The potential for ground-water contamination by pesticides depends on porous media, solute, and hydrologic parameters. Although sophisticated deterministic computer models are available for assessing aquifer-contamination potential on a site-by-site basis, most deterministic models are too complex for vulnerability assessment on a regional scale because they require input data that are spatially and temporally variable, and which may not be available at this scale. Therefore, development of an affordable model that is robust under conditions of uncertainty at the watershed scale with minimum input of field data becomes a useful ground-water management tool. The purpose of this study was to examine the usefulness of fuzzy rule-based techniques in predicting aquifer vulnerability to pesticides at the regional scale. The objectives were to (1) develop fuzzy rule-based models using the same input parameters contained in an index-based model (i.e., the modified DRASTIC model), (2) determine the sensitivity of fuzzy rule model predictions, (3) compare the outputs of the fuzzy rule-based models with those of the modified DRASTIC model and with the results of aquifer water-quality analyses, and (4) examine the spatial variability of field parameters around contaminated wells of the Alluvial aquifer in Woodruff County Arkansas. The fuzzy rule-based model for objective (1) was developed using similar parameter weights and ratings as the modified DRASTIC model. For objective (2), fuzzy rule-based models were created using fewer parameters than the modified DRASTIC model. Sensitivity of the fuzzy rule-based models was determined using different combinations of weights of the four input parameters in DRASTIC. It was found that variations in the weights of the input parameters and number of fuzzy sets influenced the location of the aquifer-vulnerability categories as well as the area within each fuzzy category. The fuzzy rule models tended to predict somewhat higher vulnerabilities of the Alluvial aquifer than the modified DRASTIC model. The fuzzy rule base that had the soil-leaching index (S) as the highest weight was chosen as the best fuzzy rule model in predicting potential contamination by pesticides of the aquifer. In general, the fuzzy rule models tended to overestimate the vulnerability of the aquifer in the study area.     

MESURE DE LA PERMÉABILITÉ DES SOLS EN PLACE: UN NOUVEL APPAREIL POUR LA MÉTHODE MUNTZ UNE EXTENSION DE LA MÉTHODE PORCHET AUX SOLS HÉTÉROGÈNES

ABSTRACT

The authors make a general survey of the methods of measuring soil permeability in the field, looking at them from the point of view of the hydrologist.

In the first part they describe new equipment built by the ORSTOM Centre in Lome, Togo, to apply the Muntz method of vertical infiltration under constant head, equipment using a double cylinder and semi-automatic regulation of the discharge.

In the second part, the authors make a mathematical analysis of the Porchet method of horizontal infiltration in a borehole full of water: they show that results may be interpreted even with heterogeneous (multi-layered) soils and that the calculations are simplified using a computer programme.

In conclusion they deal with some results of measurements obtained by the two methods, and examine their accuracy and their correlation with the physical and hydrodynamics characteristics of the soils.     

Modelling of infiltration using artificial intelligence techniques in semi-arid Iran

ABSTRACT

Infiltration plays a fundamental role in streamflow, groundwater recharge, subsurface flow, and surface and subsurface water quality and quantity. In this study, adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and random forest (RF) models were used to determine cumulative infiltration and infiltration rate in arid areas in Iran. The input data were sand, clay, silt, density of soil and soil moisture, while the output data were cumulative infiltration and infiltration rate, the latter measured using a double-ring infiltrometer at 16 locations. The results show that SVM with radial basis kernel function better estimated cumulative infiltration (RMSE = 0.2791 cm) compared to the other models. Also, SVM with M4 radial basis kernel function better estimated the infiltration rate (RMSE = 0.0633 cm/h) than the ANFIS and RF models. Thus, SVM was found to be the most suitable model for modelling infiltration in the study area.     

QANAT SYSTEMS IN IRAN

Abstract

A fuzzy rule-based technique is used for modelling the relationship between climatic forcing and droughts in a Central/Eastern European country, Hungary. Two types of climatic forcing'called premises'are considered: atmospheric circulation patterns (CP) and El Niño Southern Oscillation (ENSO). Both the Hess-Brezowsky CP types and ENSO events influence the occurrence of droughts, but the ENSO signal is relatively weak in a statistical sense. The fuzzy rule-based approach is able to learn the high space—time variability of monthly Palmer Drought Severity Index (PDSI) and results in a proper reproduction of the empirical frequency distributions. The “engine” of the approach, the fuzzy rules, are ascertained from a subset called the learning set of the observed time series of premises (monthly CP frequencies and Southern Oscillation Index) and PDSI response. Then an independent subset, the validation set, is used to check how the application of fuzzy rules reproduces the observed PDSI.     

An isotopic investigation of a near-surface groundwater system

ABSTRACT

The distribution of environmental tritium, deuterium and oxygen-18 in the unsaturated zone and the underlying sandy phreatic aquifer was studied throughout 1981 in an area of high pine forests in the Rhine valley near Heidelberg. The observed vertical distribution of isotopes in the unsaturated zone can be satisfactorily explained by the combined use of a multi-cell model for moisture transport and an evapotranspiration model. The distribution in the underlying aquifer of the tracer input at the water table obtained using this method is found by replacing the total vertical diffusion coefficient in the diffusion equation with the dispersion coefficient. In this way observed tritium profiles are satisfactorily simulated for the period 1966–1981. The stable isotope profile in the unsaturated zone however remains largely unexplained due to inadequate data on the stable isotope content of precipitation over the investigated area.     

基于模糊故障树法的深基坑支护工程项目风险评估

深基坑工程施工中安全事故的发生给社会及施工企业带来了不良影响和巨大的经济损失,如何减少深基坑工程安全事故发生率,降低工程风险是一个值得研究的问题。本文通过将模糊理论与故障树分析方法相结合,建立了基于模糊故障树法的深基坑支护工程项目风险评估模型,克服了传统故障树方法忽略底事件发生概率的不确定性这一缺陷。通过对某深基坑工程实例的计算分析,说明了本文所建立的模型在深基坑工程风险评估中具有较好的理论价值与实际应用价值。     

基于模糊综合评判的大石围天坑群生态旅游形象定位

现代旅游给游客诸多形象感知,影响其对旅游地选择的决策。乐业大石围天坑群具有世界级的旅游资源品位,由于交通的不便、天坑本身陡峭险峻等因素,使旅游者对景区产生的形象感知偏差。本文简述了大石围天坑群景区的本底形象,通过专家问卷调查,运用层次分析法评价并分析大石围天坑群资源的吸引力;在层次分析法的基础上,运用多层次模糊综合评判计算各层因子的吸引力得分。结果表明,大石围天坑群景区的资源非常具有吸引力,旅游地的地质地貌及动植物组成、旅游资源的科研与观赏价值、资源的典型奇特性的分值分别为89.71、87.08、87.57分,因此在景区的宣传中应着重放在地质、地貌、动物、植物的稀有性、完整性、典型性、奇特性上。