Long-range forecasts of River Po discharges based on predictable solar activity and a fuzzy neural network model / Prévisions à long terme des débits du Fleuve Pô basées sur l’activité solaire prévisible et sur un modèle de réseau de neurones flou

Abstract

Abstract Is it possible to make seasonal and interannual forecasts of hydrological variables if one cannot predict next week’s rainfall? Contrary to common view, some scientists support the hypothesis that variations in mean global temperature and precipitation are controlled more by external forcing (solar variability and volcanic eruptions) than by increasing atmospheric concentration of greenhouse gases. Temperature and precipitation are connected with special phases of the 11-year sunspot cycle, which coincide with significant accumulation of energetic solar eruptions. Because of the possibility of identifying years with many solar eruptions, the attractive prospect emerges of the long-term hydrological forecasting based on cycles of solar activity. Starting from this assumption, an expert system was built based on a fuzzy neural network model for seasonal and interannual forecasting of the Po River discharge. It was found that indices of solar activity and of global circulation are sufficient to yield useful forecasts of hydrological variables.     

Intelligent control for modeling of real‐time reservoir operation,part II: artificial neural network with operating rule curves

To bridge the gap between academic research and actual operation, we propose an intelligent control system for reservoir operation. The methodology includes two major processes, the knowledge acquired and implemented, and the inference system. In this study, a genetic algorithm (GA) and a fuzzy rule base (FRB) are used to extract knowledge based on the historical inflow data with a design objective function and on the operating rule curves respectively. The adaptive network‐based fuzzy inference system (ANFIS) is then used to implement the knowledge, to create the fuzzy inference system, and then to estimate the optimal reservoir operation. To investigate its applicability and practicability, the Shihmen reservoir, Taiwan, is used as a case study. For the purpose of comparison, a simulation of the currently used M‐5 operating rule curve is also performed. The results demonstrate that (1) the GA is an efficient way to search the optimal input–output patterns, (2) the FRB can extract the knowledge from the operating rule curves, and (3) the ANFIS models built on different types of knowledge can produce much better performance than the traditional M‐5 curves in real‐time reservoir operation. Moreover, we show that the model can be more intelligent for reservoir operation if more information (or knowledge) is involved. Copyright © 2004 John Wiley & Sons, Ltd.     

Conceptual fuzzy neural network model for water quality simulation

Artificial neural networks (ANNs) have been applied successfully in various fields. However, ANN models depend on large sets of historical data, and are of limited use when only vague and uncertain information is available, which leads to difficulties in defining the model architecture and a low reliability of results. A conceptual fuzzy neural network (CFNN) is proposed and applied in a water quality model to simulate the Barra Bonita reservoir system, located in the southeast region of Brazil. The CFNN model consists of a rationally‐defined architecture based on accumulated expert knowledge about variables and processes included in the model. A genetic algorithm is used as the training method for finding the parameters of fuzzy inference and the connection weights. The proposed model may handle the uncertainties related to the system itself, model parameterization, complexity of concepts involved and scarcity and inaccuracy of data. The CFNN showed greater robustness and reliability when dealing with systems for which data are considered to be vague, uncertain or incomplete. The CFNN model structure is easier to understand and to define than other ANN‐based models. Moreover, it can help to understand the basic behaviour of the system as a whole, being a successful example of cooperation between human and machine. Copyright © 2006 John Wiley & Sons, Ltd.     

Vectorial fuzzy logic: A novel technique for enhanced mineral prospectivity mapping,with reference to the orogenic gold mineralisation potential of the Kalgoorlie Terrane,Western Australia

Modern exploration is a multidisciplinary task requiring the simultaneous consideration of multiple disparate geological, geochemical and geophysical datasets. Over the past decade, several research groups have investigated the role of Geographic Information Systems as a tool to analyse these data. From this research, a number of techniques has been developed that allow the extraction of exploration‐relevant spatial factors from the datasets. The spatial factors are ultimately condensed into a single prospectivity map. Most techniques used to construct prospectivity maps tend to agree, in general, as to which areas have the lowest and highest prospectivities, but disagree for regions of intermediate prospectivity. In such areas, the prospectivity map requires detailed interpretation, and the end‐user must normally resort to analysis of the original datasets to determine which conjunction of factors results in each intermediate prospectivity value. To reduce this burden, a new technique, based on fuzzy logic principles, has been developed for the integration of spatial data. Called vectorial fuzzy logic, it differs from existing methods in that it displays prospectivity as a continuous surface and allows a measure of confidence to be incorporated. With this technique, two maps are produced: one displays the calculated prospectivity and the other shows the similarity of input values (or confidence). The two datasets can be viewed simultaneously as a three‐dimensional perspective image in which colour represents prospectivity and topography represents confidence. With the vectorial fuzzy logic method, factors such as null data and incomplete knowledge can also be incorporated into the prospectivity analysis.     

Traffic safety prediction model for identifying spatial degrees of exposure to the risk of road accidents based on fuzzy logic approach

This article, we propose a traffic accident prediction system based on fuzzy logic which allows to identify ‘the degree of exposure to road accidents’ risk’ and to analyse the level of complexity of the factors involved. We focus our study on the possible influence of a series of local criteria observed and selected for each kilometre per segment of the road network studied. The study was conducted on a road network within the rural area of the Wilaya of Mascara in the north-western region of Algeria. A Geographic Information System was integrated into the analysis process to enable a spatial visualization of the degrees of exposure to road accidents’ risk, providing a cartographically measurable solution to establish and attenuate accident risk. Results show that the developed system can be effectively applied as an useful Road Safety tool capable of identifying risk factors related to the characteristics of the road.     

基于模糊加权线性回归法的重力仪漂移系数计算

重力仪漂移系数对于观测数据的有效零漂改正,进而获得可靠重力观测数据具有重要意义。笔者提出利用模糊加权线性回归法进行重力仪漂移系数计算,通过对理论与实际的重力仪静态数据的试验、比较与分析,认为该方法具有计算自动化、有效压制干扰、计算结果稳定等特点,能够实现对重力仪漂移系数的有效计算,具有应用前景。     

基于马尔科夫随机场与模糊C均值的遥感影像分类

模糊C均值聚类是一种经典的非监督聚类模型,成功地应用于遥感影像分类。但是该方法对初始值敏感,容易陷入局部最优解;同时聚类时仅考虑光谱信息,忽略了空间信息。本文提出了一种新的基于马尔科夫随机场的模糊C均值聚类方法,该方法把马尔科夫随机场和模糊C均值结合在一起。初始值依据第一主成分的密度函数确定,这样克服了对初始值的依赖性,又在聚类的时候考虑了空间信息。通过实例数据验证,所提出的方法分类精度优于传统的模糊C均值模型。     

融合多特征的遥感影像变化检测

常规多时相遥感影像变化检测主要基于光谱信息,没有充分利用纹理、几何、形状等多种特征信息,不足以体现检测目标的完整性和准确性。本文针对不同特征在变化检测中应用的优势,在提取影像多种特征的基础上,构建了1维和多维两种基于信息融合策略的变化检测方法,即利用1维特征空间加权距离相似度运算、多维特征空间的模糊集融合和支持向量机融合策略进行变化检测。利用多时相QuickBird高分辨率遥感影像进行城市土地覆盖变化检测试验,结果表明,本文方法可以有效集成不同特征的优势与表征变化信息的能力,提高变化检测过程的稳定性和适用性,同时能够更好地保持变化地物的结构和形状,突出主要变化目标。     

模糊综合评价法在新疆农业主导功能定位中的应用

 新疆区域农业主导功能具有空间和组合上的复合性特征,探讨建立一种适用于区域农业主导功能为复合功能,并对复合型农业主导功能的主要功能和辅助功能进行明确定位的综合评价方法,对保证新疆农业功能区划的科学性、合理性和可操作性具有十分重要的意义。在研究构建新疆农业功能综合评价指标体系的基础上,应用模糊综合评价方法,构建综合评价模型,对全疆88 个涉农县（市、区）的农业功能进行综合评价。在综合测评结果基础上,对各县（市、区）的农业功能重要性进行纵向比较和横向比较,以确定各区划单元四大农业功能的主辅地位以及优势主导功能。根据模糊综合评价的测评排序结果,结合问卷调查和专家咨询结果,提出了新疆区域农业主导功能定位及布局方案。应用模糊综合评价方法对新疆农业功能进行综合测评和主导功能定位,同时结合定性方法对评价结果进行比对和修正,得出的研究方案符合新疆实际,表明该方法是对复合型农业主导功能区进行农业功能定位及区划的有效方法之一,同时为新疆农业主导功能区制定不同的发展对策和战略措施,优化新疆农业产业结构和布局提供了科学依据。     

水库长期优化调度的可持续性模型初探

在水资源可持续利用思想指导下,建立了水库可持续长期优化调度模型.该模型以包含经济、社会、环境效益的综合效益最大化作为目标函数,在满足各种约束条件下协调当代与后代的用水需求.文中同时提出了分层模糊多目标动态规划法作为模型求解的一条途径.实例计算表明,该模型在一定程度上能够为水库可持续利用提供令人满意的决策方案.