scholarly journals Causal Inference of Optimal Control Water Level and Inflow in Reservoir Optimal Operation Using Fuzzy Cognitive Map

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2147 ◽  
Author(s):  
Liu ◽  
Zhou ◽  
He ◽  
Lu ◽  
Jia ◽  
...  
Keyword(s):  
Optimal Control ◽  
Causal Inference ◽  
Water Level ◽  
Causal Relationship ◽  
Optimal Operation ◽  
Cognitive Map ◽  
Experimental Results ◽  
Fuzzy Cognitive Map ◽  
Model Complex ◽  
Control Water

Reservoir optimal operation (ROO) has always been a hot issue in the field of water resources management. Analysis of the relationship of optimal control water level and inflow is conducive to understanding and solving ROO under deterministic inflow conditions. The current research uses a fuzzy cognitive map (FCM) as a tool to effectively model complex systems and then extracts systematic relationship diagrams from the dataset. A new fuzzy cognitive map with offset (FCM-O) is proposed to overcome the causal inference error caused by non-linear mapping of the activation function in a traditional FCM. With the application of inferring the causal relationship between the optimal control water level and inflow of ROO for the Three Gorges Reservoir (TGR), the experimental results show that, compared with FCM in the min data error, FCM-O reduces 11.11% and 7.14% in the training and the testing, respectively. Also, the experimental results of FCM-O are more reasonable than those of FCM. Finally, the following conclusions about the causal inference of optimal control water level and inflow in ROO for TGR are drawn: (1) The optimal control water level in September, October and November needs to be raised as much as possible to raise the water head of power generation, which is mainly affected by the constraints of the maximum operating water level of the reservoir rather than inflow; (2) the optimal control water level in January, February and March is positively affected by the inflow of the adjacent months; (3) the optimal control water level in April is due to the approaching flood season. In order to prevent water discarding, the water level is low and the optimum operation space is small. All of those shows that FCM-O is more competent than FCM in the causal relationship between optimal control water level and inflow in ROO.

SIMPLIFICATION, MERGING AND DIVISION OF FUZZY COGNITIVE MAPS

2002 ◽  
Vol 02 (02) ◽  
pp. 185-208 ◽  
Author(s):  
YUAN MIAO ◽  
ZHI-QIANG LIU ◽  
XUE HON TAO ◽  
ZHI QI SHEN ◽  
CHUN WEN LI
Keyword(s):  
Knowledge Representation ◽  
Causal Inference ◽  
Structural Complexity ◽  
Cognitive Maps ◽  
Cognitive Map ◽  
Fuzzy Cognitive Maps ◽  
Fuzzy Cognitive Map ◽  
Flexible Framework ◽  
Improved Methods

Fuzzy Cognitive Map (FCM) is a powerful and flexible framework for knowledge representation and causal inference. However, in most real applications, it is difficult to design and analyze FCMs due to their structural complexity. Simplification, merging, and division are the important operations on the structure of FCMs. In this paper we present approaches to simplifying FCMs. These approaches show how to clean up a FCM, how to divide a complex FCM into basic FCMs, and how to extract the eigen structure of these basic FCMs. Two improved methods for merging FCMs from different human experts are also proposed in this paper. We discuss difficulties in merging FCMs and present possible solutions.

FCM-Based Modeling of LMS Users' Quality of Interaction

2015 ◽  
pp. 388-420
Keyword(s):  
Fuzzy Logic ◽  
Management System ◽  
Cognitive Map ◽  
Experimental Results ◽  
Time Dependent ◽  
Fuzzy Cognitive Map ◽  
Educational Context ◽  
Learning Context ◽  
Basic Fuzzy Logic

Part III is concluded with this chapter that proposes a Fuzzy Cognitive Map (FCM)-based modeling of the Quality of Interaction (QoI) of the Learning Management System (LMS) users within a blended (b)-learning context, namely FCM-QoI model. Two training/testing scenarios were conducted and explored here, i.e., time-dependent and time-independent, using as pre-validated QoI data the ones presented in chapter 13. Moreover, a FCM-Viewer application that facilitates the visualization of the FCM-QoI model structure is also presented. The experimental results show that the proposed FCM-QoI model can provide concepts interconnection and causal dependencies representation of LMS users' interaction behavior. With this chapter, the circle around the basic fuzzy logic topics discussed in Part II, i.e., FIS, ANFIS, IFIS and FCM, injected into the educational context is fulfilled. Based on the models discussed so far, prospective hybrid modeling approaches are envisioned in the final section of the book (Part IV) that follows.

CIRCULAR ECONOMY STRATEGIES FOR ELECTRIC AND ELECTRONIC EQUIPMENT: A FUZZY COGNITIVE MAP

2017 ◽  
Vol 16 (8) ◽  
pp. 1807-1817 ◽  
Author(s):  
Fabiana Tornese ◽  
Maria Grazia Gnoni ◽  
Giorgio Mossa ◽  
Giovanni Mummolo ◽  
Rossella Verriello
Keyword(s):  
Circular Economy ◽  
Cognitive Map ◽  
Electronic Equipment ◽  
Fuzzy Cognitive Map

A preliminary fuzzy cognitive map - based desicion support tool for geriatric depression assessment

2013 ◽  
Author(s):  
Elpiniki I. Papageorgiou ◽  
Antonis S. Billis ◽  
Christos Frantzidis ◽  
Evdokimos I. Konstantinidis ◽  
Panagiotis D. Bamidis
Keyword(s):  
Cognitive Map ◽  
Fuzzy Cognitive Map ◽  
Geriatric Depression ◽  
Support Tool ◽  
Depression Assessment

The prediction of online time series with concept drift based on dynamic intuitionistic fuzzy cognitive map

2021 ◽  
Vol 25 (4) ◽  
pp. 949-972
Author(s):  
Nannan Zhang ◽  
Xixi Yao ◽  
Chao Luo
Keyword(s):  
Fuzzy Set ◽  
Sequence Data ◽  
Concept Drift ◽  
Intuitionistic Fuzzy Set ◽  
Cognitive Map ◽  
Fuzzy Cognitive Maps ◽  
Fuzzy Cognitive Map ◽  
Online Data ◽  
Intuitionistic Fuzzy ◽  
Public Data

Fuzzy cognitive maps (FCMs) have widely been applied for knowledge representation and reasoning. However, in real life, reasoning is always accompanied with hesitation, which is deriving from the uncertainty and fuzziness. Especially, when processing the online data, since the internal and external interference, the distribution and characteristics of sequence data would be considerably changed along with the passage of time, which further increase the difficulty of modeling. In this article, based on intuitionistic fuzzy set theory, a new dynamic intuitionistic fuzzy cognitive map (DIFCM) scheme is proposed for online data prediction. Combined with a novel detection algorithm of concept drift, the structure of DIFCM can be adaptively updated with the online learning scheme, which can effectively improve the representation of online information by capturing the real-time changes of sequence data. Moreover, in order to tackle with the possible hesitancy in the process of modeling, intuitionistic fuzzy set is applied in the construction of dynamic FCM, where hesitation degree as a quantitative index explicitly expresses the hesitancy. Finally, a series of experiments using public data sets verify the effectiveness of the proposed method.

scholarly journals Optimal Operation of Floodwater Resources Utilization of Lakes in South-to-North Water Transfer Eastern Route Project

2021 ◽  
Vol 13 (9) ◽  
pp. 4857
Author(s):  
Zitong Yang ◽  
Xianfeng Huang ◽  
Jiao Liu ◽  
Guohua Fang
Keyword(s):  
Water Level ◽  
Resource Utilization ◽  
Flood Control ◽  
Optimal Operation ◽  
Water Transfer ◽  
Water Levels ◽  
Reservoir Storage ◽  
Flood Season ◽  
Hongze Lake ◽  
North Water

In order to meet the demand of emergency water supply in the northern region without affecting normal water transfer, considering the use of the existing South-to-North Water Transfer eastern route project to explore the potential of floodwater resource utilization in the flood season of Hongze Lake and Luoma Lake in Jiangsu Province, this paper carried out relevant optimal operating research. First, the hydraulic linkages between the lakes were generalized, then the water resources allocation mode and the scale of existing projects were clarified. After that, the actual available amount of flood resources in the lakes was evaluated. The average annual available floodwater resources in 2003–2017 was 1.49 billion m3, and the maximum available capacity was 30.84 billion m3. Then, using the floodwater resource utilization method of multi period flood limited water levels, the research period was divided into the main flood season (15 July to 15 August) and the later flood season (16 August to 10 September, 11 September to 30 September) by the Systematic Clustering Analysis method. After the flood control calculation, the limited water level of Hongze Lake in the later flood season can be raised from 12.5 m to 13.0 m, and the capacity of reservoir storage can increase to 696 million m3. The limited water level of Luoma Lake can be raised from 22.5 m to 23.0 m (16 August to 10 September), 23.5 m (11 September to 30 September), and the capacity of reservoir storage can increase from 150 to 300 million m3. Finally, establishing the floodwater resource optimization model of the lake group with the goals of maximizing the floodwater transfer amount and minimizing the flood control risk rate, the optimal water allocation scheme is obtained through the optimization algorithm.

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