Abstract

The present paper contributes to the development and discussion on fuzzy cognitive map (FCM)-based theory for inventive problem solving (TRIZ) for sustainable sediment management in reservoirs. FCM combines aspects of fuzzy logic, neural networks, semantic networks, expert systems, and nonlinear dynamical systems. TRIZ is a constructive methodology that includes practically reproducible models and methods that allow the development of new inventions as well as the teaching of the process, the models, and the methods of creating inventions. A proposed approach in this paper is an improvement methodology that is designed to bring about rapid improvements/changes to processes by defining and implementing the changes that can be quickly identified and easily implemented, thereby reducing the cost and time to bring about improvement and change in reservoirs. Results of this study provide a road map for how to introduce FCM and TRIZ into local sustainable sediment management with consideration of technical and executive requirements, economic factors, social welfare, and environmental impacts.

INTRODUCTION

The reduced rate of dam construction throughout the world combined with storage loss due to reservoir sedimentation currently results in more storage being lost annually than is added. This problem is further exacerbated by population growth, which results in a sharply declining storage volume per capita. Climate change will further adversely impact the performance of reservoirs (Nikolaos et al. 2017).

Consideration of technical and executive requirements, economic factors, social welfare, and environmental impacts should be the used combination of alternatives to extend the useful life of reservoirs. Based on the literature and existence experiences, a list of alternatives for sediment management in dam reservoirs can be categorized as follows (Khakzad & Elfimov 2014a). (1) Reducing sediment inflows: watershed management, upstream check structures, reservoir bypass, off-channel storage. (2) Managing sediments within the reservoir: operating rules, tactical dredging. (3) Evacuation of sediments from the reservoir: flushing, sluicing, density current venting, mechanical removal. (4) Replacing lost storage: increased dam height, construction of a new dam. (5) Decommissioning. The cost and applicability of each strategy will vary from one site to another and study of sites will create appreciation of the complexity of sediment problems and the way in which they can be controlled (Khakzad & Elfimov 2014b, 2015).

In light of the potential problems described above, there is a clear need for a strategy that can combine traditional scientific knowledge with public local context, thereby reducing uncertainty and providing for a diversified and adaptable knowledge base (Gray et al. 2014). To assist in the development of a sediment management plan based on both public participation and expert knowledge, we propose the use of fuzzy cognitive maps (FCMs) as a semi-quantitative model tool that provides a structured, simple, and inexpensive way to model overall fluvial systems through a soft evaluation of the relationship between different concepts and factors interpreted by stakeholders. In environmental sciences, cognitive mapping techniques have been used mainly in environmental conflict management (Ozesmi & Ozesmi 2004; Papageorgiou & Kontogianni 2012; Gutiérrez et al. 2017; Malek 2017; Paolisso & Trombley 2017).

In addition, to help the engineer effectively solve contradiction problems occurring in the system, the theory for inventive problem solving (TRIZ) method was invented. Altshuller (1984), an inventor, introduced a contradiction table and 40 inventive principles including 88 sub-principles that have been extended by an additional 72 sub-principles extracted from TRIZ standard solutions, by analyzing over 400,000 patents. This enhanced version of 40 inventive principles with 160 sub-principles is used in the presented research. D'Anna & Cascini (2011) proposed the sustainability map, based on two key items of TRIZ: the existence of evolution trends and the system operator to identify scenarios to achieve sustainability. Practically, all these TRIZ tools can be used for solving different tasks and problems of eco-innovation. Because of the capability of solving conflict problems, implementing TRIZ for eco-innovative design tasks have been proposed in the literature, establishing a link between eco-efficiency and the inventive principles and the contradiction matrix (Chen & Liu 2003; Chang & Chen 2004; Justel et al. 2006; Vidal et al. 2015).

In the present study, the general methodology applied to the development of a major process improvement for sediment management in dam reservoir is based on FCM-based TRIZ inventive principles. Experimental designs were applied to the execution of process characterization studies evaluating the impact of operating parameters on process performance parameters. Data from process characterization experiments were used to define the proven acceptable range and classification of operating parameters.

METHOD

Fuzzy cognitive maps

An FCM has the topology of a fuzzy signed directed graph and dynamics similar to feedback nonlinear neural networks, as is illustrated in Figure 1 (Kosko 1986, 1992; Papageorgiou et al. 2010). The fuzzy causal relationship between two concepts Cj and Ci is described with the weight Rji, taking a value in the range −1 to 1. There are three possible types of causal relationships between concepts:

  • Rji>0 which indicates positive causality between concepts Cj and Ci. That is, an increase (decrease) in the value of Cj leads to an increase (decrease) in the value of Ci.

  • Rji<0 which indicates negative causality between concepts Cj and Ci. That is, an increase (decrease) in the value of Cj leads to a decrease (increase) in the value of Ci.

  • Rji= 0 which indicates no relationship between Cj and Ci.

Figure 1

Conceptual representation of fuzzy cognitive map.

Figure 1

Conceptual representation of fuzzy cognitive map.

The simulation and inference process of the FCM is based on a mathematical formulation described in Equation (1). Through the following calculation rule, the values of concepts are calculated at every simulation step k iteratively: 
formula
(1)
where Ai(k+1) is the value of concept Ci at simulation step k + 1, Aj(k) is the value of concept Cj at simulation step k, Rji is the weight of the interconnection from concept Cj to concept Ci, and f is a sigmoid threshold function: 
formula
(2)
where λ> 0 is a parameter that determines its steepness. In our approach, the value λ= 1 has been used. This function is selected since the values Ai lie within [0, 1]. The values Ai of concepts Ci are initially fuzzy and arise from the transformation of the real values of the corresponding variables to each one concept. Also, the values for the weights of the interconnections Rji among concepts are fuzzy. These fuzzy values are converted into numerical values after the defuzzification process of fuzzy logic and are used in the FCM simulation process. The value Ai of the concept Ci expresses the degree of its corresponding physical value. At each simulation step, the value Ai of a concept Ci is calculated by computing the influence of other concepts Ci’s on the specific concept Ci following the corresponding mathematical formulation.

The theory for inventive problem solving

The simplest TRIZ algorithm is shown in Figure 2. General comments regarding the scheme are as follows (Cong & Tong 2008; Collan 2018; Orloff 2018).

Figure 2

TRIZ for pass through standard contradiction.

Figure 2

TRIZ for pass through standard contradiction.

Stage TREND

Resolution of any problem starts with an analysis of the initial situation or, if you will, with diagnosing the problem. The analysis naturally involves determination of ‘what,’ ‘where,’ and ‘when’ occurs in the system so that the quality of outcomes produced by such a system and, accordingly, the efficiency of its operation and makeup, become suspect answers to these questions and must produce a sufficiently accurate and concise description of the initial problem situation.

Stage REDUCING

Be that as it may, the concentrated answer to the question ‘what for’ – as posed at the Stage Trend – is formulated in the form of the ‘Ideal Final Result’ (IFR). This IFR substantially shapes the trend, i.e., the purpose and pattern, of the future system transformation.

Stage INVENTING

At this stage, the generation of ideas is supported with transformation models borrowed from catalogs created in TRIZ. In Modern TRIZ, this stage is also called ‘transformation.’

Stage ZOOMING

The use of the metaphorical name ‘zooming’ is determined by the nature of the main verification procedures, namely, that they must verify the ideas at several levels of examination. To do this, we must ‘zoom up’ and assess the quality of the solution, say, at the level of the entire system. Then, we might want to take an even broader look at the level encompassing the interests of the user for whom the artifact (system) was created in the first place.

RESULTS AND DISCUSSION

Schematic representation of a step-by-step FCM generation process is shown in Figure 3. The context of the interview is explained to the interviewee. In the next step, the interviewee is asked to write down a list of factors that he or she thinks is important in relation to the central question. After the factor list is finished the interviewee is asked to start with the drawing of the map. After all the causal connections including their directions are stated, the strength of the connections is determined. In most cases the interviewees were asked to choose numerical values right away, e.g., 0.1 for a very weak relation, 1 for a very strong connection.

Figure 3

Schematic representation of a step-by-step FCM generation process.

Figure 3

Schematic representation of a step-by-step FCM generation process.

For sustainable sediment management, the 12 individual maps built by stakeholders and decision-makers represented the raw data to develop the FCM. After the interview sessions, all maps were coded into adjacency-matrices and then analyzed and visualized with FCMapper (Table 1 and Figure 4). When no causal relationship was given in the individual map, zero was used. The connection values between two specific variables were summed and averaged over the total number of individual maps. Figure 4 visualizes the group map, where circles symbolize concepts of the system and lines reflect causal relationships between concepts. The size reflects the centrality of the variable within the system. The most central variables were construction and operation time, economic costs, and economic benefits.

Table 1

Adjacency matrix of FCM for sustainable sediment management

Sustainable sediment management Construction and operation time Economic benefits Economic costs The complexity in the implementation process Quality of recreational use Awareness Effects on water users Attitude towards Standards Effects on water quality Resettlement Stock of valuable fish Water level 
Construction and operation time 0.00 −0.20 0.60 0.00 0.00 0.00 −0.20 0.00 −0.20 0.00 0.00 0.00 
Economic benefits 0.25 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 −0.10 0.00 0.00 
Economic costs −0.60 0.00 0.00 0.20 0.00 0.00 0.00 0.00 0.35 0.00 0.00 0.50 
The complexity in the implementation process 0.00 0.20 0.25 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.20 
Quality of recreational use 0.00 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 −0.10 0.00 0.00 
Awareness 0.25 0.00 −0.10 0.00 0.00 0.00 0.10 0.20 0.00 0.00 0.00 0.00 
Effects on water users 0.00 0.20 0.00 0.00 0.10 0.00 0.00 0.00 0.00 −0.20 0.00 0.00 
Attitude towards Standards 0.60 0.00 −0.10 0.00 0.00 0.20 0.10 0.00 0.20 0.00 0.00 0.00 
Effects on water quality 0.00 0.25 0.00 0.00 0.10 0.00 0.00 0.00 0.00 −0.20 0.20 0.00 
Resettlement 0.00 −0.25 0.30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 
Stock of valuable fish 0.00 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 −0.10 0.00 0.00 
Water level 0.00 0.50 0.25 0.00 0.00 0.00 0.10 0.00 0.10 −0.10 0.10 0.00 
Sustainable sediment management Construction and operation time Economic benefits Economic costs The complexity in the implementation process Quality of recreational use Awareness Effects on water users Attitude towards Standards Effects on water quality Resettlement Stock of valuable fish Water level 
Construction and operation time 0.00 −0.20 0.60 0.00 0.00 0.00 −0.20 0.00 −0.20 0.00 0.00 0.00 
Economic benefits 0.25 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 −0.10 0.00 0.00 
Economic costs −0.60 0.00 0.00 0.20 0.00 0.00 0.00 0.00 0.35 0.00 0.00 0.50 
The complexity in the implementation process 0.00 0.20 0.25 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.20 
Quality of recreational use 0.00 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 −0.10 0.00 0.00 
Awareness 0.25 0.00 −0.10 0.00 0.00 0.00 0.10 0.20 0.00 0.00 0.00 0.00 
Effects on water users 0.00 0.20 0.00 0.00 0.10 0.00 0.00 0.00 0.00 −0.20 0.00 0.00 
Attitude towards Standards 0.60 0.00 −0.10 0.00 0.00 0.20 0.10 0.00 0.20 0.00 0.00 0.00 
Effects on water quality 0.00 0.25 0.00 0.00 0.10 0.00 0.00 0.00 0.00 −0.20 0.20 0.00 
Resettlement 0.00 −0.25 0.30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 
Stock of valuable fish 0.00 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 −0.10 0.00 0.00 
Water level 0.00 0.50 0.25 0.00 0.00 0.00 0.10 0.00 0.10 −0.10 0.10 0.00 
Figure 4

Results of FCM model for sustainable sediment management.

Figure 4

Results of FCM model for sustainable sediment management.

After preparing the FCM and evaluating the interrelation between factors’ effects on sustainable sediment management, the definition of scenarios built on the policy objectives identified by stakeholders with the help of TRIZ which is designed to show how changes to processes can be quickly improved and change the quality of sediment management in a dam reservoir. Parameter to improve, undesired results (conflict), and principles for three scenarios with the help of stakeholders based on the TRIZ method with 40 inventive principles and 160 sub-principles are shown in Table 2. Scenarios describe events and situations that could happen in the future real-world. A scenario can be defined as a hypothetical set of plausible and logical events, built to concentrate on causal processes and decision events. Scenario-based analysis is considered as a conjectural forecasting technique usually associated with future research.

Table 2

Scenario planning based on TRIZ for sustainable sediment management

 Parameter to improve Undesired results (conflict) Principles Available management alternative 
Scenario 1 Harmful side effects Waste of substance 10 Preliminary action Dam raising 
34 Recycling 
Scenario 2 Amount of substance Harmful side effects 3 Local quality Dredging near the dam 
39 Inert environment 
Scenario 3 Harmful side effects Complexity of device 19 Periodic action Flushing 
1 Segmentation 
 Parameter to improve Undesired results (conflict) Principles Available management alternative 
Scenario 1 Harmful side effects Waste of substance 10 Preliminary action Dam raising 
34 Recycling 
Scenario 2 Amount of substance Harmful side effects 3 Local quality Dredging near the dam 
39 Inert environment 
Scenario 3 Harmful side effects Complexity of device 19 Periodic action Flushing 
1 Segmentation 

Results of changes of the interrelation between factors based on each scenario with the help of FCM for sustainable sediment management are presented in Tables 3 and 4. In general terms, an FCM is able to predict the outcome by letting the relevant issues interact with one another. These predictions can be used for finding out whether a decision made by someone is consistent with the entire collection of stated causal assertions.

Table 3

Comparison between different scenarios

Concepts No changes (Scenario 1) Scenario 1 Scenario 2 Scenario 3 Results – no changes (Scenario 1) Results – Scenario 1 Results – Scenario 2 Results – Scenario 3 
Construction and operation time   0.65 0.55350 0.55852 0.55392 0.65000 
Economic benefits    0.63507 0.66489 0.64688 0.62315 
Economic costs    0.65216 0.66510 0.65421 0.66576 
The complexity in the implementation process 0.6   0.53256 0.60000 0.53266 0.53324 
Quality of recreational use  0.6  0.52708 0.52937 0.60000 0.52373 
Awareness 0.6   0.52629 0.60000 0.52629 0.52629 
Effects on water users 0.6  0.45 0.51380 0.60000 0.51486 0.45000 
Attitude towards Standards    0.52629 0.52996 0.52629 0.52629 
Effects on water quality  0.65 0.5 0.57040 0.57617 0.65000 0.50000 
Resettlement    0.38982 0.38001 0.38284 0.39663 
Stock of valuable fish    0.54357 0.54865 0.54860 0.54012 
Water level 0.8 0.65  0.60649 0.80000 0.65000 0.60815 
Concepts No changes (Scenario 1) Scenario 1 Scenario 2 Scenario 3 Results – no changes (Scenario 1) Results – Scenario 1 Results – Scenario 2 Results – Scenario 3 
Construction and operation time   0.65 0.55350 0.55852 0.55392 0.65000 
Economic benefits    0.63507 0.66489 0.64688 0.62315 
Economic costs    0.65216 0.66510 0.65421 0.66576 
The complexity in the implementation process 0.6   0.53256 0.60000 0.53266 0.53324 
Quality of recreational use  0.6  0.52708 0.52937 0.60000 0.52373 
Awareness 0.6   0.52629 0.60000 0.52629 0.52629 
Effects on water users 0.6  0.45 0.51380 0.60000 0.51486 0.45000 
Attitude towards Standards    0.52629 0.52996 0.52629 0.52629 
Effects on water quality  0.65 0.5 0.57040 0.57617 0.65000 0.50000 
Resettlement    0.38982 0.38001 0.38284 0.39663 
Stock of valuable fish    0.54357 0.54865 0.54860 0.54012 
Water level 0.8 0.65  0.60649 0.80000 0.65000 0.60815 
Table 4

Positive and negative changes for different scenarios

Positive changes Strength (pos) Negative changes Strength (neg) 
Scenario 1 
 Construction and operation time Resettlement 
 Economic benefits   
 Economic costs   
 Quality of recreational use   
 Attitude towards Standards   
 Effects on water quality   
 Stock of valuable fish   
Scenario 2 
 Construction and operation time Resettlement 
 Economic benefits   
 Economic costs   
 The complexity in the implementation process   
 Effects on water users   
 Stock of valuable fish   
Scenario 3 
 Economic costs Economic benefits 
 The complexity in the implementation process Quality of recreational use 
 Resettlement Stock of valuable fish 
 Water level   
Positive changes Strength (pos) Negative changes Strength (neg) 
Scenario 1 
 Construction and operation time Resettlement 
 Economic benefits   
 Economic costs   
 Quality of recreational use   
 Attitude towards Standards   
 Effects on water quality   
 Stock of valuable fish   
Scenario 2 
 Construction and operation time Resettlement 
 Economic benefits   
 Economic costs   
 The complexity in the implementation process   
 Effects on water users   
 Stock of valuable fish   
Scenario 3 
 Economic costs Economic benefits 
 The complexity in the implementation process Quality of recreational use 
 Resettlement Stock of valuable fish 
 Water level   

A comparison of the interrelation between factors based on each scenario indicates that three alternatives were ranked as high. Sediment flushing near the dam can effectively deal with the deposition of the turbidity current sediment that is arriving at the dam. This would only be effective in flushing sediment from the wedge area in close proximity to the dam. An operational procedure is required for flushing that involves the passage of flows at various intervals to control downstream sediment concentrations and to allow water clearance time, both of which are required to address the present downstream fisheries operations.

Dam raising produces the highest present value of net benefits and directly addresses the loss of storage volume. Based on temperature and oxygen profiles taken in reservoirs, it appears that the reservoir does not stratify, and oxygen levels are relatively high throughout the water column. Resettlement of these communities will require that livelihoods of the residents are maintained, and undue hardship is minimized.

Dredging is very sensitive to the cost of dredging and produces present values of net benefits, which range from a small positive value to negative. Dredging operations often result in increased suspended sediment in the water column. The extent of increased suspended sediment in the water column is highly dependent on the type of dredger used with a clam-type dredge most likely to increase suspended sediment and an airlift dredger or suction dredge less likely to significantly increase suspended sediment.

SUMMARY AND CONCLUSIONS

In this study, FCM and TRIZ were used successfully for identification and prioritization of effective factors on sustainable sediment management in dam reservoirs and to evaluate the derived prediction model. Our proposal goes beyond the eco-rules definition or the prioritization of eco-friendly guidelines with traditional techniques used in technological forecasting. FCMs assess TRIZ evolution trends for eco-design innovation, allowing prioritizing and further decision-making based on scenario analysis. This study is based on technical and executive requirements, economic factors, social welfare and environmental impacts and their respective categories. The FCM algorithm was invoked for building and evaluating a cognitive map, and TRIZ algorithm was employed for scenario building. Based on results, flushing, dredging near the dam option and the dam raising alternative, were ranked at a high level for different scenarios. Results and application of the proposed framework to rank sediment management alternatives showed that this method provides a powerful tool for the selection of the optimum response scenario against the identified risks and can be an important instrument for change in the quality of sediment management.

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