Evolving Connectionist Systems Versus Neuro-Fuzzy System for Estimating Total Dissolved Gas at Forebay and Tailwater of Dams Reservoirs

2020 ◽  
pp. 109-126
Author(s):  
Salim Heddam ◽  
Ozgur Kisi
Keyword(s):  
Fuzzy System ◽  
Dissolved Gas ◽  
Total Dissolved Gas ◽  
Neuro Fuzzy

Understanding gas bubble trauma in an era of hydropower expansion: how do fish compensate at depth?

2020 ◽  
Vol 77 (3) ◽  
pp. 556-563 ◽  
Author(s):  
Naomi K. Pleizier ◽  
Charlotte Nelson ◽  
Steven J. Cooke ◽  
Colin J. Brauner
Keyword(s):  
Bubble Growth ◽  
Gas Bubble ◽  
Hydroelectric Dams ◽  
Dissolved Gas ◽  
Empirical Relationships ◽  
Lethal Effects ◽  
Exposed Fish ◽  
Total Dissolved Gas ◽  
Rainbow Trout Oncorhynchus Mykiss ◽  
The Relationship

Hydrostatic pressure is known to protect fish from damage by total dissolved gas (TDG) supersaturation, but empirical relationships are lacking. In this study we demonstrate the relationship between depth, TDG, and gas bubble trauma (GBT). Hydroelectric dams generate TDG supersaturation that causes bubble growth in the tissues of aquatic animals, resulting in sublethal and lethal effects. We exposed fish to 100%, 115%, 120%, and 130% TDG at 16 and 63 cm of depth and recorded time to 50% loss of equilibrium and sublethal symptoms. Our linear model of the log-transformed time to 50% LOE (R2 = 0.94) was improved by including depth. Based on our model, a depth of 47 cm compensated for the effects of 4.1% (±1.3% SE) TDG supersaturation. Our experiment reveals that once the surface threshold for GBT from TDG supersaturation is known, depth protects rainbow trout (Oncorhynchus mykiss) from GBT by 9.7% TDG supersaturation per metre depth. Our results can be used to estimate the impacts of TDG on fish downstream of dams and to develop improved guidelines for TDG.

scholarly journals Energy dissipation efficiency as a new variable in the empirical correlation of total dissolved gas

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jingying Lu ◽  
Xiaolong Cheng ◽  
Zhenhua Wang ◽  
Ran Li ◽  
Jingjie Feng ◽  
...  
Keyword(s):  
Energy Dissipation ◽  
Standard Error ◽  
Air Entrainment ◽  
Empirical Equations ◽  
Generation Process ◽  
Field Observations ◽  
Dissolved Gas ◽  
Total Dissolved Gas ◽  
Future Work ◽  
Hydropower Stations

AbstractTotal dissolved gas (TDG) supersaturation, which occurs during dam spilling, may result in fish bubble disease and mortality. Many studies have been conducted to identify the factors pertaining to TDG generation, such as the spilling discharge and tailwater depth. Additionally, the energy dissipation efficiency should be considered due to its effect on the air entrainment, which influences the TDG generation process. According to the TDG field observations of 49 cases at Dagangshan and Xiluodu hydropower stations, the TDG was positively related to the energy dissipation efficiency, tailwater depth and discharge per unit width. A correlation between the generated TDG level and these factors was established. The empirical equations proposed by the USACE were calibrated, and the TDG level estimation performance was compared with the established correlation for 25 spillage cases at seven other dams. Among the considered cases, the standard error of the TDG estimation considering the energy dissipation efficiency was 5.7%, and those for the correlations obtained using the USACE equations were 13.0% and 10.0%. The findings indicated that the energy dissipation efficiency considerably influenced the TDG level, and its consideration helped enhance the precision of the TDG estimation. Finally, the generality of this approach and future work were discussed.

Structure identification and IO space partitioning in a nonlinear fuzzy system for prediction of patient survival after surgery

2017 ◽  
Vol 10 (2) ◽  
pp. 166-182 ◽  
Author(s):  
Shabia Shabir Khan ◽  
S.M.K. Quadri
Keyword(s):  
Pancreatic Cancer ◽  
Fuzzy Inference System ◽  
Fuzzy System ◽  
Fuzzy Inference ◽  
Patient Survival ◽  
Data Partitioning ◽  
Structure Identification ◽  
Inference System ◽  
Content Type ◽  
Neuro Fuzzy

Purpose As far as the treatment of most complex issues in the design is concerned, approaches based on classical artificial intelligence are inferior compared to the ones based on computational intelligence, particularly this involves dealing with vagueness, multi-objectivity and good amount of possible solutions. In practical applications, computational techniques have given best results and the research in this field is continuously growing. The purpose of this paper is to search for a general and effective intelligent tool for prediction of patient survival after surgery. The present study involves the construction of such intelligent computational models using different configurations, including data partitioning techniques that have been experimentally evaluated by applying them over realistic medical data set for the prediction of survival in pancreatic cancer patients. Design/methodology/approach On the basis of the experiments and research performed over the data belonging to various fields using different intelligent tools, the authors infer that combining or integrating the qualification aspects of fuzzy inference system and quantification aspects of artificial neural network can prove an efficient and better model for prediction. The authors have constructed three soft computing-based adaptive neuro-fuzzy inference system (ANFIS) models with different configurations and data partitioning techniques with an aim to search capable predictive tools that could deal with nonlinear and complex data. After evaluating the models over three shuffles of data (training set, test set and full set), the performances were compared in order to find the best design for prediction of patient survival after surgery. The construction and implementation of models have been performed using MATLAB simulator. Findings On applying the hybrid intelligent neuro-fuzzy models with different configurations, the authors were able to find its advantage in predicting the survival of patients with pancreatic cancer. Experimental results and comparison between the constructed models conclude that ANFIS with Fuzzy C-means (FCM) partitioning model provides better accuracy in predicting the class with lowest mean square error (MSE) value. Apart from MSE value, other evaluation measure values for FCM partitioning prove to be better than the rest of the models. Therefore, the results demonstrate that the model can be applied to other biomedicine and engineering fields dealing with different complex issues related to imprecision and uncertainty. Originality/value The originality of paper includes framework showing two-way flow for fuzzy system construction which is further used by the authors in designing the three simulation models with different configurations, including the partitioning methods for prediction of patient survival after surgery. Several experiments were carried out using different shuffles of data to validate the parameters of the model. The performances of the models were compared using various evaluation measures such as MSE.

scholarly journals A Neuro-Fuzzy System for Extracting Environment Features Based on Ultrasonic Sensors

Sensors ◽  
2009 ◽  
Vol 9 (12) ◽  
pp. 10023-10043 ◽  
Author(s):  
Graciliano Nicolás Marichal ◽  
Angela Hernández ◽  
Leopoldo Acosta ◽  
Evelio José González
Keyword(s):  
Fuzzy System ◽  
Ultrasonic Sensors ◽  
Neuro Fuzzy
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