scholarly journals A Bi-level Neuro-Fuzzy System Soft Computing for Reservoir Operation

2021 ◽  
Vol 13 (3) ◽  
pp. 224-247
Author(s):  
Mekonnen Redi ◽  
Mihret Dananto ◽  
Natesan Thillaigovindan
Keyword(s):  
Soft Computing ◽  
Water Conservation ◽  
Fuzzy System ◽  
Reservoir Operation ◽  
Ethiopian Rift ◽  
Fuzzy Arithmetic ◽  
Model Parameters ◽  
Main Ethiopian Rift ◽  
Term Operation ◽  
Neuro Fuzzy

Reservoir operation studies purely based on the storage level, inflow, and release decisions during dry periods only fail to serve the optimal reservoir operation policy design because of the fact that the release decision during this period is highly dependent on wet season water conservation and flood risk management operations. Imperatively, the operation logic in the two seasons are quite different. If the two operations are not sufficiently coordinated, they may produce poor responses to the system dynamics. There are high levels of uncertainties on the model parameters, values and how they are logically operated by human or automated systems. Soft computing methods represent the system as an artificial neural network (ANN) in which the input- output relations take the form of fuzzy numbers, fuzzy arithmetic and fuzzy logic (FL). Neuro-Fuzzy System (NFS) soft computing combine the approaches of FL and ANN for single purpose reservoir operation. Thus, this study proposes a Bi-Level Neuro-Fuzzy System (BL-NFS) soft computing methodology for short and long term operation policies for a newly inaugurated irrigation project in Gidabo Watershed of Main Ethiopian Rift Valley Basin. Keywords: Bankruptcy rule, BL-NFS, Reservoir operation, Sensitivity analysis, Soft computing, Water conservation.

Intelligent Biometric System Using Soft Computing Tools

2011 ◽  
pp. 259-276
Author(s):  
Anupam Shukla ◽  
Ritu Tiwari ◽  
Chandra Prakash Rathore
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Soft Computing ◽  
Fuzzy System ◽  
Person Identification ◽  
Security Applications ◽  
Neuro Fuzzy ◽  
Biometric Systems ◽  
Physical Attributes ◽  
Novel Concept

Biometric Systems verify the identity of a claimant based on the person’s physical attributes, such as voice, face or fingerprints. Its application areas include security applications, forensic work, law enforcement applications etc. This work presents a novel concept of applying Soft Computing Tools, namely Artificial Neural Networks and Neuro-Fuzzy System, for person identification using speech and facial features. The work is divided in four cases, which are Person Identification using speech biometrics, facial biometrics, fusion of speech and facial biometrics and finally fusion of optimized speech and facial biometrics.

Intelligent Biometric System Using Soft Computing Tools

2010 ◽  
pp. 191-207
Author(s):  
Anupam Shukla ◽  
Ritu Tiwari ◽  
Chandra Prakash Rathore
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Soft Computing ◽  
Fuzzy System ◽  
Person Identification ◽  
Security Applications ◽  
Neuro Fuzzy ◽  
Biometric Systems ◽  
Physical Attributes ◽  
Novel Concept

Biometric Systems verifiy the identity of a claimant based on the person’s physical attributes, such as voice, face or fingerprints. Its application areas include security applications, forensic work, law enforcement applications etc. This work presents a novel concept of applying Soft Computing Tools, namely Artificial Neural Networks and Neuro-Fuzzy System, for person identification using speech and facial features. The work is divided in four cases, which are Person Identification using speech biometrics, facial biometrics, fusion of speech and facial biometrics and finally fusion of optimized speech and facial biometrics.

scholarly journals The Challenge in Increasing Water and Soil Resources Resilience by Landscape Restoration: Examples from Southern Ethiopia and Iceland

Proceedings ◽  
2020 ◽  
Vol 30 (1) ◽  
pp. 84
Author(s):  
Michaela Hrabalikova ◽  
David Christian Finger ◽  
Dominika Kobzova ◽  
Petra Huislova ◽  
Jan Ures
Keyword(s):  
Soil Erosion ◽  
Water Conservation ◽  
Large Scale ◽  
High Rate ◽  
Soil Reclamation ◽  
Southern Ethiopia ◽  
Ethiopian Rift ◽  
Strong Wind ◽  
Main Ethiopian Rift ◽  
Special District

Soil degradation and subsequent soil erosion is a major threat to vital ecosystem services, to food production, and finally to human societies. This threat is particularly imminent in subarctic Iceland and tropical Ethiopia. Both countries underwent large-scale deforestation in the past. Especially in Ethiopia, the demand for wood for cooking, heating, and construction is still high, inducing deforestation. On the other hand, Iceland solved the need for wood for energy purposes through the utilization of geothermal energy. Deforestation, overgrazing, and specific climatic conditions resulted in a high rate of soil erosion in both countries. In this study, the effectivity of restoration efforts is mapped in selected areas in Iceland and Ethiopia. Soil-water conservation (SWC) measures mapping was conducted in the Sidama zone and Halaba special district of southern Ethiopia, as well as in Thorlákshöfn, a municipality in southern Iceland. The Ethiopian study area is located in the Main Ethiopian rift valley. The Icelandic study area is in the Mid-Atlantic Rift. Degraded areas and applied SWC were GPS mapped in the field. The erosion agents in both countries are dominated by water erosion. In addition, Iceland has a high rate of soil loss due to strong wind erosion. In order to mitigate erosion, numerous SWC actions were implemented in both countries. In Ethiopia, indigenous SWC techniques have been applied since 400 BC, while the government-driven activities started after 1970. In Iceland, governmental soil reclamation programs started in 1907 through establishment of The Soil Conservation Service of Iceland (SCSI). Usually, all the reclamation program actions involve the closing of reclaimed area for livestock and people so that natural regeneration accompanied by additional measures such as planting seedlings can take place. In Ethiopia, such an area is called an “Area Closure”. The land is owned by the community. The common problem in the restoration of Closure Areas lies in people not respecting the watershed divide. Hence, the approach to land degradation lacks a systematic approach covering the entire watershed. Another issue is the construction of the road and path network, which in many cases acts as ways of concentrate surface runoff. Degraded paths are frequently abandoned, and new paths are constructed. The main difference in Iceland from the Ethiopia case is land ownership, which is private in most cases. The land restoration began 50 years ago by sowing grass. Today the land is slowly being reforested.

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.

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