Multipurpose Single Reservoir Operation under Fuzzy Environment with Fuzzy Resources And Fuzzy Technological Coefficients

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

ARTIFICIAL NEURAL NETWORK METHOD FOR ESTIMATION OF MISSING DATA

The availability of precipitation data plays important role for analysis of various systems required for design of water resources systems. The perfect measurements are not available always. The scientist/hydrologists come across the problem of missing data due to a variety of reasons. There may be various reasons of unavailability of data. Measurement of hydrologic variables (e.g. rainfall, stream flows, etc.) is prone to various instrumental/systematic, manual and random errors. In the current study, missing rainfall data is evaluated by using Artificial Neural Network Method. Historical precipitation data from 6 rain-gauge stations in the Maharashtra State, India, are used to train and test the ANN method and derive conclusions from the improvements in result given by ANN. Results suggest that ANN model can be work for estimation of missing data

A SWARM INTELLIGENCE OPTIMIZATION APPROACH FOR SMART HIVE FINDING IN HONEY BEES

Swarm Intelligence is a problem-solving behavior that occurs as a result of a multiplicity of interactions between independent components that make up the entire system Swarm intelligence comes from the biological study of social insect and insight about how they manage to solve complex problems in their daily lives. Research field as swarm systems are examples of behavior based systems. The proposed approach can be useful in traveling salesman problem also in optimized way of path finding. The Smart approach of swarm honey bees for the Hive finding is the novel approach. Our intention is to use this kind of methodology in our conventional problems to solve efficiently. BCO model has adopted mainly two natural behaviors from the social bees’ life: The mating process behavior and the foraging process behavior