Heavy seasonal rain makes waterway flood and is one of the preeminent reason behind flooding.Flooding causes various perils with outcomes including danger to human life,harm to building,streets,misfortune to horticult...Heavy seasonal rain makes waterway flood and is one of the preeminent reason behind flooding.Flooding causes various perils with outcomes including danger to human life,harm to building,streets,misfortune to horticultural fields and bringing about human uprooting.Thus,prediction of flood is of prime importance so as to reduce exposure of people and destruction of property.This paper focuses on applying different neural networks approach,i.e.Multilayer Perceptron,Radial Basis functional neural network,Local Linear Radial Basis Functional Neural Network and Artificial Neural Network with Whale Optimization to predict flood in terms of rainfall,gauge,area,velocity,pressure,average temperature,average wind speed that are setup through field and lab investigation from the contextual analysis of river“Daya”and“Bhargavi”.It has always been a troublesome undertaking to predict flood as many factors have influence on it although with this neural network models the prediction accuracy can be optimized using back propagation method which is a widely applied over traditional learning method for neural system because of its preeminent learning ability.The flood prediction system is built with the four models and a comparison is made which provides us the answer to which model is effective for the prediction.展开更多
The time series data is chaotic,non seasonal,non stationary and random in nature.It becomes quite challenging to discover the hidden patterns of time series data.In this paper the time series data is predicted with th...The time series data is chaotic,non seasonal,non stationary and random in nature.It becomes quite challenging to discover the hidden patterns of time series data.In this paper the time series data is predicted with the help of a machine learning algorithm i.e.Elephant Herd Optimization(EHO).Three different types of time series data are used to testify the superiority of the proposed method namely stock market data,currency exchange data and absenteeism at work.The data are first subjected to feature selection methods namely ANOVA and Friedman test.The feature selection methods provide relevant set of features which is fed to the neural network trained with the method.The proposed method is also compared with other methods such as local linear radial basis functional neural network and particle swarm optimization.The results prove supremacy of EHO over other methods.展开更多
文摘Heavy seasonal rain makes waterway flood and is one of the preeminent reason behind flooding.Flooding causes various perils with outcomes including danger to human life,harm to building,streets,misfortune to horticultural fields and bringing about human uprooting.Thus,prediction of flood is of prime importance so as to reduce exposure of people and destruction of property.This paper focuses on applying different neural networks approach,i.e.Multilayer Perceptron,Radial Basis functional neural network,Local Linear Radial Basis Functional Neural Network and Artificial Neural Network with Whale Optimization to predict flood in terms of rainfall,gauge,area,velocity,pressure,average temperature,average wind speed that are setup through field and lab investigation from the contextual analysis of river“Daya”and“Bhargavi”.It has always been a troublesome undertaking to predict flood as many factors have influence on it although with this neural network models the prediction accuracy can be optimized using back propagation method which is a widely applied over traditional learning method for neural system because of its preeminent learning ability.The flood prediction system is built with the four models and a comparison is made which provides us the answer to which model is effective for the prediction.
文摘The time series data is chaotic,non seasonal,non stationary and random in nature.It becomes quite challenging to discover the hidden patterns of time series data.In this paper the time series data is predicted with the help of a machine learning algorithm i.e.Elephant Herd Optimization(EHO).Three different types of time series data are used to testify the superiority of the proposed method namely stock market data,currency exchange data and absenteeism at work.The data are first subjected to feature selection methods namely ANOVA and Friedman test.The feature selection methods provide relevant set of features which is fed to the neural network trained with the method.The proposed method is also compared with other methods such as local linear radial basis functional neural network and particle swarm optimization.The results prove supremacy of EHO over other methods.
