Feed forward Artificial Neural Network model to predict the average summer-monsoon rainfall in India

Chattopadhyay, Surajit

doi:10.2478/s11600-007-0020-8

Cited by 94 publications

(54 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…inclusion into the ANN model were scaled appropriately. In the present investigation, the input neurons were scaled in the range of [−1, 1] and a transfer function was implemented to explain the nonlinear relationship between input and output neurons (Chattopadhyay, 2007). For determining the optimum ANN model to be used in this work, the set of five back propagation training algorithms used were as follows: scaled conjugate gradient (trainscg), one-step secant (trainoss), BFGS quasi-Newton (trainbfg), Bayesian regulation (trainbr) and Levenberg-Marquardt (trainlm).…”

Section: Network Architecture and Optimum Elm And Ann Modelmentioning

confidence: 99%

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

Deo

Şahin

2015

Atmospheric Research

260

102

View full text Add to dashboard Cite

The prediction of future drought is an effective mitigation tool for assessing adverse consequences of drought events on vital water resources, agriculture, ecosystems and hydrology. Data-driven model predictions using machine learning algorithms are promising tenets for these purposes as they require less developmental time, minimal inputs and are relatively less complex than the dynamic or physical model. This paper authenticates a computationally simple, fast and efficient non-linear algorithm known as extreme learning machine (ELM) for the prediction of Effective Drought Index (EDI) in eastern Australia using input data trained from 1957-2008 and the monthly EDI predicted over the period 2009-2011. The predictive variables for the ELM model were the rainfall and mean, minimum and maximum air temperatures, supplemented by the large-scale climate mode indices of interest as regression covariates, namely the Southern Oscillation Index, Pacific Decadal Oscillation, Southern Annular Mode and the Indian Ocean Dipole moment. To demonstrate the effectiveness of the proposed datadriven model a performance comparison in terms of the prediction capabilities and learning speeds was conducted between the proposed ELM algorithm and the conventional artificial neural network (ANN) algorithm trained with Levenberg-Marquardt back propagation. The prediction metrics certified an excellent performance of the ELM over the ANN model for the overall test sites, thus yielding Mean Absolute Errors, Root-Mean Square Errors, Coefficients of Determination and Willmott's Indices of Agreement of 0.277, 0.008, 0.892 and 0.93 (for ELM) and 0.602, 0.172, 0.578 and 0.92 (for ANN) models. Moreover, the ELM model was executed with learning speed 32 times faster and training speed 6.1 times faster than the ANN model. An improvement in the prediction capability of the drought duration and severity by the ELM model was achieved. Based on these results we aver that out of the two machine learning algorithms tested, the ELM was the more expeditious tool for prediction of drought and its related properties.

show abstract

Section: Network Architecture and Optimum Elm And Ann Modelmentioning

confidence: 99%

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

Deo

Şahin

2015

Atmospheric Research

260

102

View full text Add to dashboard Cite

show abstract

“…Somvanshi et al developed a rainfall prediction model which made use of the past observations of Hyderabad (India) region [13]. Chattopadhyay developed a three layer neural model which used the data ranging from 1950 to 1995 to predict the average monsoon rainfall in India [14]. Hung et al developed a neural network that could predict the rainfall in real time in Bangkok [15].…”

Section: Previous Workmentioning

confidence: 99%

Artificial Neural Network Models for Rainfall Prediction in Pondicherry

Dubey¹

2015

IJCA

View full text Add to dashboard Cite

Rainfall forecasting plays an important role in catchment management applications, the flood warning system being one of them. Rainfall forecasting is one of the most difficult tasks given the variability of space, time and other given conditions change rapidly. Over the years, with the evolution of the intelligent computing methods, many rainfall prediction methods have been proposed, Artificial Neural Network being one of the most prominent. Since the last decade, many researchers have proposed different artificial neural network models in order to create accurate rainfall prediction models. In this paper, different artificial neural networks have been created for the rainfall prediction of Pondicherry, a coastal region in India. These ANN models were created using three different training algorithms namely, feed-forward back propagation algorithm, layer recurrent algorithm and feedforward distributed time delay algorithm. The number of neurons for all the models was kept at 20. The mean squared error was measured for each model and the best accuracy was obtained by feed-forward distributed time delay algorithm with MSE value as low as .0083.

show abstract

“…A back propagation network [9] consists of at least three layers (multi layer perception): an input layer, at least one intermediate hidden layer, and an output layer. In contrast to the Interactive Activation and Competition (IAC) neural networks (IAC) and Hopfield networks, connection weights in a back propagation network are one way.…”

Section: )mentioning

confidence: 99%

An Efficient Weather Forecasting System using Artificial Neural Network

Baboo¹,

Shereef²

2010

IJESD

141

View full text Add to dashboard Cite

Abstract-Temperature warnings are important forecasts because they are used to protect life and property. Temperature forecasting is the application of science and technology to predict the state of the temperature for a future time and a given location. Temperature forecasts are made by collecting quantitative data about the current state of the atmosphere. In this paper, a neural network-based algorithm for predicting the temperature is presented. The Neural Networks package supports different types of training or learning algorithms. One such algorithm is Back Propagation Neural Network (BPN) technique. The main advantage of the BPN neural network method is that it can fairly approximate a large class of functions. This method is more efficient than numerical differentiation. The simple meaning of this term is that our model has potential to capture the complex relationships between many factors that contribute to certain temperature. The proposed idea is tested using the real time dataset. The results are compared with practical working of meteorological department and these results confirm that our model have the potential for successful application to temperature forecasting. Real time processing of weather data indicate that the BPN based weather forecast have shown improvement not only over guidance forecasts from numerical models, but over official local weather service forecasts as well.

show abstract

Feed forward Artificial Neural Network model to predict the average summer-monsoon rainfall in India

Cited by 94 publications

References 36 publications

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

Artificial Neural Network Models for Rainfall Prediction in Pondicherry

An Efficient Weather Forecasting System using Artificial Neural Network

Contact Info

Product

Resources

About