A rainfall prediction model using artificial neural network

Abhishek, Kumar; Kumar, Abhay; Ranjan, Rajeev; Kumar, Shubham

doi:10.1109/icsgrc.2012.6287140

Cited by 158 publications

(80 citation statements)

References 4 publications

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“…This strategy consolidates the Support Vector Machine (SVM) and Fuzzy Logic techniques. Predictive models keeping in mind the end goal to foresee downpour power (mm/day) in Athens, Greece, by Artificial Neural Networks (ANN) models, has been raised by P. T. Nastos et al [15]. The ANNs results stress the conspired mean, greatest and least month to month downpour power for the following four progressive months in Athens.…”

Section: Related Workmentioning

confidence: 99%

Efficient Dissemination of Rainfall Forecasting to Safeguard Farmers from Crop Failure Using Optimized Neural Network Model

Ananthanarayanan¹,

Balan²,

Balamurali³

et al. 2017

IJIES

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Abstract:In the field of weather forecasting, especially in rainfall prediction many researchers employed different data mining techniques. There is numerous method of organizing agricultural engineering substance and it remains an open research issue particularly when taking to distinctive arrangements of clients -farmers, agricultural engineers, agri-organizations -both from proficiency point of view. Keeping these factors Indian farmers in mind, we have chosen to do research on efficient dissemination of rainfall forecasting to safeguard farmers from crop failure using optimized neural network (NN) model. Here, at first, we generate the feature matrix based on five feature indicator. Once the feature matrix is formed, the prediction is done based on the hybrid classifier. In hybrid classifier, particle swarm optimization algorithm is combined with Grey Wolf optimization for training the RBF NN. The performance of the algorithm is analyzed with the help of real datasets gathered from pechiparai and perunchani regions.

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Section: Related Workmentioning

confidence: 99%

Efficient Dissemination of Rainfall Forecasting to Safeguard Farmers from Crop Failure Using Optimized Neural Network Model

Ananthanarayanan¹,

Balan²,

Balamurali³

et al. 2017

IJIES

View full text Add to dashboard Cite

show abstract

“…The available data may be noisy thus, data should be cleaned. Similarly, it has to be normalized because, all the parameters are of different units and normalization will help the input and output parameters to correlate with each other [6]. The data should be divided in training and testing samples in proper proportion so that the results can be predicted, tested and validated properly.…”

Section: Artificial Neural Network For Weather Forecastingmentioning

confidence: 99%

“…Kumar Abhishek et al, 2012 [6] develops an ANN model to forecast average monthly rainfall. He selected data from Udupi, Karnataka which is eight months data for fifty years making 400 entries for input and output.…”

Section: Literature Surveymentioning

confidence: 99%

Daily Weather Forecasting using Artificial Neural Network

Narvekar¹,

Fargose²

2015

IJCA

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Daily Weather forecasting is used for multiple reasons in multiple areas like agriculture, energy supply, transportations, etc. Accuracy of weather conditions shown in forecast reports is very necessary. In this paper, the review is conducted to investigate a better approach for forecasting which compares many techniques such as Artificial Neural Network, Ensemble Neural Network, Backpropagation Network, Radial Basis Function Network, General Regression Neural Network, Genetic Algorithm, Multilayer Perceptron, Fuzzy clustering, etc. which are used for different types of forecasting. Among which neural network with the backpropagation algorithm performs prediction with minimal error. Neural network is a complex network which is self-adaptive in nature. It learns by itself using the training data and generates some intelligent patterns which are useful for forecasting the weather. This paper reviews various techniques and focuses mainly on neural network with back propagation technique for daily weather forecasting. The technique uses 28 input parameters to forecast the daily weather in terms of temperature, rainfall, humidity, cloud condition, and weather of the day.

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“…A study was conducted on detection of nonlinear response and damage detection on signal processing, and concluded that artificial neural networks (ANN) can be used for modeling and forecasting nonlinear time series. Recently ,numerous ANN-based rainfallrunoff models have been proposed to forecast stream flow [8] [9][10] and reservoir inflow. In addition, neural networks and fuzzy logic have been used as effective modeling tools in different environmental processes such as waste water treatment, water treatment and air pollution.…”

Section: Literature Surveymentioning

confidence: 99%