Artificial Neural Network Models for Rainfall Prediction in Pondicherry

Dubey, Akash Dutt

doi:10.5120/21210-3910

Cited by 30 publications

(26 citation statements)

References 19 publications

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“…Numerous research works are proposed by various researchers using various machine learning techniques.Deepak RanjanNayak [15] surveyed about rainfall prediction using Artificial Neural Network. Akash D Dubey [16], made a prediction on Pondicherry about the rainfall. A semi-automatic segmentation method proposed by Rui Lu et al estimated the volume of tumor cells in the liver [15].…”

Section: Related Workmentioning

confidence: 99%

“…Though the process consumed quite a lot of time for computation it claimed to be very efficient in finding the volume of the tumor cells by segmenting it into slices. In [16], Kostas Haris introduced a hybrid image segmentation using a morphological algorithm for watersheds that combined edge and region-based techniques. The technique found to be effective as it reduced the number of false edge detection.…”

Section: Related Workmentioning

confidence: 99%

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Rainfall Prediction using Regression Model

2019

ijrte

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Artificial intelligence is been widely used in all the applications and weather forecasting is not an exception. When it comes to weather forecasting, rainfall prediction is one of the most widely used research areas as numerous lives and property damages occur due to this. Intense rainfall has abundant impacts on society and on our daily life from cultivation to disaster measures. Previous rainfall prediction models that are widely used, makes use of many the complicated blend of mathematical instruments which was insufficient to get a higher classification rate. In this project, we propose new novel methods for predicting monthly rainfall using linear regressionanalysis. Rainfall predictions are made by collecting quantitative data about the current state of the atmosphere. Numerous machine learning algorithms can learn complex mappings from inputs to outputs, based solely on samples and require limited. Accurate prediction of rainfall is a difficult task due to the dynamic nature of the atmosphere. To predict the future’s rainfall condition, the variation in the conditions in past years must be utilized. We have proposed the use of linear regressions by making use of various parameters such as temperature, humidity,and wind. The proposed model tends to forecast rainfall based on the previous records of a particular geographic area, therefore, this prediction will prove to be much reliable. The performance of the model is more accurate when compared with traditional rainfall prediction systems

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Rainfall Prediction using Regression Model

2019

ijrte

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“…The study has given a reportthat wet bulb temperature isconsidered as one amongst the key features that are used for such short term predictions thatmakes use of victimization feature choice technique. Akash D Dubey [3], researched on predicting the rainfall in Pondicherry district using Artificial Neural Networks. A literaturesurvey has encountered that [4,5,6,7] Artifical Neural Networks provides a better efficiency when compared to othertraditional classifiers for predicting the data.…”

Section: Related Workmentioning

confidence: 99%

“…Akash D Dubey [3], researched on predicting the rainfall in Pondicherry district using Artificial Neural Networks. A literaturesurvey has encountered that [4,5,6,7] Artifical Neural Networks provides a better efficiency when compared to othertraditional classifiers for predicting the data. ANN is also used immensely for the prediction of various tasks as it provides good accuracy.…”

Section: Related Workmentioning

confidence: 99%

Rainfall Prediction using Apriori Algorithm

2019

ijrte

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The growth of machine learning techniques has paved way for predicting various areas and weather prediction is one of the most commonly used application. One of the most interesting part of research is rainfall prediction Rainfall prediction is done by various researchers as it is one of the prime aspects in detecting the rainfall and measuring its intensity beforehand to reduce the number of loss of lives and properties. Nowadays, severe rainfall is one of the major reason is most of disasters such as floods and landslides. Predicting its occurrence well before could be very much helpful in further disaster management operations. In this paper, we have predicted rainfall by making use of various machine learning techniques and is been deployed by using it in predicting Chennai rainfall dataset. The research work makes use of Apriori Algorithm to demonstrate the feature set and to look out for the possibility of rainfall. Preprocessing of the dataset is done in order to extract the features of the dataset and then the Apriori Algorithm is used. The designed model is proposed to produce about 95% of accuracy, potency and lesser time when compared to other model

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“…Dubey [14] studied different neural network models for the purpose of rainfall prediction using climatic data and found out that the distributed time delay model outperformed back propagation and layer recurrent models.…”

Section: Related Workmentioning

confidence: 99%

K-Means based radial basis function neural networks for rainfall prediction

Dubey

2015

2015 International Conference on Trends in Automation, Communications and Computing Technology (I-Tact-15)

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Rainfall prediction problem has been one of the major issues of catchment management source water protection. Accurate rainfall prediction can be efficiently put to use by the agro based economy countries in terms of long term prediction. In this research work, a rainfall prediction model has been developed which uses K-Means clustering and artificial neural networks to fulfill the purpose. Artificial Neural Networks has been one of the major soft computing techniques used for the rainfall prediction since they are considered as one of the best function approximators. However, artificial neural networks have two issues which limit its applications, the computation complexity of the network and the learning time. In order to deal with these two issues, K-means clustering has been used in this work. Firstly, the data samples in this work are clustered using K means which cluster the samples according to their features. The number of clusters K has been computed using the Silhouette method. The clustered data samples are then used for training, validation and testing of different radial basis function neural networks. The results obtained from this method were then compared to the results obtained by only using a radial basis function neural network. For the comparison purpose, statistical criteria like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Nash-Sutcliffe model efficient coefficient (E) and Correlation Coefficient (R) were used. It was observed that the results obtained by this method (R= 0.94587, E=0.90148) were better than only using RBFNN (R= 0. 88015, E=0. 82159).

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Artificial Neural Network Models for Rainfall Prediction in Pondicherry

Cited by 30 publications

References 19 publications

Rainfall Prediction using Regression Model

Rainfall Prediction using Regression Model

Rainfall Prediction using Apriori Algorithm

K-Means based radial basis function neural networks for rainfall prediction

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