Prediction of Rainfall in India using Artificial Neural Network (ANN) Models

Nanda, Santosh Kumar; Tripathy, Debi Prasad; Nayak, Simanta Kumar; Mohapatra, Subhasis

doi:10.5815/ijisa.2013.12.01

Cited by 38 publications

(26 citation statements)

References 15 publications

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“…e present work is different from the majority of the work reported on the forecasting of rainfall at different time scale, where the time series data of either rainfall or meteorological parameters are utilized to forecast the timelagged rainfall. In the present scenario of rainfall forecasting, researchers have utilized various sets of surface meteorological variables such as minimum and maximum temperature [35,39,41,[43][44][45][46]. Some other researchers have utilized extended meteorological parameters in addition to temperature variables such as relative humidity and previous day rainfall [62].…”

Section: Summary Discussion and Conclusionmentioning

confidence: 99%

“…By using the soft computing techniques, the rainfall forecasting can be categorized in two groups: either by using historical time series rainfall data [48] or by using historical time series data of meteorological variables [45]. Over the Indian region, ANN techniques have been utilized extensively for forecasting of long range Indian summer monsoon rainfall with time lagged climatic indices-rainfall relationship approach [49] or from the stochastic time series data of rainfall [35,41]. e simulation of daily rainfall by using height profiles of concurrent meteorological parameters is still to be seen over the Indian monsoon region in particular and over the globe in general.…”

Section: Introductionmentioning

confidence: 99%

“…e advantage of an ANN approach is that it can be used to develop a functional relationship, including a nonlinear relationship, amongst the various parameters of the process under study even in the absence of full understanding of its mathematical model [34]. e ANN techniques such as feedforward, feedback, and competitive neural networks are extensively used for rainfall forecasting at different time scales such as, yearly [35][36][37], monthly/seasonal [13,14,[38][39][40][41][42][43][44], and weekly/daily basis [45][46][47]. By using the soft computing techniques, the rainfall forecasting can be categorized in two groups: either by using historical time series rainfall data [48] or by using historical time series data of meteorological variables [45].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Simulation of Daily Rainfall from Concurrent Meteorological Parameters over Core Monsoon Region of India: A Novel Approach

Misra

Deshamukhya

Sharma³

et al. 2018

Advances in Meteorology

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In the present work, daily rainfall is simulated over the core monsoon region of India by using a feedforward multilayer perceptron (MLP) model. Daily rainfall is found to be optimally dependent on four concurrent meteorological parameters, namely, geopotential height, specific humidity, zonal, and meridional wind at 1000 mb, 925 mb, 850 mb, and 700 mb pressure levels during 00, 06, 12, and 18 Greenwich Mean Time (GMT). e architecture of the optimized feedforward MLP model consists of 64 nodes in the input layer, 10 nodes in the hidden layer, and 1 node in the output layer. e results from the model are compared with the 3B42 (version 7) rainfall product. In terms of root mean square error (rmse) and correlation coefficient (cc), the model is performing better compared to the satellite-derived 3B42 rainfall product, whereas in terms of bias, the performance of the 3B42 product is better compared to the model. e weight matrices of the feedforward MLP model are estimated at a particular location (22.5°N, 82.5°E). ese weight matrices are able to simulate daily rainfall at neighbourhood locations also with reasonably good accuracy with cc in the range of 0.41 to 0.55. e performance of the model improves in case of an aerial average of daily rainfall with significantly enhanced cc (0.72). e model is able to capture monthly and intraseasonal variation of rainfall with reasonably good accuracy, with cc of 0.88 and 0.68, respectively. e simulation model has a limitation that it is not able to simulate extreme high rainfall events (>60 mm/day). Overall, the developed model is performing reasonably well. is approach has a potential to be used as a rain parameterization scheme in the dynamical atmospheric and coupled models to simulate daily rainfall. Nevertheless, the present approach can also be used for multistep prediction of rainfall.

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Section: Summary Discussion and Conclusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Simulation of Daily Rainfall from Concurrent Meteorological Parameters over Core Monsoon Region of India: A Novel Approach

Misra

Deshamukhya

Sharma³

et al. 2018

Advances in Meteorology

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show abstract

“…Nanda et al used the Multi Layer Perceptron, Functionallink Artificial Neural Network and Legendre Polynomial Equation models for prediction of Rainfall in India. Quite accurate results were obtained with these models for complex time series model [15]. Ghiasi-Freez et al optimized the Neural Network models for the prediction of nuclear magnetic resonance parameters in carbonate reservoir.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

A Study on the Diagnosis of Parkinson’s Disease using Digitized Wacom Graphics Tablet Dataset

Akyol¹

2017

IJITCS

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Abstract-ParkinsonDisease is a neurological disorder, which is one of the most painful, dangerous and noncurable diseases, which occurs at older ages. The Static Spiral Test, Dynamic Spiral Test and Stability Test on Certain Point records were used in the application which was developed for the diagnosis of this disease. These datasets were divided into 80-20% training and testing data respectively within the framework of 10-fold cross validation technique. Training data as the input data were sent to the Random Forest, Logistic Regression and Artificial Neural Networks classifier algorithms. After this step, performances of these classifier algorithms were evaluated on testing data. Also, new data analysis was carried out. According to the results obtained, Artificial Neural Networks is more successful than Random Forest and Logistic Regression algorithms in analysis of new data.

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“…Genetic Algorith [22] and Artificial Neural Network [23,24,25,26], Multi-Layer Perceptron (MLP), Functional Link Artificial Neural Network (FLANN) and Legendre Polynomial Equation (LPE) [27], Multiple Linear Regression (MLR) techniques [28] were introduced for Rainfall prediction.…”

Section: Related Workmentioning

confidence: 99%

Development and Analysis of Artificial Neural Network Models for Rainfall Prediction by Using Time-Series Data

Mishra¹,

Soni²,

Sharma³

et al. 2018

IJISA

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Abstract-Time Series data is large in volume, highly dimensional and continuous updating. Time series data analysis for forecasting, is one of the most important aspects of the practical usage. Accurate rainfall forecasting with the help of time series data analysis will help in evaluating drought and flooding situations in advance. In this paper, Artificial Neural Network (ANN) technique has been used to develop one-month and twomonth ahead forecasting models for rainfall prediction using monthly rainfall data of Northern India. In these model, Feed Forward Neural Network (FFNN) using Back Propagation Algorithm and Levenberg-Marquardt training function has been used. The performance of both the models has been assessed based on Regression Analysis, Mean Square Error (MSE) and Magnitude of Relative Error (MRE). Proposed ANN model showed optimistic results for both the models for forecasting and found one month ahead forecasting model perform better than two months ahead forecasting model. This paper also gives some future directions for rainfall prediction and time series data analysis research.

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Prediction of Rainfall in India using Artificial Neural Network (ANN) Models

Cited by 38 publications

References 15 publications

Simulation of Daily Rainfall from Concurrent Meteorological Parameters over Core Monsoon Region of India: A Novel Approach

Simulation of Daily Rainfall from Concurrent Meteorological Parameters over Core Monsoon Region of India: A Novel Approach

A Study on the Diagnosis of Parkinson’s Disease using Digitized Wacom Graphics Tablet Dataset

Development and Analysis of Artificial Neural Network Models for Rainfall Prediction by Using Time-Series Data

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