Seasonal prediction of tropical cyclone activity over the north Indian Ocean using three artificial neural networks

Nath, Sankar; Kotal, S. D.; Kundu, Prabir Kumar

doi:10.1007/s00703-016-0446-0

Cited by 18 publications

(17 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ability to learn through examples and generalize information learned is the main attraction of troubleshooting through ANNs (Nath et al, 2016). Generalization, which is associated with the network capacity to learn through a small set of examples giving consistent answers to unknown data, demonstrates the ANN's ability to go far beyond a simple mapping of input and output relations.…”

Section: Introductionmentioning

confidence: 99%

Reference Evapotranspiration Forecasting by Artificial Neural Networks

2017

View full text Add to dashboard Cite

Evapotranspiration (ET) is the main component of water balance in agricultural systems and the most active variable of the hydrological cycle. In the literature, few studies have used the forecast the day before via Artificial Neural Networks (ANNs) for the northern region of São Paulo state, Brazil. Therefore, this aimed to predict the reference evapotranspiration for Jaboticabal, the major sugarcane-producing region of São Paulo state. We used a historical series of data on average air temperature, wind speed, net radiation, soil heat flux, and daily relative humidity from 2002 to 2012, for Jaboticabal, SP (Brazil). ET was estimated by Penman-Monteith method. To forecast reference evapotranspiration, we used a feed-forward Multi-Layer Perceptron (MLP), which is a traditional Artificial Neural Network. Numerous topologies and variations were tested between neurons in intermediate and outer layers until the most accurate were obtained. We separated 75% from data for network training (2002 to 2010) and 25% for testing (2011 to 2013). The criteria for assessing the ANN performance were accuracy, precision, and trend. ET could be accurately estimated with a day to spare at any time of the year, by means of artificial neural networks, and using only air temperature data as an input variable.

show abstract

Section: Introductionmentioning

confidence: 99%

Reference Evapotranspiration Forecasting by Artificial Neural Networks

2017

View full text Add to dashboard Cite

show abstract

“…Some other models, which are found to have strong abilities to forecast tropical cyclonic occurrences and landfalls, are the threshold autoregressive model-a hybrid statistical model combining the regression model and the time-series autoregressive model- (Feng & Luo, 2014), the auto-regressive integrated moving average (ARIMA) model (Geetha & Narisa, 2016), projection pursuit regression with smooth multiple additive regression technique (SMART) generalization (Chan et al, 1998), and the least sum of absolute deviations regression (Gray et al, 1993(Gray et al, , 1994. Studies have also been carried out on long-term forecasting of tropical cyclones using support vector regression model (Richman & Leslie, 2012;Wijnands et al, 2014), multilayer perceptron (Nath et al, 2016) and feed-forward neural networks (FFNN) (Yip & Yau, 2012). Recently, Mitchell and Camp (2021) have applied the Conway-Maxwell-Poisson distribution to model seasonal tropical cyclone counts for East China and forecasted its tropical cyclones over a few months ahead.…”

Section: Overview Of Literaturementioning

confidence: 99%

Long-term forecasting of tropical cyclones over Bay of Bengal using linear and non-linear statistical models

2021

View full text Add to dashboard Cite

Forecasting tropical cyclones with climate and physical variability and observed cyclonic disturbances has been developed over the years for all the ocean basins successfully and is still one of the priorities for disaster risk reduction policymaking. This study attempts to forecast seasonal cyclonic disturbances and severe cyclonic storms over the Bay of Bengal, where about 80% of the tropical cyclones of the North Indian Ocean are formed. We have used three time-series models, namely, the seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) model, artificial neural network-nonlinear autoregressive with exogenous variables (ANN-NARX) model, and the hybrid model. The basic purpose of considering three different models is to improve the forecasting accuracy of tropical cyclones. We have shown that the intensification rate of the severe cyclonic storms over the Bay of Bengal has been significant and increasing over the years. Results show that the ANN-NARX model with sea surface temperature and near-surface wind speed as predictors is the best performance model for longterm forecasting of cyclonic disturbances. Hence, the distribution of cyclonic disturbances is non-linear. The correlations between observed and predicted occurrences are 0.80 and 0.85 for cyclonic disturbances and severe cyclonic storms, respectively, corroborating, by and large, the forecasting accuracies of some previous studies. The forecasting of cyclonic disturbances indicates that they will vary from 5 to 13 annually and there will be, on average, one severe cyclonic storm per year. The likelihood of occurrence of severe cyclonic storms is most significant in the post-monsoon season. This forecast till 2050 would help the scientific community and policymakers significantly for applications and good disaster risk governance.

show abstract

“…Therefore, the RBF-ANN avoids lengthy iterative calculations, such as those found in the learning algorithms of back propagation neural networks, and the possibility of falling into a local extremum. RBF-ANN is widely used in many fields, including meteorology (Nath et al, 2016), soil (Zakian, 2017), vegetation (Hilbert and Ostendorf, 2001), and engineering control (Sarimveis et al, 2004).…”

Section: Radial Basis Function Artificial Neural Networkmentioning

confidence: 99%

Supplementary material to "Improving maps of forest aboveground biomass: A combined approach using machine learning with a spatial statistical model"

Dai¹,

Zheng²,

Gao³

et al. 2020

Preprint

View full text Add to dashboard Cite

Seasonal prediction of tropical cyclone activity over the north Indian Ocean using three artificial neural networks

Cited by 18 publications

References 32 publications

Reference Evapotranspiration Forecasting by Artificial Neural Networks

Reference Evapotranspiration Forecasting by Artificial Neural Networks

Long-term forecasting of tropical cyclones over Bay of Bengal using linear and non-linear statistical models

Supplementary material to "Improving maps of forest aboveground biomass: A combined approach using machine learning with a spatial statistical model"

Contact Info

Product

Resources

About

Seasonal prediction of tropical cyclone activity over the north Indian Ocean using three artificial neural networks

Cited by 18 publications

References 32 publications

Reference Evapotranspiration Forecasting by Artificial Neural Networks

Reference Evapotranspiration Forecasting by Artificial Neural Networks

Long-term forecasting of tropical cyclones over Bay of Bengal using linear and non-linear statistical models

Supplementary material to &quot;Improving maps of forest aboveground biomass: A combined approach using machine learning with a spatial statistical model&quot;

Contact Info

Product

Resources

About

Supplementary material to "Improving maps of forest aboveground biomass: A combined approach using machine learning with a spatial statistical model"