Long-term analysis of thunderstorm-related parameters over Visakhapatnam and Machilipatnam, India

Umakanth, N.; Satyanarayana, G.; Simon, B.; Rao, M. C.; Babu, N. Ranga

doi:10.1007/s11600-020-00431-2

Cited by 32 publications

(12 citation statements)

References 21 publications

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“…A neural network is useful for modeling the non-linear relation between the input and output of a system [1]. Compared to other machine learning methods such as autoregressive moving averages (ARMA), autoregressive integrated moving averages (ARIMA), and random forest (RF), the ANN model showed better performance in regression prediction problems [2][3][4]. According to Agrawal [5], the ANN model predicted rainfall events more accurately than the ARIMA model.…”

Section: Introductionmentioning

confidence: 99%

Increasing Neurons or Deepening Layers in Forecasting Maximum Temperature Time Series?

2020

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Weather forecasting, especially that of extreme climatic events, has gained considerable attention among researchers due to their impacts on natural ecosystems and human life. The applicability of artificial neural networks (ANNs) in non-linear process forecasting has significantly contributed to hydro-climatology. The efficiency of neural network functions depends on the network structure and parameters. This study proposed a new approach to forecasting a one-day-ahead maximum temperature time series for South Korea to discuss the relationship between network specifications and performance by employing various scenarios for the number of parameters and hidden layers in the ANN model. Specifically, a different number of trainable parameters (i.e., the total number of weights and bias) and distinctive numbers of hidden layers were compared for system-performance effects. If the parameter sizes were too large, the root mean square error (RMSE) would be generally increased, and the model’s ability was impaired. Besides, too many hidden layers would reduce the system prediction if the number of parameters was high. The number of parameters and hidden layers affected the performance of ANN models for time series forecasting competitively. The result showed that the five-hidden layer model with 49 parameters produced the smallest RMSE at most South Korean stations.

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Section: Introductionmentioning

confidence: 99%

Increasing Neurons or Deepening Layers in Forecasting Maximum Temperature Time Series?

2020

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“…Topographical differences, wind regimes, and the inland distance far from the coastal and hilly regions may differ sensitivity results in these categories. Based on the sensitivity assessment, TSD, IMF1, and IMF2 generated the highest score, similar to other thunderstorm-associated parameters in India by Umakanth et al (2020). TSD is very high in sensitivity analysis due to an enhanced number of TSF causing moist air circulated from the Bay of Bengal (BoB).…”

Section: Discussionmentioning

confidence: 72%

Development of novel hybrid machine learning models for monthly thunderstorm frequency prediction over Bangladesh

et al. 2021

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Accurate thunderstorm frequency (TSF) prediction is of great significance under climate extremes for reducing potential damages. However, TSF prediction has received little attention because a thunderstorm event is a combination of intricate and unique weather scenarios with high instability, making it difficult to predict. To close this gap, we proposed a novel hybrid machine learning model through hybridization of data pre-processing Ensemble Empirical Mode Decomposition (EEMD) with two state-of-arts models namely artificial neural network (EEMD-ANN), support vector machine (EEMD-SVM) for TSF prediction at three categories of yearly frequencies over Bangladesh. We were demarcated the yearly TSF datasets into three categories for the period 1981-2016 recorded at 28 sites; high (March-June), moderate (July-October), and low (November-February) TSF months. The performance of the proposed EEMD-ANN and EEMD-SVM hybrid models was compared 2 with classical ANN, SVM, Autoregressive Integrated Moving Average (ARIMA). EEMD-ANN and EEMD-SVM hybrid models showed 8.02%-22.48% higher performance precision in terms of root mean square error (RMSE) compared to other models at high, moderate and low-frequency categories. Eleven out of 21 input parameters were selected based on the Random Forest (RF) variable importance analysis. The sensitivity analysis results showed that each input parameter was positively contributed to building the best model of each category and thunderstorm days are the most contributing parameters influencing TSF prediction. The proposed hybrid models outperformed the conventional models where EEMD-ANN is the most skillful for high TSF prediction, and EEMD-SVM is for moderate and low TSF prediction. The findings indicate the potential of hybridization of EEMD with the conventional models for improving prediction precision. The hybrid model developed in this work can be adopted for TSF prediction in Bangladesh as well as different parts of the world.

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“…Similarly [20], they have been proposed to use the data from 11 intense thunderstorms to train the random forest, a support vector machine, and a boosting tree (SVM) while also modifying the misclassification cost in the classifier (cost-sensitive learning) and resampling the training set (resampling). Likewise [21], the NCEP NCAR reanalysis monthly dataset for temperature (t) and relative humidity (RH) from 1948 to 2012 ARMA and an artificial neural network have been developed using this technology (ANN), with the ANN achieving a correlation with observations of 91% and the ARMA performing a correlation of 81%. While [22] MYRORSS, A numerical weather model with a rapid update cycle dataset, has 22 901 storms from 2004-11.…”

Section: 1mentioning

confidence: 99%

Thunderstorms Prediction Using Satellite Images

Amjad¹,

Malik²,

Ghous³

et al. 2023

ijisct

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Human life and property are put at risk by lightning, heavy hail, and strong winds caused by thunderstorms. Accurate prediction of thunderstorms has long been a difficult problem. In difference from conventional methods, the modern methods for predicting thunderstorms utilize a combination of computer models, observation (via the use of radars and satellite photos), and modern knowledge and patterns employed by thunderstorms stations. Observations: Using these methods, it is possible to make reasonably accurate predictions. Prediction models based on complex formulas run the majority of forecasting computer models. Artificial neural networks, machine learning, deep learning, and statistics are all examples of adaptive systems that alter their structure in response to information flowing through the network. In this reviewed paper, different Artificial intelligence techniques are used such as machine learning, deep learning, and ANN (Artificial neural network). Different statistical models were presented to predict thunderstorm systems. Various researchers achieved technical milestones by presenting statistical methods on thunderstorm systems using satellite data.

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Long-term analysis of thunderstorm-related parameters over Visakhapatnam and Machilipatnam, India

Cited by 32 publications

References 21 publications

Increasing Neurons or Deepening Layers in Forecasting Maximum Temperature Time Series?

Increasing Neurons or Deepening Layers in Forecasting Maximum Temperature Time Series?

Development of novel hybrid machine learning models for monthly thunderstorm frequency prediction over Bangladesh

Thunderstorms Prediction Using Satellite Images

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