Neural network modelling for estimating linear and nonlinear influences of meteo-climatic variables on Sergentomyia minuta abundance using small datasets

Pasini, Antonello; Amendola, Stefano; Giacomi, Angelo; Calderini, Pietro; Barlozzari, Giulia; Macrì, Gladia; Pombi, Marco; Gabrielli, Simona

doi:10.1016/j.ecoinf.2020.101055

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…Our tool (Pasini 2015), adopted here, properly maximises the information for training, minimises overfitting problems and 'averages away' the NN model variability by multiple ensemble runs. This tool has been recently applied to several climate-driven problems: see, for instance, Pasini and Modugno (2013), Pasini et al (2017), Pasini and Amendola (2019), Pasini et al (2020). Here, we briefly sketch the main characteristic features of the tool, leaving further details to its presentation paper (Pasini 2015).…”

Section: Methodsmentioning

confidence: 99%

Is natural variability really natural? The case of Atlantic Multidecadal Oscillation investigated by a neural network model

Pasini

Amendola²,

Federbusch

2022

Theor Appl Climatol

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Is Atlantic Multidecadal Oscillation a genuine representation of natural variability in the climate system? Or perhaps is it strongly forced by external drivers? In this paper, a data-driven attribution investigation has been performed for the Atlantic Multidecadal Oscillation (AMO) behaviour in the past via a machine learning technique, NN modelling. We clearly see a forced nature of AMO in the last 150 years, with a strong contribution of the forcing coming from anthropogenic sulphates, which induces its typical oscillating behaviour. The following original application of our model to future predictions of the AMO behaviour shows that it shall probably lose its oscillating characteristic features. The only way to recover them is to consider an unrealistic increase in anthropogenic sulphates in the future under a strong mitigation scenario, and possibly a low-power solar regime. Due to the established influence of AMO on climate and meteorological phenomena in several regions of the world, our results can be important to better understand the past and envisage several future scenarios.

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Section: Methodsmentioning

confidence: 99%

Is natural variability really natural? The case of Atlantic Multidecadal Oscillation investigated by a neural network model

Pasini

Amendola²,

Federbusch

2022

Theor Appl Climatol

Self Cite

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“…In previous studies, the first step was to select the most sensitive predictors, which can be done by linear regression, nonlinear regression or machine learning methods (Pasini et al ., 2001; 2020). However, the proposed method selects the sensitive predictors after each working circle is finished, as shown in Figure 1.…”

Section: Fctf Neural Network Theorymentioning

confidence: 99%

Auto station precipitation data making up using an improved neuro net

Zhang²

2020

Meteorological Applications

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In the real world, precipitation data of automatic weather stations are easily influenced by direct thunderstrokes, instrument ageing, electromagnetic interference, human operation errors and other factors. When close to the observation time, if the missing automatic station data cannot be corrected in a timely fashion, the whole quality of the station data will be affected. Thus, correct handling of the missing precipitation data to maintain their integrity has important significance. In this paper, we propose a "from coarse to fine" (FCTF) neural network to fill out the missing blanks and experiments show that this method to solve the problem of meteorological data shortage is effective.

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Comparison of different forecasting tools for short-range lightning strike risk assessment

et al. 2022

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Thunderstorms, the main generator of lightning on earth, are characterized by the presence of extreme atmospheric conditions (turbulence, hail, heavy rain, wind shear, etc.). Consequently, the atmospheric conditions associated with this kind of phenomenon (in particular the strike itself) can be dangerous for aviation. This study focuses on the estimation of the lightning strike risk induced by thunderstorms over the sea, in a short-range forecast, from 0 to 24 h. In this framework, three methods have been developed and compared. The first method is based on the use of thresholds and weighting functions; the second method is based on a neural network approach, and the third method is based on the use of belief functions. Each method has been applied to the same dataset comprising predictors defined from numerical weather prediction model outputs. In order to assess the different methods, a “ground truth” dataset based on lightning stroke locations supplied by the World Wide Lightning Location Network (WWLLN) has been used. The choice of one method over the others will depend on the compromise that the user is willing to accept between false alarms, missed detections, and runtimes. The first method has a very low missed detection rate but a high false alarm rate, while the other two methods have much lower false alarm rates, but at the cost of a non-negligible missed detection rate. Finally, the third method is much faster than the other two methods.

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Neural network modelling for estimating linear and nonlinear influences of meteo-climatic variables on Sergentomyia minuta abundance using small datasets

Cited by 7 publications

References 23 publications

Is natural variability really natural? The case of Atlantic Multidecadal Oscillation investigated by a neural network model

Is natural variability really natural? The case of Atlantic Multidecadal Oscillation investigated by a neural network model

Auto station precipitation data making up using an improved neuro net

Comparison of different forecasting tools for short-range lightning strike risk assessment

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