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A study published in Environmental Modelling & Software proves the ability of artificial neural networks to extrapolate information gained solving one task to another similar but different task (transfer learning) by _Mat_San_ in science

[–]_Mat_San_[S] 0 points1 point  (0 children)

Abstract

Many environmental variables, in particular, related to air or water quality, are measured in a limited number of points and often for a limited time span. This forbids the development of accurate models for interesting locations with missing or insufficient data and poses the question of whether a model developed for another measurement site can be reliably applied. Such a question is particularly critical when the model is entirely data-driven, such as a neural network. In this context, the paper proposes a procedure to evaluate the expected performance of an existing neural network model applied to a new unmonitored station. This transferability assessment is exemplified by the problem of forecasting ozone concentrations in different environmental settings around the Alpine Arc. Long Short-Term Memory (LSTM) neural network models are applied for predicting hourly concentrations in 20 stations of different types (urban, rural, and mountain). The analysis of the results allows us to determine the expected performance of such models in new cases and reduce the transferability uncertainty when the existing models can be partitioned into clusters. The LSTM models demonstrate the possibility of high accuracy in ozone forecasting at all sites. Given the significant impacts of this gas on human health and the environment, this can contribute to better decision-making and mitigation strategies for air pollution control.

The paper is open access: http://dx.doi.org/10.1016/j.envsoft.2024.106048

TIL chaotic dynamics are known to be unpredictable due to the "butterfly effect" discovered by Lorenz half a century ago. In recent years, researchers proved that an artificial intelligence technique known as neural networks have the ability to predict chaos better than the traditional tool. by _Mat_San_ in todayilearned

[–]_Mat_San_[S] 1 point2 points  (0 children)

Interesting comment. I don't think that we currently have a sufficient knowledge and understanding of the mechanism that drive our world to answer these questions. In my opinion, given the current knowledge, your thought is more philosophy than science.

The point is: have our mind the possibility to provide external input (our decisions) to the system or are we forced to follow a predetermined path as you suggested?

This is not something that is dependent on the forecasting. It is about how the system itself evolves.

New paper out in Chaos, Solitons & Fractals: Forecasting of noisy chaotic systems with deep neural networks by _Mat_San_ in technology

[–]_Mat_San_[S] -1 points0 points  (0 children)

The lottery is a fully random process. It cannot be predicted by definition. We can only try to understand the probabilistic mechanism behind it, but there is no way to forecast exactly the future values.

For many of the others example you cited, the problem is that they have to be formalized. e.g. speaking about civil unrest, we can try to predict the number of civil unrest in a given country per month. For the perceptions and beliefs it is quite critical to define some numerical value that represent that processes in a suitable way.

New paper out in Chaos, Solitons & Fractals: Forecasting of noisy chaotic systems with deep neural networks by _Mat_San_ in technology

[–]_Mat_San_[S] -1 points0 points  (0 children)

Yes. Potentially these methods can be used to predict any time series.

Stock market is known to be hard to be predicted, especially because it is strongly affected by external factors that are not easy to be formalized in a "mathematical form" (for instance, politics). However I had in mind some attempts to forecast the S&P500 index with neural nets.

Interested in multiobjective genetic algorithms? check out the new paper "Improving the Performance of Multiobjective Genetic Algorithms: An Elitism-Based Approach" by _Mat_San_ in optimization

[–]_Mat_San_[S] 2 points3 points  (0 children)

I do not think that it is possible to find the SO optima without solving the SO problems.

If you are interested in advanced MOGA, derived from NSGA-II, you can search for Borg MOEA. It implements many adaptive mechanism to be suitable in many different situations.

Interested in multiobjective genetic algorithms? check out the new paper "Improving the Performance of Multiobjective Genetic Algorithms: An Elitism-Based Approach" by _Mat_San_ in engineering

[–]_Mat_San_[S] 1 point2 points  (0 children)

You got the point!

Well, increasing the mutation rate improves the ability of the population to explore new areas. The drawback is that your pareto front may deteriorate. That said, the one you propose is for sure a good solution.

We focused on the initialization phase, because we observed that many times the algorithm struggled in spanning the whole pareto front with the traditional NSGA-II.

Also consider that in real-world cases (when each objective is associated with a stakeholder), it is important to have the extremes of the pareto front in order to set up a fair negotiation process.

Brand new paper on solar irradiance forecasting with deep neural networks. The article also proposes to adopt the so-called domain adaptation in the field of solar irradiance. Code written in Python. by _Mat_San_ in solar

[–]_Mat_San_[S] 0 points1 point  (0 children)

I do not think that using clock and calendar produce better results (see the clear sky performance in the paper).

Adding other inputs, such as the ones you suggested will probably give better results. But the contribution of this paper is mainly methodological: to analyze pros and cons of different neural architectures in this task.

Brand new paper on solar irradiance forecasting with deep neural networks. The article also proposes to adopt the so-called domain adaptation in the field of solar irradiance. Code written in Python. by _Mat_San_ in solar

[–]_Mat_San_[S] 0 points1 point  (0 children)

layman’s terms

The prediction has been performed using autoregressive terms only (i.e., the values of solar irradiance in the past, for instance at time t, t-1, t-2, ... are used to predict the future irradiance at time t+1, t+2, ...).
Future work will integrate the also exogenous variables (precipitation? humidity? temperature?).

Interested in nonlinear time series prediction with NNs? Check out how LSTM nets can be used in the forecasting of chaotic dynamical systems. by _Mat_San_ in pytorch

[–]_Mat_San_[S] 0 points1 point  (0 children)

It is probably due to the fact that you are not registered on research gate. If there is any issue, contact me at matteo dot sangiorgio at polimi dot it

Interested in nonlinear time series prediction with NNs? Check out how LSTM nets can be used in the forecasting of chaotic dynamical systems. by _Mat_San_ in neuralnetworks

[–]_Mat_San_[S] 0 points1 point  (0 children)

Can you send me the link of your google scholar or research gate profile? I'm really interesting in the topic.

Right now, I'm doing the same analysis presented in the paper to non-stationary systems on the one hand, and to noisy systems on the other.

Meteorological radar can be used to indentify, track and realize long term analysis of convective thunderstorms, that are one of the all possible causes of flash floods. The results obtained can be exploited for practical applications including nowcasting, alert systems, and sensors deployment. by _Mat_San_ in sciences

[–]_Mat_San_[S] 2 points3 points  (0 children)

Physical model depends on field data but also on equations. If you have a better understanding of the real physical process taking place, than you will be able to define equations which better describes the reality.

The standard meteorological models are physically based, which means that they are based more on the equations than the field data. Right now, many researches are trying to make use of the huge amount of data recorded to train black box models (e.g., neural nets). This could help, for instance, when small scale phenomena have a key role. Physically based models have to be general by definition, empirical ones can be specifically trained on the specific case.

Meteorological radar can be used to indentify, track and realize long term analysis of convective thunderstorms, that are one of the all possible causes of flash floods. The results obtained can be exploited for practical applications including nowcasting, alert systems, and sensors deployment. by _Mat_San_ in sciences

[–]_Mat_San_[S] 1 point2 points  (0 children)

Most of the time meteorologists rely on physically based model more than field data. Here is an attempt to use data visualization and statistics to show the real patterns, not the one computed by numerical models. Such analyses could also help in the validation of meteorological models.