Use of Genetic Algorithms in Numerical Weather Prediction

Oana, Liviu; Spataru, Adrian

doi:10.1109/synasc.2016.075

Cited by 11 publications

(11 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of the main contributions of this work is to use a state-of-the-art ocean model as a prior step to the calculation of the fitness function, with all the complexity in terms of methodology, infrastructure and resources that this option entails. This is only possible because of the aforementioned availability of computing power, and it is also highly facilitated by the usage of advanced scientific workflow solutions, that allows the integration of the model executions in the evolutionary workflow (Oana and Spataru, 2016;Dueben and Bauer, 2018;Rueda-Bayona et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Use of Genetic Algorithms for Ocean Model Parameter Optimisation

Falls

Bernardello

Castrillo

et al. 2021

Preprint

View full text Add to dashboard Cite

Abstract. When working with Earth system models, a considerable challenge that arises is the need to establish the set ofparameter values that ensure the optimal model performance in terms of how they reflect real-world observed data. Giventhat each additional parameter under investigation increases the dimensional space of the problem by one, simple brute-forcesensitivity tests can quickly become too computationally strenuous. In addition, the complexity of the model and interactionsbetween parameters mean that testing them on an individual basis has the potential to miss key information. As such, this5work argues the need of the development of a tool that can give an estimation of parameters. Specifically it proposes the useof a Biased Random Key Genetic Algorithm (BRKGA). This method is tested using the one dimensional configuration ofPISCES-v2, the biogeochemical component of NEMO, a global ocean model. A test case of particulate organic carbon in theNorth Atlantic down to 1000 m depth is examined, using observed data obtained from autonomous biogeochemical Argo floats.In this case, two sets of tests are run, one where each of the model outputs are compared to the model outputs with default10settings, and another where they are compared with 3 sets of observed data from their respective regions, which is followed bya cross reference of the results. The results of these analyses provide evidence that this approach is robust and consistent, andalso that it provides indication of the sensitivity of parameters on variables of interest. Given the deviation of the optimal set ofparameters from the default, further analyses using observed data in other locations are recommended to establish the validityof the results obtained.

show abstract

Section: Discussionmentioning

confidence: 99%

Use of Genetic Algorithms for Ocean Model Parameter Optimisation

Falls

Bernardello

Castrillo

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…As we know, traffic congestions and accidents are highly possible in weather conditions like heavy fog, heavy rain and so on. Timely and accurate weather prediction can save us from weather disasters [2]. Weather prediction represents the scientific method of forecasting the state of atmosphere depending on time and certain locations [3].…”

Section: Introductionmentioning

confidence: 99%

Hyperparameter Tuned Bidirectional Gated Recurrent Neural Network for Weather Forecasting

Manikandan¹,

Nagaraj²

2022

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

Weather forecasting is primarily related to the prediction of weather conditions that becomes highly important in diverse applications like drought discovery, severe weather forecast, climate monitoring, agriculture, aviation, telecommunication, etc. Data-driven computer modelling with Artificial Neural Networks (ANN) can be used to solve non-linear problems. Presently, Deep Learning (DL) based weather forecasting models can be designed to accomplish reasonable predictive performance. In this aspect, this study presents a Hyper Parameter Tuned Bidirectional Gated Recurrent Neural Network (HPT-BiGRNN) technique for weather forecasting. The HPT-BiGRNN technique aims to utilize the past weather data for training the BiGRNN model and achieve the effective forecasts with minimum time duration. The BiGRNN is an enhanced version of Gated Recurrent Unit (GRU) that follows the process of passing input via forward and backward neural network and the outputs are linked to the identical output layer. The BiGRNN technique includes several hyper-parameters and hence, the hyperparameter optimization process takes place using Bird Mating Optimizer (BMO). The design of BMO algorithm for hyperparameter optimization of the BiGRNN, particularly for weather forecast shows the novelty of the work. The BMO algorithm is used to set hyperparameters such as momentum, learning rate, batch size and weight decay. The experimental result the HPT-BiGRNN approach has resulted in a lower RMSE of 0.173 whereas the Fuzzy-GP, Fuzzy-SC, MLP-ANN and RBF-ANN methods have gained an increased RMSE of 0.218, 0.216, 0.202 and 0.245 respectively.

show abstract

“…On the other hand, Oana and Spataru [11] studied the application of genetic algorithms in conjunction with WRF numerical weather prediction systems for optimizing and forecasting using GA. It was observed that the conjunction model was efficient and showed less error.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Models for Weather Parameter Forecasting

Bhardwaj

Duhoon

2021

Complexity

View full text Add to dashboard Cite

The objective of the paper is to compare hybrid conjunction models with conventional models for the reduction of errors in weather forecasting. Besides the simple models like RBF model, SMO model, and LibSVM model, different hybrid conjunction models have been used for forecasting under different schemes. The forecasts from these models are further compared on the basis of errors calculated and time taken by the hybrid models and simple models in order to forecast weather parameters. In this paper, conjunction models over the convectional models are designed for forecasting the weather parameters for the reduction of error. India is a tropical country with variations in weather conditions. The objective is to build a conjunction model with less error to forecast weather parameters. A hybrid conjunction model is developed and analysed for different weather parameters for different metropolitan cities of India. Performance measurement is analysed for weather parameters. It is observed that, on the basis of error comparison and time taken by the models, the hybrid wavelet-neuro-RBF model gives better results as compared to the other models due to lower values of determined errors, better performance, and lesser time taken. The study becomes significant as weather forecasting with accuracy is a complex task along with the reduction of prediction error by the application of different models and schemes. It is concluded that the proposed hybrid model is helpful for forecasting and making policies in advance for the betterment of the human being, farmers, tourists, and so on as in all these activities, weather forecast plays an important role.

show abstract

Use of Genetic Algorithms in Numerical Weather Prediction

Cited by 11 publications

References 9 publications

Use of Genetic Algorithms for Ocean Model Parameter Optimisation

Use of Genetic Algorithms for Ocean Model Parameter Optimisation

Hyperparameter Tuned Bidirectional Gated Recurrent Neural Network for Weather Forecasting

Hybrid Models for Weather Parameter Forecasting

Contact Info

Product

Resources

About