New Weather Forecasting Applications

Shouman, Mohamed

doi:10.21608/ajmris.2023.317449

Cited by 2 publications

(2 citation statements)

References 40 publications

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“…Besides testing the neural network on the performance metrics presented above, the FFNN is compared with "persistence" as a benchmark [52][53][54][55][56][57][58][59]. The persistent model assumes the phenomenon maintains its characteristics, which means the velocity vector, the average and maximum reflectivity, and the area at time t remain constant for the following steps until t + 60.…”

Section: Model Performancementioning

confidence: 99%

Forecasting Convective Storms Trajectory and Intensity by Neural Networks

Borghi,

Guariso,

Sangiorgio

2024

Forecasting

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Convective storms represent a dangerous atmospheric phenomenon, particularly for the heavy and concentrated precipitation they can trigger. Given their high velocity and variability, their prediction is challenging, though it is crucial to issue reliable alarms. The paper presents a neural network approach to forecast the convective cell trajectory and intensity, using, as an example, a region in northern Italy that is frequently hit by convective storms in spring and summer. The predictor input is constituted by radar-derived information about the center of gravity of the cell, its reflectivity (a proxy for the intensity of the precipitation), and the area affected by the storm. The essential characteristic of the proposed approach is that the neural network directly forecasts the evolution of the convective cell position and of the other features for the following hour at a 5-min temporal resolution without a relevant loss of accuracy in comparison to predictors trained for each specific variable at a particular time step. Besides its accuracy (R2 of the position is about 0.80 one hour in advance), this machine learning approach has clear advantages over the classical numerical weather predictors since it runs at orders of magnitude more rapidly, thus allowing for the implementation of a real-time early-warning system.

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Section: Model Performancementioning

confidence: 99%

Forecasting Convective Storms Trajectory and Intensity by Neural Networks

Borghi,

Guariso,

Sangiorgio

2024

Forecasting

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“…For example, let's consider a research project focused on analyzing weather data. Python, combined with the matplotlib library, allows for the creation of visually striking temperature trend graphs that effectively convey seasonal variations over the years (Shouman, 2023). Additionally, Python's capabilities extend to generating sophisticated statistical graphics (Melitoshevich, 2023;Kok et al, 2020).…”

Section: Importance Of Data Visualizationmentioning

confidence: 99%

Analysing Influential Factors in Student Academic Achievement: Prediction Modelling and Insight

Ananna,

Nowreen,

Al Jahwari

et al. 2023

IJEMD-CSAI

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The fascination with understanding student academic performance has drawn widespread attention from various stakeholders, including parents, policymakers, and businesses. The 'Students Performance in Exams' dataset, available on platforms like Kaggle, stands as a treasure trove. It extends beyond test scores, encompassing diverse student attributes like ethnicity, gender, parental education, test preparation, and even lunch type. In our tech-driven age, predicting academic success has become a compelling pursuit. This study aims to delve deep into this dataset, utilizing data mining methods and robust classification algorithms like Logistic Regression and Random Forest in a Jupyter Notebook environment. Rigorous model training, testing, and fine-tuning strive for the utmost predictive accuracy. Data cleaning and preprocessing play a crucial role in establishing a reliable dataset for accurate predictions. Beyond numbers, the project emphasizes data visualization's impact, transforming raw data into comprehensible insights for effective communication. The Logistic Regression Model exhibits an impressive 87.6% accuracy, highlighting its potential in predicting academic performance. Moreover, the Random Forest Model excels with a remarkable 100% accuracy in forecasting student grades, showcasing its effectiveness in this domain.

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New Weather Forecasting Applications

Abstract: Most state-of-the-art approaches for weather and climate modelling are based on physics informed numerical models of the atmosphere. These approaches aim to model the non-linear dynamics 1 2

Cited by 2 publications

References 40 publications

Forecasting Convective Storms Trajectory and Intensity by Neural Networks

Forecasting Convective Storms Trajectory and Intensity by Neural Networks

Analysing Influential Factors in Student Academic Achievement: Prediction Modelling and Insight

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