Deep-Sea Trenches of the Pacific Ocean: A Comparative Analysis of the Submarine Geomorphology Using Data Modeling by Gmt, Python and R

Lemenkova, Polina

doi:10.2139/ssrn.3858289

Cited by 2 publications

(1 citation statement)

References 208 publications

(381 reference statements)

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“…That work emphasises the functionality of a high-level language such as Python and the applicability of machine learning algorithms to earth science. In this study, the Seaborn statistical package built on the Matplpotlib Python library was tested to analyse, model and visualise geospatial data using existing approaches [36]. Efficient and accurate graphing supported by Python demonstrates the undeniable benefits of machine learning in earth sciences.…”

Section: Remote Sensing Imagery Data and Preparation Of The Developme...mentioning

confidence: 99%

Application of Machine Learning and Neural Networks to Predict the Yield of Cereals, Legumes, Oilseeds and Forage Crops in Kazakhstan

et al. 2023

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The article provides an overview of the accuracy of various yield forecasting algorithms and offers a detailed explanation of the models and machine learning algorithms that are required for crop yield forecasting. A unified crop yield forecasting methodology is developed, which can be adjusted by adding new indicators and extensions. The proposed methodology is based on remote sensing data taken from free sources. Experiments were carried out on crops of cereals, legumes, oilseeds and forage crops in eastern Kazakhstan. Data on agricultural lands of the experimental farms were obtained using processed images from Sentinel-2 and Landsat-8 satellites (EO Browser) for the period of 2017–2022. In total, a dataset of 1600 indicators was collected with NDVI and MSAVI indices recorded at a frequency of once a week. Based on the results of this work, it is found that yields can be predicted from NDVI vegetation index data and meteorological data on average temperature, surface soil moisture and wind speed. A machine learning programming language can calculate the relationship between these indicators and build a neural network that predicts yield. The neural network produces predictions based on the constructed data weights, which are corrected using activation function algorithms. As a result of the research, the functions with the highest prediction accuracy during vegetative development for all crops presented in this paper are multi-layer perceptron, with a prediction accuracy of 66% to 99% (85% on average), and polynomial regression, with a prediction accuracy of 63% to 98% (82% on average). Thus, it is shown that the use of machine learning and neural networks for crop yield prediction has advantages over other mathematical modelling techniques. The use of machine learning (neural network) technologies makes it possible to predict crop yields on the basis of relevant data. The individual approach of machine learning to each crop allows for the determination of the optimal learning algorithms to obtain accurate predictions.

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Section: Remote Sensing Imagery Data and Preparation Of The Developme...mentioning

confidence: 99%

Application of Machine Learning and Neural Networks to Predict the Yield of Cereals, Legumes, Oilseeds and Forage Crops in Kazakhstan

et al. 2023

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Seismic Background Noise Level and Station Detectability in the Flores Sea

Khusnani,

Jufriansah,

Anggraini

et al. 2024

Glob. J. Earth Sci. Eng.

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The Flores back-arc thrust fissure is a significant contributor to earthquake events in the Flores Sea region, as evidenced by seismic investigations. As part of the endeavor to mitigate earthquake risk, seismic data investigations are necessary due to the high potential for earthquakes in the Flores Sea. Background noise in earthquakes is the term used to describe the micro vibrations that are perpetually produced as a result of natural phenomena, such as ocean waves, wind, or human activities. It is crucial to investigate this cacophony in seismology in order to distinguish the primary earthquake signal. Its spectrum analysis can assist in the identification of land changes and the prediction of tectonic activity. This analysis was conducted by employing the Incorporated Research Institutions for Seismology (IRIS) client function as a fetch data tool and the Modular Utility for Statistical Knowledge Gathering Data browser as a data quality monitoring system to verify the health and reliability of seismic data. The three station sites closest to the Flores Sea are the focus of this research data examination. The study's findings indicate that the recorded data at the station is still dominated by cultural noise, as evidenced by the analysis of the probability density function, power spectral density, and noise spectrograms. Additionally, each station exhibits activity with degrees of probability noise that are both high and variable. These results highlight the need for advanced techniques to filter cultural noise and improve the accuracy of seismic signal interpretation in this region. This analysis contributes to understanding tectonic activity in the Flores Sea and underscores the importance of continuous monitoring for earthquake preparedness and risk reduction.

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Deep-Sea Trenches of the Pacific Ocean: A Comparative Analysis of the Submarine Geomorphology Using Data Modeling by Gmt, Python and R

Cited by 2 publications

References 208 publications

Application of Machine Learning and Neural Networks to Predict the Yield of Cereals, Legumes, Oilseeds and Forage Crops in Kazakhstan

Application of Machine Learning and Neural Networks to Predict the Yield of Cereals, Legumes, Oilseeds and Forage Crops in Kazakhstan

Seismic Background Noise Level and Station Detectability in the Flores Sea

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