Seamount age prediction machine learning model based on multiple geophysical observables: methods and applications in the Pacific Ocean

Bai, Yongliang; Rong, Yilin; Sun, Jihong; Chen, Leiming; Zhang, Diya; Yin, Zhengtong

doi:10.1007/s11001-021-09451-z

Cited by 3 publications

(2 citation statements)

References 52 publications

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“…The loss of some data information if a single model is used for prediction is inevitable. Sampling several prediction models for combination forecasting, such as introducing the improving grey model (Liu et al, 2021;Yang et al, 2022) and BP neural network model (Bai Y. L. et al, 2021;Hu, 2022;Liu and Zhou, 2022), which can improve the accuracy of the forecasting model, can also be considered.…”

Section: Research Prospectmentioning

confidence: 99%

Simulation, prediction and driving factor analysis of ecological risk in Savan District, Laos

Peng

Qin³

2023

Front. Environ. Sci.

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Introduction: In recent years, Laos, with its unique geographical advantages, has vigorously developed overseas trade and cultural tourism industry, which has promoted local economic development and introduced numerous ecological and environmental problems.Method: Therefore, this research took Savan District as the research area, built the landscape ecological risk assessment model to reveal the spatiotemporal evolution characteristics of Savan District’s ecological risk from 2000 to 2020. Then, the Geodetector model was utilized to explain the driving forces behind changes in the landscape’s ecological risk. Finally, the Markov-PLUS coupling model was used to simulate and predict the changes in the land use pattern and ecological risk in the Savan District in 2030.Results: The following results are presented. 1) The landscape types in Savan District are mainly cultivated land and forest land, with the lowest degree of landscape fragmentation and loss, and the highest degree of landscape loss in unused land. 2) From 2000 to 2020, the average ecological risk index of the whole study area was low and showed a decreasing trend yearly, with the area of low-risk areas accounting for more than 90%. 3) In 2030, the average ecological risk index of the entire Savan District and the two provinces will rise, meanwhile the low-risk areas in the midwest regions and high-risk areas in the northwest will increase.Discussion: This study can guide the future coordinated development of the social economy and ecological environment in Savan District, Laos, and has good reference significance for ecological construction in similar areas in Southeast Asia.

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Section: Research Prospectmentioning

confidence: 99%

Simulation, prediction and driving factor analysis of ecological risk in Savan District, Laos

Peng

Qin³

2023

Front. Environ. Sci.

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“…The input of the memory cell module is presumably x t at time t ; the output is h t ; the unit state is c t . Therefore, the formulas of the input, forget, output gates, the input transition (Bai et al, 2021), the unit state update, and the structure of the output memory block are controlled as follows:…”

Section: Lstm Neural Network Prediction Modelmentioning

confidence: 99%

Demand Forecast of Railway Transportation Logistics Supply Chain Based on Machine Learning Model

Wang,

Zhang,

Guo

2023

International Journal of Information Technologies and Systems Approach

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The deep learning method based on long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (Bi-LSTM) was constructed by researching the factors affecting railway transportation logistics. Moreover, a simulation study on Tianjin Station was conducted. The deep learning model suitable for the logistics demand forecasting of Tianjin Station was established, and the changing trend of logistics supply chain demand in Tianjin Station in the future was analyzed. Moreover, a strategy for railway construction and regional cooperation was proposed. In this study, three deep learning neural networks, namely LSTM, GRU, and Bi-LSTM, were used to construct a demand forecasting model for the logistics supply chain in Tianjin Station. Bi-LSTM, which has bidirectional storage performance and the highest prediction accuracy, is superior to the traditional neural network structure in terms of period and fluctuation.

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Prediction of ultrabasic rocks by support vector machine based on airborne magnetic and radioactivity data

Liu,

Xiong,

Yang

2024

Computers & Geosciences

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Seamount age prediction machine learning model based on multiple geophysical observables: methods and applications in the Pacific Ocean

Cited by 3 publications

References 52 publications

Simulation, prediction and driving factor analysis of ecological risk in Savan District, Laos

Simulation, prediction and driving factor analysis of ecological risk in Savan District, Laos

Demand Forecast of Railway Transportation Logistics Supply Chain Based on Machine Learning Model

Prediction of ultrabasic rocks by support vector machine based on airborne magnetic and radioactivity data

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