Research on SSA-LSTM-Based Slope Monitoring and Early Warning Model

Yang, Shasha; Jin, Anjie; Nie, Wen; Liu, Cong; Liu, Yu

doi:10.3390/su141610246

Cited by 17 publications

(7 citation statements)

References 42 publications

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“…Taking the macro-factors into account, the correct rates of the three models of logistic regression, the neural network, and random forest reached 79.2%, 74.03%, and 80.52%, respectively. That is, the division of the economic cycle in this paper and the consideration of economic cycle factors are shown to be effective in the process of establishing the model [30,31].…”

Section: Discussionmentioning

confidence: 91%

Sustainability of Shipping Logistics: A Warning Model

Liu

Meng

et al. 2023

Sustainability

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The shipping industry is the foundation of the economy, and it is affected by fluctuations in the economic cycle. The mainstream of financial early warning research is quantitative modeling research. There are few systematic studies on financial early warning of shipping enterprises, and most of them still remain in the qualitative stage. This paper chooses Chinese listed shipping companies as its target, takes the economic cycle as an important reference, and then uses logistic regression, neural network, and random-forest methods to establish a model for financial warning. The random-forest model is employed to rank the importance of warning indicators. The results show that it is effective to consider macro-factors, such as the economic cycle, and the predictive accuracy of the random-forest method is higher than that of the financial warning models established by logistic regression and by the neural network. Financial alerts can help managers prepare for crises in advance. The purpose of this paper is to provide an early warning model for the sustainable development of shipping logistics.

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Section: Discussionmentioning

confidence: 91%

Sustainability of Shipping Logistics: A Warning Model

Liu

Meng

et al. 2023

Sustainability

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“…The prediction of landslide movement in the major branches of the Three Gorges Dam area in China is carried out through combined models such as LSTM-TAR VMDstacked, LSTM-FC models, and LSTM models combined with Weighted Moving Average (WMA) using rainfall and reservoir water level data in each cycle with high accuracy [50], [51]. A prediction model for slope displacement based on the LSTM neural network and the Singular Spectrum Analysis (SSA) algorithm, using survey and geotechnical monitoring data, has significantly improved the model's performance in the dataset for predicting displacement within the next 24 hours [52]. Landslides are likely to occur more frequently, along with climate change and the increasing surface loads caused by human activities.…”

Section: Related Workmentioning

confidence: 99%

Predictive Modeling of Landslide Susceptibility in Soft Soil Canal Regions: A Focus on Early Warning Systems

Anh,

Danh,

Nguyen

2023

IJACSA

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The Mekong Delta (MD) has suffered significant losses in land resources, economic damage, and human and property casualties due to recent landslides. An early warning system for landslides is a valuable tool for identifying the effectiveness and timely detection of changes in the soil to promptly determine solutions and minimize damage caused by landslides in an area. In this study, we apply a machine learning approach based on the Long Short-Term Memory (LSTM) algorithm to experiment with early warning of landslide events on soft soil in the MD. Horizontal pressure, the change in inclination angles of the sensor pile due to the soil mass sliding in both the x and y directions, and the warning levels are determined based on the deformation and displacement of the soil along the riverbank, considered candidate factors for inputs in the model. Data from the established sensor system is used to train the model, creating a training and testing dataset of 374,415 samples. The accuracy of the detection and classification threshold of the system is proposed to be measured using the average F1 score derived from precision and recall values. The optimal prediction results are gleaned from an observational window of 4 minutes and 30 seconds to project roughly 2 hours into the future. The validation process resulted in recall, precision, and F1-score stands at 0.8232 with a remarkably low standard deviation of about 1%. The successful application of this research can help identify abnormal events leading to riverbank landslides due to loading, thereby creating conditions for developing a reliable information system to provide managers with the ability to suggest timely solutions to protect the lives, property of residents and infrastructures.

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“…Among many commonly used SIAs, the Sparrow Search Algorithm (SSA) 24 is known for its high search accuracy, fast convergence speed, good stability, and robustness. Additionally, SSA requires fewer model parameters, making it a relatively simple algorithm with better global optimization capabilities in complex problem-solving environments 25 . However, original SSA also has some drawbacks, such as poor uniformity and predictability in the initialization of individuals, lack of step control and individual mutation mechanisms 26 .…”

Section: 、 Introductionmentioning

confidence: 99%

Salinity prediction in Qiantang Estuary based on Improved LSTM model

Zheng,

Sun,

Jiao

et al. 2024

Preprint

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Investigating saltwater intrusion is vital for optimal use of estuarine water resources. Presently, diverse data-driven models, mainly neural network models, have been employed to predict tidal estuarine salinity. However, the high nonlinearity, randomness, and instability of salinity sequences pose challenges for accurate estuarine salinity forecasting. In this paper, a multi-factor salinity prediction model using an enhanced Long Short-Term Memory (LSTM) network was proposed, based on measured data from Cangqian and Qibao stations in the Qiantang Estuary during 2011-2012. To improve prediction accuracy, input variables of the model were determined through Grey Relational Analysis (GRA) combined with estuarine dynamic analysis, and hyperparameters were optimized using a multi-strategy Improved Sparrow Search Algorithm (ISSA). Additionally, the model was applied to forecast salinity under different runoff conditions, analyzing the sensitivity of salinity to upstream discharge. Experimental result shows that compared to other models (BP, GRU, LSTM, SSA-LSTM), the new proposed ISSA-LSTM model has smaller errors and higher prediction accuracy, with NSE improving over 5% and other metrics (MAP, MAPE, RMSE) improving over 10%. Thus, the model provides a practical solution for the rapid and precise prediction of estuarine salinity.

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Research on SSA-LSTM-Based Slope Monitoring and Early Warning Model

Cited by 17 publications

References 42 publications

Sustainability of Shipping Logistics: A Warning Model

Sustainability of Shipping Logistics: A Warning Model

Predictive Modeling of Landslide Susceptibility in Soft Soil Canal Regions: A Focus on Early Warning Systems

Salinity prediction in Qiantang Estuary based on Improved LSTM model

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