A Statistical Prediction Model for Sluice Seepage Based on MHHO-BiLSTM

Huang, Zihui; Gu, Chongshi; Peng, Jianhe; Wu, Yan; Gu, Hao; Shao, Chenfei; Zheng, Sen; Zhu, Mingyuan

doi:10.3390/w16020191

Cited by 2 publications

(2 citation statements)

References 31 publications

(34 reference statements)

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“…The LSTM network exhibits strong temporal dependencies that are capable of storing relevant information over arbitrary time intervals, thereby effectively extracting patterns from historical data. Moreover, it has demonstrated promising results in practical applications [44,45].…”

Section: Lstm Networkmentioning

confidence: 99%

Settlement Prediction for Concrete Face Rockfill Dams Considering Major Factor Mining Based on the HHO-VMD-LSTM-SVR Model

Zheng,

Ren,

et al. 2024

Water

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Some important discoveries have been revealed in some studies, including that the settlement of concrete face rockfill dams (CFRDs) may cause cracks in the concrete face slabs, which may lead to dam collapse. Therefore, deformation behavior prediction of CFRDs is a longstanding and emerging aspect of dam safety monitoring. This paper aims to propose a settlement prediction model for CFRDs combining the variational mode decomposition (VMD) algorithm, long short-term memory (LSTM) network, and support vector regression algorithm (SVR). Firstly, VMD is applied in the decomposition of dam settlement monitoring data to reduce its complexity. Furthermore, feature information on settlement time series is extracted. Secondly, the LSTM and SVR are optimized by the Harris hawks optimization (HHO) algorithm and modified least square (PLS) method to mine the major influencing factors and establish the prediction model with higher precision. Finally, the proposed model and other models are applied to predict the deformation behavior of the Yixing CFRD. Prediction results indicate that the proposed method possesses particular advantages over other models. The proposed VMD-LSTM-SVR model might help to evaluate the settlement trends and safety states of CFRDs.

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Section: Lstm Networkmentioning

confidence: 99%

Settlement Prediction for Concrete Face Rockfill Dams Considering Major Factor Mining Based on the HHO-VMD-LSTM-SVR Model

Zheng,

Ren,

et al. 2024

Water

Self Cite

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“…Huang Zihui et al [22] used multi-strategy improvements to solve the overfitting and long-term dependence problems of neural networks, which accelerated the convergence speed and, at the same time, showed higher prediction accuracy and robustness, proving the applicability and generalization of the model. Hossein Moayedi et al [23] investigated the fuzzy logic approach for predicting heat loads in residential buildings and proved the feasibility of the technique to predict heat loads.…”

Section: Introductionmentioning

confidence: 99%

Environmental Prediction Model of Solar Greenhouse Based on Improved Harris Hawks Optimization-CatBoost

Yang,

Ren,

Wang

et al. 2024

Sustainability

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Solar greenhouses provide a favorable climate environment for the production of counter-seasonal crops in northern China. The greenhouse environment is a key factor affecting crop growth, so accurate prediction of greenhouse environment changes helps to precisely regulate the crop growth environment and helps to promote the growth of fruits and vegetables. In this study, an environmental prediction model based on the combination of a gradient boosting tree and the Harris hawk optimization algorithm (IHHO-Catboost) is constructed, and in response to the problems of the HHO algorithm, such as the fact that the adjustment of the search process is not flexible enough, it cannot be targeted to carry out a stage search, and sometimes it will fall into the local optimum to make the algorithm’s search accuracy relatively poor, an algorithm based on the improved Harris hawk optimization (IHHO) algorithm-based parameter identification method is constructed. The model considers the internal and external environmental and regulatory factors affecting crop growth, which include indoor temperature and humidity, light intensity, carbon dioxide concentration, soil temperature and humidity, outdoor temperature and humidity, light intensity, carbon dioxide concentration, wind direction, wind speed, and opening and closing of upper and lower air openings of the cotton quilt, and is input into a prediction model with a time series for training and testing. The experimental results show that the MAE (mean absolute error) values of temperature, relative humidity, carbon dioxide concentration, and light intensity of the model are reduced to 49.8%, 35.3%, 72.7%, and 32.1%, respectively, compared with LSTM (Long Short-Term Memory), which is a significant decrease in error. It shows that the proposed multi-parameter prediction model for solar greenhouse environments presents an effective method for accurate prediction of environmental data in solar greenhouses. The model not only improves prediction accuracy but also reduces dependence on large data volumes, reduces computational costs, and improves the transparency and interpretability of the model. Through this approach, an effective tool for greenhouse agriculture is provided to help farmers optimize the use of resources, reduce waste, and improve crop yield and quality, ultimately leading to a more efficient and environmentally friendly agricultural production system.

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A Statistical Prediction Model for Sluice Seepage Based on MHHO-BiLSTM

Cited by 2 publications

References 31 publications

Settlement Prediction for Concrete Face Rockfill Dams Considering Major Factor Mining Based on the HHO-VMD-LSTM-SVR Model

Settlement Prediction for Concrete Face Rockfill Dams Considering Major Factor Mining Based on the HHO-VMD-LSTM-SVR Model

Environmental Prediction Model of Solar Greenhouse Based on Improved Harris Hawks Optimization-CatBoost

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