Construction Project Cost Prediction Method Based on Improved BiLSTM

Wang, Chaoxue; Qiao, Jiale

doi:10.3390/app14030978

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…A value closer to 0 means that more probability should be discarded. Otherwise, a value closer to 1 means that more probability should be retained in the new state [25][26][27].…”

Section: Long Short-term Memory Network (Lstm)mentioning

confidence: 99%

MVHS-LSTM: The Comprehensive Traffic Flow Prediction Based on Improved LSTM via Multiple Variables Heuristic Selection

Guo,

Zhu,

Wang

2024

Applied Sciences

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In recent years, the rapid growth of vehicles has imposed a significant burden on urban road resources. To alleviate urban traffic congestion in intelligent transportation systems (ITS), real-time and accurate traffic flow prediction has emerged as an effective approach. However, selecting relevant parameters from traffic flow information and adjusting hyperparameters in intelligent algorithms to achieve high prediction accuracy is a time-consuming process, posing practical challenges in dynamically changing traffic conditions. To address these challenges, this paper introduces a novel prediction architecture called Multiple Variables Heuristic Selection Long Short-Term Memory (MVHS-LSTM). The key innovation lies in its ability to select informative parameters, eliminating unnecessary factors to reduce computational costs while achieving a balance between prediction performance and computing efficiency. The MVHS-LSTM model employs the Ordinary Least Squares (OLS) method to intelligently reduce factors and optimize cost efficiency. Additionally, it dynamically selects hyperparameters through a heuristic iteration process involving epoch, learning rate, and window length, ensuring adaptability and improved accuracy. Extensive simulations were conducted using real traffic flow data from Shanghai to evaluate the enhanced performance of MVHS-LSTM. The prediction results were compared with those of the ARIMA, SVM, and PSO-LSTM models, demonstrating the innovative capabilities and advantages of the proposed model.

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“…A value closer to 0 means that more probability should be discarded. Otherwise, a value closer to 1 means that more probability should be retained in the new state [25][26][27].…”

Section: Long Short-term Memory Network (Lstm)mentioning

confidence: 99%

MVHS-LSTM: The Comprehensive Traffic Flow Prediction Based on Improved LSTM via Multiple Variables Heuristic Selection

Guo,

Zhu,

Wang

2024

Applied Sciences

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Foreign Exchange Forecasting Models: LSTM and BiLSTM Comparison

García,

Guijarro,

Oliver

et al. 2024

Itise 2024

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Cost Estimation and Prediction for Residential Projects Based on Grey Relational Analysis–Lasso Regression–Backpropagation Neural Network

Chen,

Wang

2024

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In the early stages of residential project investment, accurately estimating the engineering costs of residential projects is crucial for cost control and management of the project. However, the current cost estimation of residential engineering in China is primarily carried out by cost personnel based on their own experience. This process is time-consuming and labour-intensive, and it involves subjective judgement, which can lead to significant estimation errors and fail to meet the rapidly developing market demands. Data collection for residential construction projects is challenging, with small sample sizes, numerous attributes, and complexity. This paper adopts a hybrid method combining a grey relational analysis, Lasso regression, and Backpropagation Neural Network (GAR-LASSO-BPNN). This method has significant advantages in handling high-dimensional small samples and multiple correlated variables. The grey relational analysis (GRA) is used to quantitatively identify cost-driving factors, and 14 highly correlated factors are selected as input variables. Then, regularization through Lasso regression (LASSO) is used to filter the final input variables, which are subsequently input into the Backpropagation Neural Network (BPNN) to establish the relationship between the unit cost of residential projects and 12 input variables. Compared to using LASSO and BPNN methods individually, the GAR-LASSO-BPNN hybrid prediction method performs better in terms of error evaluation metrics. The research findings can provide quantitative decision support for cost estimators in the early estimation stages of residential project investment decision-making.

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Construction Project Cost Prediction Method Based on Improved BiLSTM

Cited by 3 publications

References 33 publications

MVHS-LSTM: The Comprehensive Traffic Flow Prediction Based on Improved LSTM via Multiple Variables Heuristic Selection

MVHS-LSTM: The Comprehensive Traffic Flow Prediction Based on Improved LSTM via Multiple Variables Heuristic Selection

Foreign Exchange Forecasting Models: LSTM and BiLSTM Comparison

Cost Estimation and Prediction for Residential Projects Based on Grey Relational Analysis–Lasso Regression–Backpropagation Neural Network

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