Predicting construction accidents on sites: An improved atomic search optimization algorithm approach

Liu, Chaoqiong; Li, Li; Qiang, Yue; Zhang, Shixin

doi:10.1002/eng2.12773

Cited by 2 publications

(1 citation statement)

References 31 publications

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“…Meanwhile, it is our belief that the BP neural network improved based on tent mapping and ASO atom optimization has universality. Previous studies have applied the Tent_ASO_BP algorithm in indoor positioning for smartphones [80], lithium battery health prediction [81], and construction safety management in the construction industry [82], all achieving favorable results. In the future, more optimization algorithms can be further improved and applied to research in the direction of forest biomass.…”

Section: The Performances Of Different Prediction Models With Differe...mentioning

confidence: 99%

Aboveground Forest Biomass Estimation Using Tent Mapping Atom Search Optimized Backpropagation Neural Network with Landsat 8 and Sentinel-1A Data

Chen,

Sun,

Zhang

et al. 2023

Remote Sensing

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Accurate forest biomass estimation serves as the foundation of forest management and holds critical significance for a comprehensive understanding of forest carbon storage and balance. This study aimed to integrate Landsat 8 OLI and Sentinel-1A SAR satellite image data and selected a portion of the Shanxia Experimental Forest in Jiangxi Province as the study area to establish a biomass estimation model by screening influencing factors. Firstly, we extracted spectral information, vegetation indices, principal component features, and texture features within 3 × 3-pixel neighborhoods from Landsat 8 OLI. Moreover, we incorporated Sentinel-1’s VV (vertical transmit–vertical receive) and VH (vertical transmit–horizontal receive) polarizations. We proposed an ensemble AGB (aboveground biomass) model based on a neural network. In addition to the neural network model, namely the tent mapping atom search optimized BP neural network (Tent_ASO_BP) model, partial least squares regression (PLSR), support vector machine (SVR), and random forest (RF) regression prediction techniques were also employed to establish the relationship between multisource remote sensing data and forest biomass. Optical variables (Landsat 8 OLI), SAR variables (Sentinel-1A), and their combinations were input into the four prediction models. The results indicate that Tent_ ASO_ BP model can better estimate forest biomass. Compared to pure optical or single microwave data, the Tent_ASO_BP model with the optimal combination of optical and microwave input features achieved the highest accuracy. Its R2 was 0.74, root mean square error (RMSE) was 11.54 Mg/ha, and mean absolute error (MAE) was 9.06 Mg/ha. Following this, the RF model (R2 = 0.54, RMSE = 21.33 Mg/ha, MAE = 17.35 Mg/ha), SVR (R2 = 0.52, RMSE = 17.66 Mg/ha, MAE = 15.11 Mg/ha), and PLSR (R2 = 0.50, RMSE = 16.52 Mg/ha, MAE = 12.15 Mg/ha) models were employed. In conclusion, the BP neural network model improved by tent mapping atom search optimization algorithm significantly enhanced the accuracy of AGB estimation in biomass studies. This will provide a new avenue for large-scale forest resource surveys.

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Section: The Performances Of Different Prediction Models With Differe...mentioning

confidence: 99%

Aboveground Forest Biomass Estimation Using Tent Mapping Atom Search Optimized Backpropagation Neural Network with Landsat 8 and Sentinel-1A Data

Chen,

Sun,

Zhang

et al. 2023

Remote Sensing

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An improved Harris hawks optimizer with enhanced logarithmic spiral and dynamic factor and its application for predicting molten iron temperature in the blast furnace

Liu,

Fang,

Liu

et al. 2024

Engineering Reports

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In response to the problem of poor search performance and difficulty in escaping from the local optimum in the Harris hawks optimizer, an improved Harris hawks optimizer with enhanced logarithmic spiral and dynamic factor (IHHO‐ELSDF) is proposed in this paper. The enhanced logarithmic spiral mechanism is adopted in the exploration phase, and its main feature is the use of an improved opposite‐learning hybrid logarithmic spiral mechanism to search for more promising regions. The dynamic factor is used to replace the escaping energy to improve the global search capability of the algorithm, and it can better balance exploration and exploitation. In addition, a random distribution strategy is proposed for the exploitation phase to avoid falling into the local optimum. Based on 23 classical test functions, the influence of the distribution probability, the three improved mechanisms, and the exploration–exploitation ratio in IHHO‐ELSDF are analyzed. Subsequently, IHHO‐ELSDF is subjected to a comparative analysis with 17 algorithms on the IEEE CEC2022 benchmark suite. These tests show that IHHO‐ELSDF outperforms most competitors in numerical optimization. Furthermore, to assess its applicability in real‐world problems, IHHO‐ELSDF is employed to optimize parameters in the wavelet neural network used for molten iron temperature prediction. The simulation results based on real production data show that the proposed prediction model achieves a high prediction precision with , , and .

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Predicting construction accidents on sites: An improved atomic search optimization algorithm approach

Cited by 2 publications

References 31 publications

Aboveground Forest Biomass Estimation Using Tent Mapping Atom Search Optimized Backpropagation Neural Network with Landsat 8 and Sentinel-1A Data

Aboveground Forest Biomass Estimation Using Tent Mapping Atom Search Optimized Backpropagation Neural Network with Landsat 8 and Sentinel-1A Data

An improved Harris hawks optimizer with enhanced logarithmic spiral and dynamic factor and its application for predicting molten iron temperature in the blast furnace

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