Novel hybrid models by coupling support vector regression (SVR) with meta-heuristic algorithms (WOA and GWO) for flood susceptibility mapping

Rezaie, Fatemeh; Panahi, Mahdi; Bateni, Sayed M.; Jun, Changhyun; Neale, Christopher M. U.; Lee, Saro

doi:10.1007/s11069-022-05424-6

Cited by 16 publications

(3 citation statements)

References 103 publications

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“…Considering the advantages of the LSTM unit in processing time series data, unidirectional and bidirectional LSTM are used to predict the sales volume of typical auto parts. Furthermore, since support vector regression (SVR) and tree-based models such as gradient boosted decision tree (GBRT) generally have good performance in solving nonlinear problems [24][25][26], they are also considered.…”

Section: Resultsmentioning

confidence: 99%

Sales Volume Forecast of Typical Auto Parts Based on BiGRU: A Case Study

Shang

et al. 2023

E3S Web Conf.

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Inventory management is an important part of the auto parts supplier business. Accurate prediction of sales volume for different auto parts is the basis for staff to formulate marketing strategies and procurement plans. Based on the limited historical sales data of the South China, North China and East China branches of an auto parts company, some prediction models are trained and tested to determine the best model for predicting future production sales. An orthogonal experimental method is used to implement hyperparameter estimation for the prediction models on this basis. In addition, a posteriori test is used to verify the validity and accuracy of the Bi-GRU model in predicting the sales volume of typical auto parts. The results show that, compared with other models, the bidirectional gated recurrent unit (Bi-GRU) model has the highest accuracy in testing and is used to predict the future sales of typical auto parts. The posterior test proved that the validity and accuracy of the Bi-GRU model is verified. The orthogonal experiment method can effectively realize the hyperparameter estimation for each model. According to the prediction results, the sales volume of blind drive caps in South China, North China and East China will reach 18235, 17030 and 14949 pieces, respectively, after 90 days. Meanwhile, the corresponding sales volume of bolts will reach 13141, 15062 and 10253 pieces, respectively.

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Section: Resultsmentioning

confidence: 99%

Sales Volume Forecast of Typical Auto Parts Based on BiGRU: A Case Study

Shang

et al. 2023

E3S Web Conf.

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“…Various ML models thus emerge as most well‐performing in different case applications and recent studies have found that integration of several ML models can boost the modeling performance compared to that of standalone models (e.g., Bui et al., 2018; Nguyen, 2022; Pham et al., 2020; Rezaie et al., 2022). For example, Hong, Panahi, et al.…”

Section: Introductionmentioning

confidence: 99%

“…Various ML models thus emerge as most well-performing in different case applications and recent studies have found that integration of several ML models can boost the modeling performance compared to that of standalone models (e.g., Bui et al, 2018;Nguyen, 2022;Pham et al, 2020;Rezaie et al, 2022). For example, Hong, Panahi, et al (2018) found that the standalone Adaptive Neuro-Fuzzy Inference System (ANFIS) model was outperformed by ANFIS integrated with Genetic Algorithm and Differential Evolution metaheuristic algorithms for spatial flood modeling in China.…”

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confidence: 99%

A Country Wide Evaluation of Sweden's Spatial Flood Modeling With Optimized Convolutional Neural Network Algorithms

Panahi,

Khosravi,

Rezaie

et al. 2023

Earth's Future

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Flooding is one of the most serious and frequent natural hazards affecting human life, property, and the environment. This study develops and tests a deep learning approach for large‐scale spatial flood modeling, using Convolutional Neural Network (CNN) and optimized versions combined with the Gray Wolf Optimizer (GWO) or the Imperialist Competitive Algorithm (ICA). With Sweden as an application case for nation‐wide flood susceptibility mapping, this modeling approach considers ten geo‐environmental input factors (slope, elevation, aspect, plan curvature, length of slope, topographic wetness index, distance from river, distance from wetland, rainfall, and land use). The GWO and ICA optimization improves model prediction by 12% and 8%, respectively, compared with the standalone CNN model performance. The results show 40% of the land area, 45% of the railroad, and 43% of the road network of Sweden to have high or very high flood susceptibility. They also show the aspect to have the highest input factor impact on flood susceptibility prediction while, for example, rainfall ranks only seven of the total 10 considered geo‐environmental input factors. In general, accurate nation‐wide flood susceptibility prediction is essential for guiding flood management and mitigation efforts. This study's approach to such prediction has emerged as well‐performing and cost‐effective for the case of Sweden, calling for further application and testing in other world regions.

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