Forecasting direct economic losses of marine disasters in China based on a novel combined model

Zhao, Xin; Li, Hui; Ding, Lili; Wang, Wei; Xue, Yuemei

doi:10.1016/j.ijdrr.2020.101921

Cited by 10 publications

(5 citation statements)

References 32 publications

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“…The model has been used to investigate the CO 2 emissions in Asia-Pacific Economic Cooperation (APEC) member countries [29], to forecast route passenger demand in the air transport industry, to project natural gas consumption [30], to forecast municipal waste generation [31], to investigate biofuel production and consumption in top CO 2 emitting countries [32], to forecast electricity consumption [33][34] among many other applications in several fields. The model has found wide applications because of its simplicity, low data requirement and high prediction accuracy [35][36]. The grey system theory posits that a system whose information structure, functions and connections with its environment are partially known proposed a vehicle emission policy that seeks to limit fuel sulphur content to 50 ppm for both diesel and petrol 2 and also reduce the age of used vehicles imported to the region to 5 years for light vehicles and 10 years for heavy vehicles [12].…”

Section: A2mentioning

confidence: 99%

Grey Model Analysis of Vehicle Population, Road Transport Energy Consumption and Vehicular Emissions

C.N.¹,

Ekwe²,

Ishaq³

et al. 2023

Communications

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This study employs the grey models to explore Nigeria's road transport energy consumption, vehicle population and vehicular emissions. The vehicular emissions were evaluated using the European Environment Agency Tier 1 Approach, based on the fuel consumption. A baseline scenario, based on historical data and three other alternative scenarios were developed. The study considered fuel quality, vehicle technology and survival rates as the key drivers of scenario formulation. Results show that vehicle population increases by about 3.58 % annually from 12.9 million units in 2018 to 38.5 million in 2050. Analysis of the alternative pathways reveals that their adoption would significantly reduce road transport energy consumption and air pollutant emissions.

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Section: A2mentioning

confidence: 99%

Grey Model Analysis of Vehicle Population, Road Transport Energy Consumption and Vehicular Emissions

C.N.¹,

Ekwe²,

Ishaq³

et al. 2023

Communications

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“…Wang et al proposed an echo state network (ESN) in electrical energy consumption forecasting [31]. Zhao et al also used the Elman neural network (ENN) in the boosting structure to predict direct economic losses of marine disasters in China [32]. Ameyaw and Yao employed the bidirectional long short-term memory (BILSTM) to predict the African CO2 Emissions affected by GDP [33].…”

Section: A Related Workmentioning

confidence: 99%

A New Multipredictor Ensemble Decision Framework Based on Deep Reinforcement Learning for Regional GDP Prediction

Yan

2022

IEEE Access

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Gross domestic product (GDP) can effectively reflect the situation of economic development and resource allocation in different regions. The high-precision GDP prediction technology lays a foundation for the sustainable development of regional resources and the proposal of economic management policies.To build an accurate GDP prediction model, this paper proposed a new multi-predictor ensemble decision framework based on deep reinforcement learning. Overall modeling consists of the following steps: Firstly, GRU, TCN, and DBN are the main predictors to train three GDP forecasting models with their characteristics. Then, the DQN algorithm effectively analyses the adaptability of these three neural networks to different GDP datasets to obtain an ensemble model. Finally, by adaptive optimization of the ensemble weight coefficients of these three neural networks, the DQN algorithm got the final GDP prediction results. Through three groups of experimental cases from China, the following conclusions can be drawn: (1) the DQN algorithm can obtain excellent experimental results in ensemble learning, which effectively improves the prediction performance of single predictors by more than 10 %. (2) The ensemble multi-predictor region GDP prediction framework based on deep reinforcement learning can achieve better prediction results than 18 benchmark models. In addition, the MAPE value of the proposed model is lower than 4.2% in all cases.

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“…Compared with the single model, the combined model of BP and SVM was proposed to better forecast the direct economic losses and the number of populations affected by storm surge. Zhao et al (2020) combined the results of ENN and GRNN with a definite integral to achieve interval forecasting, and obtained a large number of high-precision results of the annual storm surge disaster economic losses. At present, ensemble models are widely proposed to reduce bias and to improve forecasting accuracy (Liu and Xu, 2020;Bravo and Ayuso, 2021), but the application of this model in the field of marine disaster forecasting remains rare (Ding et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling of Direct Economic Losses of Storm Surge Disasters Based on a Novel Hybrid Forecasting System

2022

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Modeling the direct economic losses of storm surge disasters can assess the disaster situation in a timely manner and improve the efficiency of post-disaster management in practice, which is acknowledged as one of the most significant issues in clean production. However, improving the forecasting accuracy of direct economic losses caused by storm surge disasters remains challenging, which is also a major concern in the field of disaster risk management. In particular, most of the previous studies have mainly focused on individual models, which ignored the significance of reduction and optimization. Therefore, a novel direct economic loss forecasting system for storm surge disasters is proposed in this study, which includes reduction, forecasting, and evaluation modules. In this system, a forecasting module based on an improved machine learning technique is proposed, which improves the generalization ability and robustness of the system. In addition, the key attributes and samples are selected by the proposed reduction module to further improve the forecasting performance from the two innovative perspectives. Moreover, an evaluation module is incorporated to comprehensively evaluate the superiority of the developed forecasting system. Data on the storm surge disasters from three typical provinces are utilized to conduct a case study, and the performance of the proposed forecasting system is analyzed and compared with eight comparison models. The experimental results show that the mean absolute percentage error (MAPE) predicted by the Extreme Learning Machine (ELM) model was 16.5293%, and the MAPE predicted by the proposed system was 1.0313%. Overall, the results show that the performance of the proposed forecasting system is superior compared to other models, and it is suitable for the forecasting of direct economic losses resulting from storm surge disasters.

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Forecasting direct economic losses of marine disasters in China based on a novel combined model

Cited by 10 publications

References 32 publications

Grey Model Analysis of Vehicle Population, Road Transport Energy Consumption and Vehicular Emissions

Grey Model Analysis of Vehicle Population, Road Transport Energy Consumption and Vehicular Emissions

A New Multipredictor Ensemble Decision Framework Based on Deep Reinforcement Learning for Regional GDP Prediction

Modeling of Direct Economic Losses of Storm Surge Disasters Based on a Novel Hybrid Forecasting System

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