Forecast of flood disaster emergency material demand based on IACO-BP algorithm

Chen, Fujiang; Chen, Junying; Liu, Jingang

doi:10.1007/s00521-021-05883-1

Cited by 12 publications

(10 citation statements)

References 14 publications

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“…Pheromone is significant in the process of finding solutions in the ant colony algorithm. e pheromone volatilization coefficient of the ant colony algorithm is constant, however, the volatilization speed is different in the early stage and the later stage [38][39][40][41]. Hence, this paper adopts the pheromone adaptive volatilization parameter to replace the fixed value.…”

Section: Iaco-bp Algorithmmentioning

confidence: 99%

Artificial Intelligence Evaluation for Mathematics Teaching in Colleges under the Guidance of Wireless Network

Chen

2022

Mobile Information Systems

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Comprehensively improving the quality of teaching is the highlight of current higher education. Also, it meets the basic needs of China’s current transformation into a country for strong education. Mathematics is one of the most important subjects in education these days, which is widely applied in research, social life, and understanding various science theories and laws. Hence, improving the quality of teaching mathematics in colleges becomes an important task in modern college teaching. In particular, the wireless integration of information has enriched and diversified the teaching methods of higher education. Vigorously promoting the application of wireless network technology in mathematics teaching is not only an effective way to solve the insufficient supply of educational resources but also a bold attempt to innovate the mathematics teaching mode in colleges and universities. Firstly, this work proposes an IACO-BP (improved ant colony optimization-based backpropagation) network to evaluate teaching quality for higher level mathematics in a wireless network environment. It improves the traditional ant colony optimization algorithm from three aspects, namely ant colony pheromone adaptive volatility coefficient, pheromone iterative elite selection strategy, and population iteration, adding a variation factor to construct IACO. Then, we use IACO to optimize initial weight and threshold to solve the issue of BP network’s falling into local optimum and improve network performance. Secondly, this work puts forward a series of countermeasures for the construction of mathematics teaching system in colleges. The correctness and superiority of the proposed strategy are verified by comparing the mathematics teaching quality in colleges before and after using the proposed strategies. Various aspects of mathematics teaching, such as teaching objectives, teaching content, and teaching process, are evaluated before and after using these strategies. These features are improved by a significant margin, up to 2.6%, after applying the proposed strategies.

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Section: Iaco-bp Algorithmmentioning

confidence: 99%

Artificial Intelligence Evaluation for Mathematics Teaching in Colleges under the Guidance of Wireless Network

Chen

2022

Mobile Information Systems

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“…One of the main challenges regarding larger emergencies is to predict when they will occur, leading to a demand of emergency resources. Several studies have been done to improve the demand prediction of emergency resources or services, either on general level (Liu et al, 2012), or for certain types of resources and services, such as emergency medical services (Setzler et al, 2009;Steins et al, 2019;Viglino et al, 2017;Vile et al, 2012), relief material (Chen et al, 2022;Shao et al, 2021), and railway emergency resources (Sun et al, 2021). Specifically for natural hazards, Chen et al (2022) used optimization to predict the demand of flood emergency material in China to support more efficient delivery of the material to the locations in most need.…”

Section: Decision Support For Improved Preparedness and Responsementioning

confidence: 99%

“…Several studies have been done to improve the demand prediction of emergency resources or services, either on general level (Liu et al, 2012), or for certain types of resources and services, such as emergency medical services (Setzler et al, 2009;Steins et al, 2019;Viglino et al, 2017;Vile et al, 2012), relief material (Chen et al, 2022;Shao et al, 2021), and railway emergency resources (Sun et al, 2021). Specifically for natural hazards, Chen et al (2022) used optimization to predict the demand of flood emergency material in China to support more efficient delivery of the material to the locations in most need. Instead of improving the demand prediction, another approach is presented by Song et al (2018), who split the demand into one base and one surge part, and propose supply chain flexibility to cope with the demand uncertainty.…”

Section: Decision Support For Improved Preparedness and Responsementioning

confidence: 99%

Decision Support for Emergency Response to Multiple Natural Hazards : CHALLENGES AND NEEDS

Gustafsson

2022

Linköping Studies in Science and Technology. Licentiate Thesis

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Global warming exceeding 1.5°C above pre-industrial levels will very likely lead to unavoidable increases of several climate hazards in the coming decades. Climate change phenomena can increase the risk of several extreme weather events, leading to risks of what is commonly considered as natural hazards, for instance landslides and forest fires. These chains or networks of events are termed natural multi-hazards or compound natural events, referring to a primary event or a driver that directly triggers or increases the probability of one or more secondary events by changing the environment. The very likely increase of single and compound natural hazards due to climate change require increased attention since it may imply new challenges to emergency response systems and new threats to society. Especially, how emergency response systems can prepare for and respond to these hazards by using knowledge of the interactions between different natural events.

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“…Therefore, strengthening the city, especially megacities’ pluvial flood disaster management and emergency capacity building, has not only become a major practical need, but also has received a lot of attention from the academic community. For example, Zhang et al [ 4 ], Zhong et al [ 5 ] and Chen et al [ 6 ] conducted theoretical discussions on the demand, reserve and distribution of emergency supplies. Slobodan et al [ 7 ] and Liu et al [ 8 ] conducted necessary research on public flood risk perception and emergency evacuation.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Emergency Response Capacity of Urban Pluvial Flooding Public Service Based on Scenario Simulation

Zhang

Kong

et al. 2022

IJERPH

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The evaluation of emergency response capability under different pluvial flooding scenarios is an essential approach to improve the emergency response capability of flood disasters. A new evaluation method of emergency response capacity of urban public services is proposed based on urban pluvial flooding scenario simulation. Firstly, inundation area and depth under different pluvial flooding scenarios are simulated based on the SCS-CN model. Following that, space densities of all indicators include inundation area and depth, road network and the emergency public service institutions. Then, the indicator weight is determined by the combined weighting method of entropy weight and coefficient of variation. Finally, the emergency response capacity index (of each pixel) is calculated based on the graph stacking method. Taking Erqi District, Zhengzhou City as an example, the emergency response capacity of public service under different urban flooding scenarios is evaluated. The results show that the spatial distribution difference of public service emergency response capacity in Erqi District, Zhengzhou City is obvious, and with the increase of the precipitation return period, the high value area of public service emergency response capability decreases gradually and the low value area increases gradually. This method takes into account the specific urban flooding scenario and the layout of public service institutions and road networks that have strong practicability. the results of the evaluation can provide a reference for the construction of urban flood emergency response capacity and provide support for emergency decision-making.

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Forecast of flood disaster emergency material demand based on IACO-BP algorithm

Cited by 12 publications

References 14 publications

Artificial Intelligence Evaluation for Mathematics Teaching in Colleges under the Guidance of Wireless Network

Artificial Intelligence Evaluation for Mathematics Teaching in Colleges under the Guidance of Wireless Network

Decision Support for Emergency Response to Multiple Natural Hazards : CHALLENGES AND NEEDS

Evaluation of Emergency Response Capacity of Urban Pluvial Flooding Public Service Based on Scenario Simulation

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