Application of ChatGPT in natural disaster prevention and reduction

Xue, Zhiwen; Xu, Chong; Xu, Xiwei

doi:10.1016/j.nhres.2023.07.005

Cited by 15 publications

(2 citation statements)

References 14 publications

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“…Before summarizing the studies encountering ChatGPT use in risk management, it is vital to highlight that the total number of studies focusing on ChatGPT use in the project management domain is in the minority. The limited studies outside of risk management generally concern the application of ChatGPT in natural disaster prevention and reduction [37,38] and the scheduling of construction projects [23].…”

Section: Literature Review On Chatgpt Use For Risk Management In Cons...mentioning

confidence: 99%

Assessing the Accuracy of ChatGPT Use for Risk Management in Construction Projects

Aladağ

2023

Sustainability

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Artificial Intelligence (AI) is considered promising digital technology that has important opportunities for enhancing project oversight and delivering improved decision-making in the risk management domain. However, there is a limited amount of research that has evaluated AI tools’ performance in risk management. Therefore, with the intention of sustaining more accurate risk-based decision-making process in the construction industry, this paper investigates the accuracy of ChatGPT in risk management for different project types. In this context, Key Performance Indicators (KPIs) related to each risk management sub-process were determined, and then a questionnaire that consisted of prompt templates was prepared for collecting data from ChatGPT. Afterwards, ChatGPT’s responses were evaluated by experts with focus group sessions. The findings indicate that ChatGPT has a moderate level of performance in managing risks. It provides more accurate knowledge in risk response and risk monitoring rather than risk identification and risk analysis sub-processes. This research paves the way for future studies by demonstrating an implication of ChatGPT use for risk-based decision making. In addition, gaining insight into the precision of ChatGPT in the risk-based decision-making process will empower decision-makers to establish resilience in business operations through technology-driven risk management.

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Section: Literature Review On Chatgpt Use For Risk Management In Cons...mentioning

confidence: 99%

Assessing the Accuracy of ChatGPT Use for Risk Management in Construction Projects

Aladağ

2023

Sustainability

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“…Landslides are globally significant and sudden geological disasters characterized by high frequency, widespread occurrence, and substantial destructiveness, with their most prominent feature being their potential to cause disaster. China stands out as one of the countries most severely affected by casualties resulting from landslide disasters [1][2][3][4][5]. According to the China Statistical Yearbook data, an average of 22,000 landslides occurred annually between 2000 and 2017, resulting in an average of 649 deaths per year.…”

Section: Introductionmentioning

confidence: 99%

Delineating Non-Susceptible Landslide Areas in China Based on Topographic Index and Quantile Non-Linear Model

Ma,

Shao,

2024

Forests

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Efficient analysis of non-susceptibility to landslides targets regions with minimal or zero landslide probability, thereby obviating the need to estimate the likelihood for low-susceptibility zones. This study assesses the effectiveness of the quantile non-linear (QNL) model in delineating the non-susceptibility of landslides in China through a topographic index. The topographic index encompassed slope angle and topographic relief, which are calculated using a 3 × 3 and 15 × 15 square cell moving window, respectively. Additionally, a global landslide susceptibility model established using a comprehensive global landslide database and fuzzy algorithm was employed for comparative analysis, providing a holistic evaluation of the QNL model’s accuracy. The results show that while the overall distribution of the two QNL models for non-susceptible landslide areas was roughly consistent, notable discrepancies were observed in localized regions, especially in the Southwest and Qinghai-Tibet geological environment areas where landslides are prone to occur. The applicability of the QNL model is significantly limited in these areas. In addition, the predicted results of the QNL_CHN model are closer to those based on the global landslide susceptibility model of the fuzzy algorithm. This study provides valuable insights to enhance the QNL model’s applicability, thereby strengthening forest ecosystem management and mitigating ecological disaster risks.

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