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A landslide inventory is a detailed register of the spatial distribution, geometry, and attributes of landslides and is essential for landslide hazard analysis, risk management, regional planning, and land use management and development, especially in landslide-prone regions. However, the development of a national landslide inventory is time-consum...
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... Various studies have emphasized the significance of incorporating landslide probability and mobility into hazard assessment and risk management strategies [37][38][39]. Landslide density is indicative of both the likelihood of landslide occurrence and the spatial distribution of landslide hazards. The size and mobility of landslides are crucial factors in determining the extent of the hazard area, making them important considerations in landslide hazard zonation mapping and mitigation strategies [51][52][53][54]. ...
Over the past three decades, Sri Lanka has observed a substantial rise in landslide occurrences linked to intensified rainfall. However, the lack of comprehensive landslide inventories has hampered the development of effective risk analysis and simulation systems, requiring Sri Lanka to rely heavily on foreign-developed models, despite the difficulty of fully examining the similarities between the characteristics of landslides in Sri Lanka and the areas where the model has been developed. Satellite images have become readily available in recent years and have provided information about the Earth’s surface conditions over the past few decades. Thus, this study verifies the utility of satellite images as a cost-effective remote-sensing method to clarify the commonalities and differences in the characteristics of landslides in two regions Ikawa, Japan, and Sabaragamuwa, Sri Lanka, which exhibit different geological formations despite similar annual rainfall. Using Google Earth satellite images from 2013 to 2023, we evaluated land-slide density, types, and geometry. The findings reveal that Ikawa exhibits a higher landslide density and experiences multiple-type landslides. In contrast, both areas have similar initiation areas; however, Sabaragamuwa predominantly experiences single landslides that are widespread and mobile. The findings also reveal that various characteristics of landslides are mainly influenced by varied topography. Here, we confirmed that even in areas where comprehensive information on landslides is conventionally lacking, we can understand the characteristics of landslides by comparing landslide geometry between sites using satellite imagery.
... The AI decision-support system utilizes optimization methods to determine the most economical inventory rules tailored to specific firm objectives and constraints. The model utilizes mathematical optimization techniques to generate optimal solutions that address conflicting objectives and compromises, such as reducing carrying costs, preventing stockouts, and optimizing order quantities [6]- [10]. The system may adjust to evolving market conditions and growing business needs through ongoing analysis of data feedback and enhancing its suggestions, resulting in long-term performance enhancement. ...
This study introduces an innovative artificial intelligence-driven comprehensive model designed for a decision-support system in inventory optimization. This model was developed using advanced deep learning techniques. The model aims to surpass the constraints of typical inventory management systems by effectively capturing temporal correlations and nuanced patterns in the data. This is achieved by integrating Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures. To navigate modern supply chains effectively, the primary focus should be on offering real-time decision support. The approach aims to enhance the decision-making processes in inventory management. This brief introduction offers insight into a distinctive method aimed at addressing the challenges of modern supply networks. The strategy focuses on providing realtime decision support and optimization strategies to enhance inventory management efficiency. The model's effectiveness is demonstrated by thorough performance evaluations, showing higher accuracy, precision, and recall compared to existing logistic regression models. The suggested LSTM model achieves an accuracy of 0.99, precision of 0.96, recall of 0.94, and an F1 Score of 0.97, surpassing existing models with higher scores in all metrics. The GRU model, albeit somewhat less effective than the LSTM, shows great overall performance, especially in recall with a score of 0.97. This investigation compares the effectiveness of different models, focusing on the potential advancements made possible by LSTM and GRU architectures in improving decision-support systems for inventory optimization. The AI-Based Holistic Model provided is at the forefront of technology advancements, offering a comprehensive solution for making educated decisions and optimizing inventories sustainably in today's highly competitive corporate climate.
... In his article, he developed a web application for the efficient development of a slippage inventory in an economical way. [17][18][19] Based on Ref. 20 their multi-item inventory model thakes into account warehouse capacity constraints, demand uncertainty, and lead times. ...
In today’s society, the amount of data available in many area has made it more important than ever to use effective technological tools for accurate and clear analysis and presentation of information. Therefore, the main objective of the research is to implement a Web/mobile system to improve inventory management in a private company. It has been developed under an agile extreme programming XP methodology, taking into account the following phases, which are carried out four structural activities planning, design, coding and testing. In addition, the following technologies were chosen: such as with programming languages PHP, with markup language PHP, MySql v. 10.0 as database system MySql v. 10.0, and cascading style sheets CSS for visual customisation. Therefore, the results showl a 13% improvement in the efficiency of the percentage of spools to be located (PSL, a 28% a better control of the index of percentage of inventoried coils (IC) and an 1,1% difference in efficiency of the Productivity per user (PU) index. With the implementation of a web-based web/mobile inventory management system to improve inventory control and mobile to better control of a company inventory management, using as a reference the most agile methodology to diversify the cases and functionalities contained in the system.
The relevance. Landslides are one of the most hazardous natural disasters in the world. In Vietnam, landslides frequently caused considerable losses of life and property. The systematic evaluation of landslide hazards is a crucial component of Vietnam's disaster prevention strategy.
The main aim of this study is to assess the landslide susceptibility of the Batxat district, Laocai province (Vietnam), using four digital elevation model resolutions (10, 30, 40, and 60 m) and nine landslide causative factors: slope aspect, elevation, slope, distance to roads, distance to faults, distance to drainage, average monthly precipitation, land use, and weathering crust.
Object of the study is the landslide susceptibility in Batxat district, Laocai province, Vietnam.
The quality of the available input data plays an important role in the reliability of the results of modeling the susceptibility of territories to the landslide process. Maps of slope aspect, slope, horizontal and relative relief, etc. are prepared based on information about the terrain, which is one of the most important parameters for conducting the analysis. The quality of a digital elevation model can be assessed based on its spatial resolution.
Methods: Certainty Factor method, fractal method.
Results. The performed analyses indicated a statistically significant relationship between the distribution of landslides and the landslide causative factors in the study area. As a result, the territory was divided into five zones according to its susceptibility to the landslide process: very low, low, moderate, high, and very high. The very low landslide susceptibility zone is less than 10% of the study area, while the very high susceptibility zone varies from 14,95 to 18,32%, depending on the digital elevation model spatial resolution. Analysis of the Receiver Operating Characteristics curve revealed that all models, independent of digital elevation model resolution, have good prediction efficiency, with the area under the receiver operating characteristic curve over 70%. In addition, the Receiver operating characteristic and fractal analysis results indicated that the model with a digital elevation model spatial resolution of 60m has the highest accuracy. This significant conclusion may be explained by the fact that the accuracy of the landslide susceptibility assessment result is dependent not only on the digital elevation model resolution but also on the ratio of the digital elevation model resolution and the average area of landslides in the study area. This conclusion proves that it is necessary to depict landslide locations and their detailed morphological characteristics on the landslide inventory maps.
Актуальность. Оползни являются одним из наиболее опасных стихийных бедствий в мире. Во Вьетнаме оползни также приводили к значительным человеческим жертвам и материальным потерям. Системная оценка оползневой опасности является важнейшим компонентом стратегии Вьетнама по предотвращению стихийных бедствий.
Целью исследования являлась оценка оползневой восприимчивости района Батсат провинции Лаокай (Вьетнам) с использованием четырех разрешений цифровой модели рельефа (10, 30, 40 и 60 м) и девяти факторов оползнеобразования: экспозиция склонов, высота над уровнем моря, крутизна склонов, расстояние до дорог, расстояние от разломов, расстояние до эрозионной сети, среднемесячное количество осадков, землепользование и тип коры выветривания.
Объектом исследования является оползневая восприимчивость в районе Батсат провинции Лаокай Вьетнама.
Качество имеющихся исходных данных играет важную роль в достоверности результатов моделирования оползневой восприимчивости территорий к оползневому процессу. Одним из наиболее значимых параметров для выполнения анализа является информация о рельефе местности, на основе которой строятся карты экспозиции и крутизны склонов, горизонтальной и вертикальной расчлененности рельефа и т. д. Качество цифровой модели рельефа может быть оценено на основе ее пространственного разрешения.
Методы: метод фактора достоверности, фрактальный метод.
Результаты. Выполненные исследования показали статистически значимую взаимосвязь между распределением оползней и факторами оползнеобразования в районе изучения. В результате территория была разделена на пять зон по восприимчивости к оползневому процессу: очень низкая, низкая, средняя, высокая и очень высокая. Зона с очень низкой оползневой восприимчивости составляет менее 10 % района исследования, в то время как зона с очень высокой восприимчивостью изменяется от 14,95 до 18,32 % в зависимости от пространственного разрешения цифровой модели рельефа. Анализ кривой ошибок показал, что все модели, вне зависимости от разрешения цифровой модели рельефа обладают хорошей эффективностью прогнозирования, площадь под кривой ошибок – AUC > 70%. Одновременно на основе результатов анализа с использованием метода кривой ошибок и фрактального метода было получено, что модель с пространственным разрешением 60 м имеет наибольшую достоверность. Этот нетривиальный вывод может быть объяснен тем фактом, что надежность результатов оценки оползневой восприимчивости зависит не столько от разрешения цифровой модели рельефа, сколько от соотношения разрешения цифровой модели рельефа и средней площади оползней в районе исследования. Этот вывод доказывает необходимость отображать на картах инвентаризации оползней не только их местоположения, но и подробные морфологические характеристики.
