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• An automated flood image detection system is presented. • Proposed flood detection system is well-suited for urban applications. • The model rapidly and reliably identifies the flooding extent and location. • Deep learning techniques considerably improve the model performance by data enhancement. Intensified climate change in recent years has had a global impact, leading to increased precipitation events of short duration and high intensity. This phenomenon poses a severe challenge to urban and underground infrastructure. Accurate detection and location of floodplains and water bodies are essential to ensure informed decision-making and implement proactive measures to minimize risks and losses. Therefore, an automated and efficient flood image detection system is the need of the hour. This study proposes a deep learning–based flood detection system. Images taken by surveillance cameras at intersections are used as input data, making the system well-suited to urban applications. Data augmentation techniques are used to improve the model performance. We demonstrated the practicality of this model by applying it to street surveillance images taken in Taiwan. The developed model quickly and successfully identified the extent and location of flooding with high precision and reliability. The developed model can be used to provide valuable insights for flood management and disaster management agencies. The test findings obtained from this study demonstrate the superior performance of the DeepLabv3 + model compared to the Mask R-convolutional neural network model, which was further enhanced using a super-resolution generative adversarial network. The model achieved remarkable precision with a precision metric score of 84 %, which is also complemented by a recall rate of 91 %. Most notably, the mean Intersection over Union (mIoU) metric reached an impressive accuracy level of 85.8 %. The results of this study highlight the importance of developing advanced flood imagery detection models aiming to considerably reduce the risks and losses incurred by flooding. The application of such a flood image detection system could help increase the ability of a city to cope with flood events caused by climate change. [ABSTRACT FROM AUTHOR]

Plant growth depends on a number of conditions, including moisture, sunlight, air and temperature. Particularly, soil moisture is critical for plant survival. Recently, climate change has increased the frequency of extreme weather events, and the influence of topography, uneven spatial and temporal rainfall distribution and extreme climate has affected food security and irrigation water allocation in Taiwan, and the utilisation of water resources is often limited. Taiwan has been increasingly experiencing droughts and water shortages. Hence, the use of technology to efficiently and conveniently manage water resources has received increasing attention. Herein, an intelligent irrigation system for rice paddies based on the Internet of Things was developed that combines monitoring, automatic control and cloud-based systems to optimise the efficiency and reduce the workload the farmers and perform remote or automated irrigation operations to improve irrigation efficiency. The systems are particularly useful in the regions of Taiwan that are facing agricultural population aging. After 4 years of field operation, the intelligent irrigation system effectively saved water when combined with appropriate irrigation strategies. The water-saving rates in the dry and wet seasons were ∼2.9–6.5% and 8.8–19.3%, respectively and the yield and other agronomic traits remained unaffected. • The irrigation system can be controlled and monitored remotely by an application. • The irrigation system can be used in various environments. • Appropriate irrigation strategies have been saving water. • This study proposes irrigation strategies that were unaffected by yield and other agronomic traits. [ABSTRACT FROM AUTHOR]

In recent years, the amount of information has been growing quickly within organisations. Thus, several organisations have implemented analytics and business intelligence (BI) for Big Data solutions. To effectively foster the acceptance and usage of BI, this study examines how the motivational factors of both expectancy theory and social exchange theory (SET) affect managers' willingness to utilise BI. A survey of 271 managers supports the proposed model. The results show that intangible rewards of expectancy theory significantly influence the intention to read information (IRI) through the desire to make good decisions. Benefit factors (organisational rewards, reputation, and reciprocity) of SET are significantly associated with the desire to exchange reports (DER). The IRI significantly directly and indirectly influences the intention to create reports through the DER. Given the empirical findings, this study provides theoretical and managerial insights for organisations. [ABSTRACT FROM PUBLISHER]