With the development of large‐scale rice cultivation management initiatives in East Asia, there is concern that a reduction in the number of human cultivators per unit area may lead to poor water management, which could result in decreased land productivity, owing to abnormally high‐ and low‐temperature damage to crops. Accurate simulation of paddy field water temperature is important for studying its impact on crops and providing timely information to aid in decision‐making for more efficient management under limited resources. We propose a neural‐network framework that considers the heat transfer by the vegetation canopy and applies physical‐theory constraints in its training. A novel tuning method is proposed to cope with the trade‐off between water temperature accuracy and physical consistency during training to ensure that the calculated water temperature variations in a paddy field enjoy high accuracy and physical consistency. In the experiments, the proposed framework outperforms physical process models and pure neural network models while maintaining high accuracy in the case of sparse data sets. Furthermore, an attention‐mechanism input layer is integrated into the model to rank feature importance, providing global interpretation to the proposed framework. We also perform sensitivity analysis on the physical process and propose models to compare their different strategies of feature ranking. The results show that the two methods have different sensitivities to different feature patterns, but they complement each other. In summary, the proposed model is credible and stable for practical applications and has the potential to guide more efficient paddy management.
Social networks, which describe relations among people or organizations as a network, have recently attracted attention. With the help of a social network, we can analyze the structure of a community and thereby promote efficient communications within it. We investigate the problem of extracting a network of researchers from the Web, to assist efficient cooperation among researchers. Our method uses a search engine to get the cooccurences of names of two researchers and calculates the streangth of the relation between them. Then we label the relation by analyzing the Web pages in which these two names cooccur. Research on social network extraction using search engines as ours, is attracting attention in Japan as well as abroad. However, the former approaches issue too many queries to search engines to extract a large-scale network. In this paper, we propose a method to filter superfluous queries and facilitates the extraction of large-scale networks. By this method we are able to extract a network of around 3000-nodes. Our experimental results show that the proposed method reduces the number of queries significantly while preserving the quality of the network as compared to former methods.
Numerous leak detection methods have been developed for pipeline systems because of the shortage of water resources, increased water demand, and leak accidents. These methods have their advantages and disadvantages in terms of cost, labor, and accuracy; therefore, it is important to narrow down the location of a leak as easily, rapidly, and accurately as possible. This study applies the technologies based on the execution of a transient event (transient testbased technologies (TTBTs)), and a model is presented for representing the relation between the leak location and the damping of the pressure transient due to the leakage. The model is verified with laboratory experiments in which the leak location can be narrowed down to be less than 10% to 30% of the total pipe length. The model is found to be more effective if the leak location is nearer to the upstream end. In addition, the leak location found by the damping model varies with an approximate absolute error of 2% to 5% of the pipe length. It is suggested that the damping model is suitable for narrowing down and not for finding the leak location, and should be used in combination with other leak detection methods. KEYWORDS detection of water leakage; pipeline; water hammer; damping of pressure transient; narrowing down of leak location; stock management of infrastructure
Climate change has led to increasing global air temperatures. In the field of crop cultivation, long-term high temperatures (heatwaves) during the ricegrowing season might increase the risk of high-temperature damage to rice, which might result in reductions to the yield and quality of rice. In this study, a hybrid forecast model consisting of a combined paddy field heat balance model and a meteorological forecast model is proposed for predicting 1-dayahead water temperatures as an alert system for high-temperature damage to paddy fields, with resolution in terms of hours. The results show close agreement between the measured and predicted water temperatures, and the hightemperature alert accuracy was 88.5%. Additionally, the climate resilience of paddy fields was investigated by using the rising annual temperatures due to climate change. The observations indicate that while paddy fields are sensitive to the climate, their climate resilience can be improved through artificial measures. Farmers and managers of paddy fields can thus be made aware of the water temperatures of the paddy fields in advance to enable reasonable management of water resources and avoid high-temperature damages caused by extreme weather conditions.
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