We propose a new approach to physically quantify the change of snake line patterns with the application of Clausius-Clapeyron scaling law. The key of the proposed approach is to establish the physically based linkage among air temperature change on ground surface, precipitation intensity, and corresponding snake line pattern of certain percentiles of rainfall events. We used 99th and 50th percentiles for representing extreme and general precipitation conditions. With the long-term observation at Kobe meteorological observatory, we successfully verified the applicability of the proposed approach. The climate projections of 5km NHRCM were then analyzed to examine the future change of Clausius-Clapeyron scaling in the Japanese archipelago. It is also revealed the change of snake line patterns under climate change influences.
In recent years, the dumping of garbage in rivers has become a common occurrence and has gradually started to affect the normal flow of river channels, which has added lots of work to the river patrol staff. Facing these problems, river authorities urgently need a reasonable and better cost-performance method, that can be adopted on a large scale to support the staff in investigating the garbage within the rivers. Although object detection using Artificial Intelligence has its advantages, it has not been widely applied in the riverine environment using drone. This study attempts to detect garbage in the Asahi River, Japan using two object detection models. By using a large amount of PET images collected from the Internet as training dataset and experimenting with a variety of model-related parameters (i.e., Batch size, Epochs), this study achieved high-accuracy results in recognition of the garbage in the study site. Conclusively, the additional dataset of PET for training, with the similar GSD as test dataset, can improve the Recall value. Nevertheless, without combining with Original dataset collected from the study site, it is difficult to detect the PET using additional dataset only. Thus, combination of Original and additional dataset is a relatively better method to improve the Recall value of detecting PET.
Input variable selection is one of the most challenging tasks for modelers when building a hydrological model using artificial neural networks (ANNs). The conventional method of input variable selection for ANN considers the linear correlation of each variable with the prediction target variable. However, this conventional approach can potentially limit the ability of ANN models. This study surveys the sensitivity of input variable selection methods in ANN performance to obtain an idea to save our time and concerns related to input variable selection. We prepared three ANN models with different input variable selection methods and two regression models as well. Comparing the results from these five models, which are for hourly-based water stage prediction at the Hirakata station, indicates that ANN provides satisfactory prediction accuracy without a careful input variable selection process. And, there is a possibility that ANN performs poorly if the variable selection process eliminates the necessary data.