Evaluation of regional water resources capacity provides a scientific basis for further water resources utilization and social economic sustainable development. This paper mainly studied on the case of Lanzhou City located in the western China. By using the method of fuzzy comprehensive evaluation and basing on the historical datum of 40 years, this paper evaluated the current situation of water resource capacity in Lanzhou and its dynamic trend. The calculation of comprehensive evaluation matrix is confirmed on the nature of membership function, the dynamic trend of water resource capacity is forecasted as well after the police was putted in practice in the future. The results showed that the utilization of water resource in Lanzhou is unreasonable now. The water resources have been developed to a considerable scale, but the water carrying capacity decreased year by year. Basing on the study, this paper suggested that the system and model of developing and utilizing water resource, policy of using water, scientific policy of water price and paying policy of water resource, saving water and protecting solution society, as well as protecting engineering related with water resource should be built up step by step.

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Abstract:Compared with optical sensors, Synthetic Aperture Radar (SAR) can provide important damage information due to its ability to map areas affected by earthquakes independently from weather conditions and solar illumination. In 2013, a new TerraSAR-X mode named staring spotlight (ST), whose azimuth resolution was improved to 0.24 m, was introduced for various applications. This data source made it possible to extract detailed information from individual buildings. In this paper, we present a new concept for individual building damage assessment using a post-event sub-meter very high resolution (VHR) SAR image and a building footprint map. With the building footprint map, the original footprints of buildings can be located in the SAR image. Based on the building imaging analysis of a building in the SAR image, the features in the building footprint can be extracted to identify standing and collapsed buildings. Three machine learning classifiers, including random forest (RF), support vector machine (SVM) and K-nearest neighbor (K-NN), are used in the experiments. The results show that the proposed method can obtain good overall accuracy, which is above 80% with the three classifiers. The efficiency of the proposed method is demonstrated based on samples of buildings using descending and ascending sub-meter VHR ST images, which were all acquired from the same area in old Beichuan County, China.

Neural models have recently shown significant progress on data-to-text generation tasks in which descriptive texts are generated conditioned on database records. In this work, we present a new Transformer-based data-totext generation model which learns content selection and summary generation in an endto-end fashion. We introduce two extensions to the baseline transformer model: First, we modify the latent representation of the input, which helps to significantly improve the content correctness of the output summary; Second, we include an additional learning objective that accounts for content selection modelling. In addition, we propose two data augmentation methods that succeed to further improve performance of the resulting generation models. Evaluation experiments show that our final model outperforms current state-of-theart systems as measured by different metrics: BLEU, content selection precision and content ordering. We made publicly available the transformer extension presented in this paper 1 .