The early-warning model for flash floods is based on a hydrological and geomorphological concept connected to the river basin, with the principle that flash floods will only occur where there is a high potential risk and when rainfall exceeds the threshold. In the model used to build flash-floods risk maps, the parameters of the basin are analyzed and evaluated and the weight is determined using Thomas Saaty’s analytic hierarchy process (AHP). The flash-floods early-warning software is built using open source programming tools. With the spatial module and online processing, a predicted precipitation of one to six days in advance for iMETOS (AgriMedia—Vietnam) automatic meteorological stations is interpolated and then processed with the potential risk maps (iMETOS is a weather-environment monitoring system comprising a wide range of equipment and an online platform and can be used in various fields such as agriculture, tourism and services). The results determine the locations of flash floods at several risk levels corresponding to the predicted rainfall values at the meteorological stations. The system was constructed and applied to flash floods disaster early warning for Thuan Chau in Son La province when the rainfall exceeded the 150 mm/d threshold. The system initially supported positive decision-making to prevent and minimize damage caused by flash floods.

Water treatment is an important process, as it improves water quality and makes it better for any end use, whether it be drinking, industrial use, irrigation, water recreation, or any other kind of use. Turbidity is one of the fundamental measurements of the clarity of water in water treatment. Specifically, this component is an optical feature of the amount of light on scatter particles when light is shined on a water sample. It is crucial in water reservoirs to provide clean water, which is difficult to manage and predict. Hence, this study focuses on the use of robust deep learning models to analyze time-series data in order to predict the water quality of turbidity in a reservoir area. Deep learning models may become an alternative solution in predicting water quality because of their accuracy. This study is divided into two parts: (a) the first part uses the optical bands of blue (B), green (G), red (R), and infrared (IR) to build a regression function to monitor turbidity in water, and (b) the second part uses a hybrid model to analyze time-series turbidity data with the recursive neural network (RNN2) model. The selected models’ accuracies are compared based on the accuracy using the input data, forecasting level, and training time. The analysis shows that these models have their strengths and limitations under different analyzed conditions. Generally, RNN2 shows the performance regarding the root-mean-square error (RMSE) evaluation metric. The most significant finding is that the RNN2 model is suitable for the accurate prediction of water quality. The RMSE is used to facilitate a comparison of the accuracy of the sampling data. In the training model, the training data have an RMSE of 20.89, and the testing data have an RMSE of 30.11. The predicted R-squared values in the RNN2 model are 0.993 (training data) and 0.941 (testing data).

The efficiency and high mobility of Unmanned Aerial Vehicle (UAV) made them essential to aerial photography assisted survey and mapping. Especially for urban land use and land cover, that they often changes, and need UAVs to obtain new terrain data and the new changes of land use. This study aims to collect image data and three dimensional ground control points in Taichung city area with Unmanned Aerial Vehicle (UAV), general camera and Real-Time Kinematic with positioning accuracy down to centimetre. The study area is an ecological park that has a low topography which support the city as a detention basin. A digital surface model was also built with Agisoft PhotoScan, and there will also be a high resolution orthophotos. There will be two conditions for this study, with or without ground control points and both were discussed and compared for the accuracy level of each of the digital surface models. According to check point deviation estimate, the model without ground control points has an average two-dimension error up to 40 centimeter, altitude error within one meter. The GCP-free RTK-airborne approach produces centimeter-level accuracy with excellent to low risk to the UAS operators. As in the case of the model with ground control points, the accuracy of x, y, z coordinates has gone up 54.62%, 49.07%, and 87.74%, and the accuracy of altitude has improved the most.

Spatial information technology has been widely used for vehicles in general and for fleet management. Many studies have focused on improving vehicle positioning accuracy, although few studies have focused on efficiency improvements for managing large truck fleets in the context of the current complex network of roads. Therefore, this paper proposes a multilayer-based map matching algorithm with different spatial data structures to deal rapidly with large amounts of coordinate data. Using the dimension reduction technique, the geodesic coordinates can be transformed into plane coordinates. This study provides multiple layer grouping combinations to deal with complex road networks. We integrated these techniques and employed a puncture method to process the geometric computation with spatial data-mining approaches. We constructed a spatial division index and combined this with the puncture method, which improves the efficiency of the system and can enhance data retrieval efficiency for large truck fleet dispatching. This paper also used a multilayer-based map matching algorithm with raster data structures. Comparing the results revealed that the look-up table method offers the best outcome. The proposed multilayer-based map matching algorithm using the look-up table method is suited to obtaining competitive performance in identifying efficiency improvements for large truck fleet dispatching.

Keywords: e-learning platform, collaboration, educational resources sharing Using GIS in the classroom enhance students’ computer skills and explore the range of knowledge. The paper highlights GIS integration on e-learning platform and introduces a variety of abundant educational resources. This research project will demonstrate tools for e-learning environment and delivers some case studies for learning interaction from Central Taiwan Universities. Feng Chia University (FCU) obtained a remarkable academic project subsidized by Ministry of Education and developed e-learning platform for excellence in teaching/learning programs among Central Taiwan’s universities. The aim of the project is to integrate the educational resources of 13 universities in central Taiwan. FCU is serving as the hub of Center University. To overcome the problem of distance, e-platforms have been established to create experiences with collaboration enhanced learning. The e-platforms provide coordination of web service access among the educational community and deliver GIS educational resources. Most of GIS related courses cover the development of GIS, principles of cartography, spatial data analysis and overlaying, terrain analysis, buffer analysis, 3D GIS application, Remote Sensing, GPS technology, and WebGIS, MobileGIS, ArcGIS manipulation. In each GIS case study, students have been taught to know geographic meaning, collect spatial data and then use ArcGIS software to analyze spatial data. On one of e-Learning platforms provide lesson plans and presentation slides. Students can learn Arc GIS online. As they analyze spatial data, they can connect to GIS hub to get data they need including satellite images, aerial photos, and vector data. Moreover, e-learning platforms provide solutions and resources. Different levels of image scales have been integrated into the systems. Multi-scale spatial development and analyses in Central Taiwan integrate academic research resources among CTTLRC partners. Thus, establish decision-making support mechanism in teaching and learning. Accelerate communication, cooperation and sharing among academic units

Taiwan is located at the junction of plates in which the stratum is relatively unstable, resulting in frequent earthquakes. Driftwood has always been regarded as a precious asset that enables ecoscientists to track earthquakes. In the event of a typhoon or heavy rain, the surface water flows to flush the woods from the hills to the coast. More specifically, a large rainfall or earthquake may cause floods and collapses, and the trees in the forest will be washed down. Therefore, this study used high-resolution images to build an image database of the new north coast of Taiwan, and a deep learning approach is incorporated to classify the driftwoods. To improve the interpretation of driftwood in the remote images, we initially import eight pieces of textured information which are employed to the raw bands (B, G, R, and IR). The usage of spatial information image extraction technology is incorporated into a deep learning analysis using two parallel approaches. The generative adversarial network (GAN) is used to analyze the color images alongside an ancillary image with texture information. Most of the salt–pepper effects are produced by applying a high-resolution thematic map, and an error matrix is generated to compare the differences between them. The raw data (original R + G + B + IR) images, when analyzed using GAN, have about 70% overall classification outcomes. Not all of the driftwood can be detected. By applying the texture information to the parallel approach, the overall accuracy is enhanced to 78%, and about 80% of the driftwood can be recognized.