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Tea is an important economic crop worldwide, especially in Asian countries. However, tea cultivation requires substantial fertilizer use and may become a nutrient pollution source and affect water quality. This study presented two objectives: one was to estimate the pollution export coefficients of tea farms, and the other was to assess the performance of bioretention cells in terms of tea farm pollution control. This study employed a tea farm pollutant transport model (TPTM) and a watershed pollutant transport model (WPTM) to link watershed management goals and the tea farm control strategy. Field data collected for Jingualiao Creek in the Feitsui Reservoir watershed in Taipei, Taiwan, were analyzed. The resulting export coefficients for total phosphorus (TP), NH3-N, suspended solids (SS), and chemical oxygen demand (COD) were 2.55, 4.22, 768.39, and 145.71 kg/ha-y, respectively. Bioretention cells, which are low-impact development (LID) facilities and structural best management practices (BMPs), were installed and tested for their ability to reduce nonpoint source pollution. The field investigation and modeling results showed that 1 m2 of bioretention cells could reduce TP, NH3-N, SS, and COD by 18.6, 20.9, 5545.5, and 881.4 g/y, respectively. According to the WPTM results, 540 m2 of bioretention cells could achieve an 85% water quality attainment goal, and 715 m2 could reach 90% water quality attainment. Four tea farms covering 1.43 ha require 30.0 m2 of bioretention cells to achieve an 85% goal and 33.5 m2 to 90% goal. The export coefficients of tea nonpoint pollution sources presented in this study can serve as a valuable tool for estimating potential exported nutrients, and the field test results of bioretention cells are helpful information for policymakers in formulating effective watershed management measures. [ABSTRACT FROM AUTHOR]

Urban stormwater drainage systems, which include many personholes to collect and discharge precipitation within a city, are extensively constructed to prevent streets and buildings from flooding. This research intends to build a machine learning model to predict whether a personhole will overflow soon, which is crucial to alleviate the damage caused by floods. To address the challenges posed by many diverse personholes, we proposed segmenting the personholes into several groups and have designed two methods employing different personhole features. The first, the geography-based method, uses the geographical locations of the personholes for the grouping. The second, the hydrology-based method, uses the characteristics that are directly related to the overflowing situation, such as the depth of the personhole, and the average and the maximum water level of the personholes. We also investigated several machine learning techniques, such as the multilayer perceptron (MLP) model and a fine-tuning architecture. The study area was located in the new Taipei city and the experimental results have shown the impressive predictive ability of the proposed approaches. Particularly, by applying the hydrology-based grouping method, and using a hybrid model combining the machine learning model prediction results with heuristic rules, we can obtain the best prediction result, and the accuracy is over 99%. We have also noticed the influence of the activation function used in the neural network and the number of frozen layers in the fine-tuning architecture. Particularly, using the tanh function with one frozen layer is good in some cases. However, since it is not general enough, we suggest the readers perform empirical studies before choosing the best setting in their own environment. [ABSTRACT FROM AUTHOR]

Weather plays a critical role in outdoor agricultural production; therefore, climate information can help farmers to arrange planting and production schedules, especially for urban agriculture (UA), providing fresh vegetables to partially fulfill city residents' dietary needs. General weather information in the form of timely forecasts is insufficient to anticipate potential occurrences of weather types and features during the designated time windows for precise cultivation planning. In this research, we intended to use a self-organizing map (SOM), which is a clustering technique with powerful feature extraction ability to reveal hidden patterns of datasets, to explore the represented spatiotemporal weather features of Taipei city based on the observed data of six key weather factors that were collected at five weather stations in northern Taiwan during 2014 and 2018. The weather types and features of duration and distribution for Taipei on a 10-day basis were specifically examined, indicating that weather types #2, #4, and #7 featured to manifest the dominant seasonal patterns in a year. The results can serve as practical references to anticipate upcoming weather types/features within designated time frames, arrange potential/further measures of cultivation tasks and/or adjustments in response, and use water/energy resources efficiently for the sustainable production of smart urban agriculture. [ABSTRACT FROM AUTHOR]

Wargames have been promoted by local governments in Taiwan since 2009, as they require far fewer resources than full-scale exercises. Previously, wargame scenarios were divulged before their launch, enabling participants to formulate response plans in advance, which made them ineffective. Currently, wargames in which scenarios are not shared in advance require a common platform for debriefing players regarding their planned actions. Owing to its geographical location, Taiwan is prone to significant flooding disasters. To assist in making countermeasures, we created a 3D GIS-based Flood Wargame Assistance Platform (FWGAP) for conducting rapid spatial analyses. Flooded areas are estimated in the FWGAP in three ways: (1) using a digital terrain model (DTM) with designated flood center and depth; (2) applying historical flooding spots; and (3) potential flooding maps. A FWGAP can estimate affected and vulnerable populations and has functions for locating resources such as shelters and hospitals near the flooded areas. Its integrated use of closed-circuit televisions, Google Street View maps, and 3D buildings to display flooded areas realistically ensures greater fidelity. This study reports on the city- and the district-level applications of the FWGAP. The results of the survey undertaken indicates that 56% of the participants agreed that the FWGAP enables disaster relief resources to be located on a GIS map. About half of the participants believed that a no-script flooding wargame using a FWGAP could help to identify problems in standard operation procedures and promote greater horizontal coordination among departments. [ABSTRACT FROM AUTHOR]

The Taipei University of Technology, under contract from the Taipei City Government, completed a study on porous asphalt (PA) and permeable interlocking concrete brick (PICB) pavement performance with respect to stormwater runoff reduction and surface temperature mitigation. Additionally, the variation of infiltration rates against time of these pavements was monitored. The results show the following: (a) Runoff peak reduction ranged from 16% for large, intense storms to 55% for small, long-duration storms. Rainfall volume reduction ranged from 16% to 77% with an average of 37.6%; (b) Infiltration rate: for PICB, it decreased by 25% to 50% over a 15-month monitoring period, but the rate at one location increased significantly after cleaning; for PA, the rate remained high at one location, but decreased by 70%–80% after 10 months at two other locations, due mainly to clogging problems; (c) Surface temperature: during storm events, porous concrete bricks had on average lower temperatures compared to regular concrete with a maximum difference of 6.6 °C; for porous asphalt the maximum drop was 3.9 °C. During dry days, both PA and PICB showed a tendency of faster temperature increase as the air temperature rose, but also faster temperature decreases as the air cooled when compared to regular pavements. On very hot days, much lower surface temperatures were observed for porous pavements (for PA: 17.0 °C and for PICB: 14.3 °C) than those for regular pavements. The results suggest that large-scale applications of porous pavements could help mitigate urban heat island impacts. [ABSTRACT FROM AUTHOR]