No abstract

Owing to their short duration and high intensity, flash floods are among the most devastating natural disasters in metropolises. The existing warning tools-flood potential maps and two-dimensional numerical models-are disadvantaged by time-consuming computation and complex model calibration. This study develops a data-driven, probabilistic rainfall-inundation model for flash-flood warnings. Applying a modified support vector machine (SVM) to limited flood information, the model provides probabilistic outputs, which are superior to the Boolean functions of the traditional rainfall-flood threshold method. The probabilistic SVM-based model is based on a data preprocessing framework that identifies the expected durations of hazardous rainfalls via rainfall pattern analysis, ensuring satisfactory training data, and optimal rainfall thresholds for validating the input/output data. The proposed model was implemented in 12 flash-flooded districts of the Xindian River. It was found that (1) hydrological rainfall pattern analysis improves the hazardous event identification (used for configuring the input layer of the SVM); (2) brief hazardous events are more critical than longer-lasting events; and (3) the SVM model exports the probability of flash flooding 1 to 3 h in advance. Such a combined model is quick and suitable for fluvial floods, but inapplicable to pluvial flash floods. A similar combined hydrological model, which derives the rainfall thresholds in pluvial flash floods, was considered by Forestieri et al. (2016) [5]. In natural hazards induced by short-term heavy rainfall such as flash floods or landslides, a timely warning is more important than evaluating the disaster impacts on people. Therefore, the empirical rainfall thresholds of flash floods and rainfall-induced landslides have been increasingly sought in recent years [5][6][7][8][9][10][11][12][13][14][15].The local governments in Taiwan also consider the rainfall thresholds in flash floods and landslides [16][17][18]. In the official flash-flood warning system of Taiwan, the rainfall thresholds in all counties and districts are decided from five cumulative rainfalls of durations 1, 3, 6, 12, and 24 h. At least one rain gauge is assigned as the reference rain gauge for each county or district [19]. In 2017, the Water Resources Agency (WRA) used 754 sets of rainfall thresholds based on 500 rain gauges and warned 368 counties and districts of pending flash floods in Taiwan. However, the rainfall thresholds are based on historical flood records, which lack the parameters (start times, areas, and durations of the floods) required in complex flood warning models. Most of the historical flood records in Taiwan are reports of people's phone calls, which are checked by officers several hours later. Therefore, when determining rainfall thresholds, the historical flood records of the WRA are useful only for predicting whether a rainfall event will cause a disaster. Although potential inundation maps improve the information input [18,20,21], the models based on th...

Pluvial floods are the most frequent natural hazard impacting urban cities because of extreme rainfall intensity within short duration. Owing to the complex interaction between rainfall, drainage systems and overland flow, pluvial flood warning poses a challenge for many metropolises. Although physical-based flood inundation models could identify inundated locations, hydrodynamic modeling is limited in terms of computational costs and sophisticated calibration. Thus, herein, a quick pluvial flood warning system using rainfall thresholds for central Taipei is developed. A tabu search algorithm is implemented with hydrological-analysis-based initial boundary conditions to optimize rainfall thresholds. Furthermore, a cross test is adopted to evaluate the effect of each rainfall event on rainfall threshold optimization. Urban sewer flood is simulated via hydrodynamic modeling with calibration using crowdsourced data. The locations and time of occurrence of pluvial floods can be obtained to increase the quality of observed data that dominate the accuracy of pluvial flood warning when using rainfall thresholds. The optimization process is a tabu search based on flood reports and observed data for six flood-prone districts in central Taipei. The results show that optimum rainfall thresholds can be efficiently determined through tabu search and the accuracy of the issued flood warnings can be significantly improved.

No abstract

Disassembly is a much needed step for remanufacturing end-of-life (EOL) products. Optimization of disassembly sequences and utilizing robotic technology capabilities are crucial for alleviating the labor-intensive nature of dismantling operations. This study proposes an optimization framework for disassembly sequence planning under uncertainty considering human-robot collaboration. The proposed model combines three attributes: disassembly cost, safety, and complexity of disassembly, namely disassembleability, to identify the optimal disassembly path and allocate operations among humans and robots. A multi-attribute utility function is used to address uncertainty and make a tradeoff among multiple attributes. The disassembly time reflects the cost of disassembly which is assumed to be an uncertain parameter with a Beta distribution; the disassembleability evaluates the feasibility of conducting operations by robot; finally, the safety index ensures the protection of human workers in the work environment. An example of dismantling a desktop computer is utilized to show the application. The model identifies the optimal disassembly sequence with less disassembly cost, high disassembleability, and increased safety index while allocating disassembly operations among human and robot. A sensitivity analysis is conducted to show the model's performance when changing the disassembly cost for the robot.

The remanufacturing workforce can benefit from the capabilities of robotic technology, where robots can alleviate the labor-intensive nature of disassembly operations and help with handling toxic and hazardous materials. However, operators’ safety is an important aspect of human-robot collaboration in disassembly operations. This study focuses on predicting human hand motion to provide advanced information to disassembly robots when collaborating with humans. A prediction framework is proposed, which consists of two deep learning models, including convolutional long short-term memory (ConvLSTM) and You Only Look Once (YOLO). ConvLSTM forecasts the next-frame image using images from the disassembly process, and then the YOLO model identifies the human hand object on the predicted image resulting from ConvLSTM. The disassembly images collected from four desktop computers are used to train the ConvLSTM and YOLO. The results reveal that the combined framework of ConvLSTM and YOLO performs well in predicting human hand motion and locating the hand object. The outcomes highlight the need for developing deep learning models capable of recognizing human motion when working with different designs as often remanufacturing workforce have to deal with a wide range of products from different brands, models, and conditions.

No abstract

No abstract