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1. Predicting high‐frequency variation in stream solute concentrations with water quality sensors and machine learning

   https://doi.org/10.1002/hyp.14000  

   Green, Mark B.; Pardo, Linda H.; Bailey, Scott W.; Campbell, John L.; McDowell, William H.; Bernhardt, Emily S.; Rosi, Emma J. (December 2020, Hydrological Processes)

   Abstract Stream solute monitoring has produced many insights into ecosystem and Earth system functions. Although new sensors have provided novel information about the fine‐scale temporal variation of some stream water solutes, we lack adequate sensor technology to gain the same insights for many other solutes. We used two machine learning algorithms – Support Vector Machine and Random Forest – to predict concentrations at 15‐min resolution for 10 solutes, of which eight lack specific sensors. The algorithms were trained with data from intensive stream sensing and manual stream sampling (weekly) for four full years in a hydrologic reference stream within the Hubbard Brook Experimental Forest in New Hampshire, USA. The Random Forest algorithm was slightly better at predicting solute concentrations than the Support Vector Machine algorithm (Nash‐Sutcliffe efficiencies ranged from 0.35 to 0.78 for Random Forest compared to 0.29 to 0.79 for Support Vector Machine). Solute predictions were most sensitive to the removal of fluorescent dissolved organic matter, pH and specific conductance as independent variables for both algorithms, and least sensitive to dissolved oxygen and turbidity. The predicted concentrations of calcium and monomeric aluminium were used to estimate catchment solute yield, which changed most dramatically for aluminium because it concentrates with stream discharge. These results show great promise for using a combined approach of stream sensing and intensive stream discrete sampling to build information about the high‐frequency variation of solutes for which an appropriate sensor or proxy is not available. 

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2. MOSS—Multi-Modal Best Subset Modeling in Smart Manufacturing

   https://doi.org/10.3390/s21010243  

   Wang, Lening; Du, Pang; Jin, Ran (January 2021, Sensors)

   null (Ed.)

   Smart manufacturing, which integrates a multi-sensing system with physical manufacturing processes, has been widely adopted in the industry to support online and real-time decision making to improve manufacturing quality. A multi-sensing system for each specific manufacturing process can efficiently collect the in situ process variables from different sensor modalities to reflect the process variations in real-time. However, in practice, we usually do not have enough budget to equip too many sensors in each manufacturing process due to the cost consideration. Moreover, it is also important to better interpret the relationship between the sensing modalities and the quality variables based on the model. Therefore, it is necessary to model the quality-process relationship by selecting the most relevant sensor modalities with the specific quality measurement from the multi-modal sensing system in smart manufacturing. In this research, we adopted the concept of best subset variable selection and proposed a new model called Multi-mOdal beSt Subset modeling (MOSS). The proposed MOSS can effectively select the important sensor modalities and improve the modeling accuracy in quality-process modeling via functional norms that characterize the overall effects of individual modalities. The significance of sensor modalities can be used to determine the sensor placement strategy in smart manufacturing. Moreover, the selected modalities can better interpret the quality-process model by identifying the most correlated root cause of quality variations. The merits of the proposed model are illustrated by both simulations and a real case study in an additive manufacturing (i.e., fused deposition modeling) process. 

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3. An integrated cyberGIS and machine learning framework for fine-scale prediction of Urban Heat Island using satellite remote sensing and urban sensor network data

   https://doi.org/10.1007/s44212-022-00002-4  

   Lyu, Fangzheng; Wang, Shaohua; Han, Su Yeon; Catlett, Charlie; Wang, Shaowen (September 2022, Urban Informatics)

   Abstract Due to climate change and rapid urbanization, Urban Heat Island (UHI), featuring significantly higher temperature in metropolitan areas than surrounding areas, has caused negative impacts on urban communities. Temporal granularity is often limited in UHI studies based on satellite remote sensing data that typically has multi-day frequency coverage of a particular urban area. This low temporal frequency has restricted the development of models for predicting UHI. To resolve this limitation, this study has developed a cyber-based geographic information science and systems (cyberGIS) framework encompassing multiple machine learning models for predicting UHI with high-frequency urban sensor network data combined with remote sensing data focused on Chicago, Illinois, from 2018 to 2020. Enabled by rapid advances in urban sensor network technologies and high-performance computing, this framework is designed to predict UHI in Chicago with fine spatiotemporal granularity based on environmental data collected with the Array of Things (AoT) urban sensor network and Landsat-8 remote sensing imagery. Our computational experiments revealed that a random forest regression (RFR) model outperforms other models with the prediction accuracy of 0.45 degree Celsius in 2020 and 0.8 degree Celsius in 2018 and 2019 with mean absolute error as the evaluation metric. Humidity, distance to geographic center, and PM_2.5concentration are identified as important factors contributing to the model performance. Furthermore, we estimate UHI in Chicago with 10-min temporal frequency and 1-km spatial resolution on the hottest day in 2018. It is demonstrated that the RFR model can accurately predict UHI at fine spatiotemporal scales with high-frequency urban sensor network data integrated with satellite remote sensing data. 

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4. RISC: Resource-Constrained Urban Sensing Task Scheduling Based on Commercial Fleets

   https://doi.org/10.1145/3397337  

   Xie, Xiaoyang; Fang, Zhihan; Wang, Yang; Zhang, Fan; Zhang, Desheng (June 2020, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   With the trend of vehicles becoming increasingly connected and potentially autonomous, vehicles are being equipped with rich sensing and communication devices. Various vehicular services based on shared real-time sensor data of vehicles from a fleet have been proposed to improve the urban efficiency, e.g., HD-live map, and traffic accident recovery. However, due to the high cost of data uploading (e.g., monthly fees for a cellular network), it would be impractical to make all well-equipped vehicles to upload real-time sensor data constantly. To better utilize these limited uploading resources and achieve an optimal road segment sensing coverage, we present a real-time sensing task scheduling framework, i.e., RISC, for Resource-Constraint modeling for urban sensing by scheduling sensing tasks of commercial vehicles with sensors based on the predictability of vehicles' mobility patterns. In particular, we utilize the commercial vehicles, including taxicabs, buses, and logistics trucks as mobile sensors to sense urban phenomena, e.g., traffic, by using the equipped vehicular sensors, e.g., dash-cam, lidar, automotive radar, etc. We implement RISC on a Chinese city Shenzhen with one-month real-world data from (i) a taxi fleet with 14 thousand vehicles; (ii) a bus fleet with 13 thousand vehicles; (iii) a truck fleet with 4 thousand vehicles. Further, we design an application, i.e., track suspect vehicles (e.g., hit-and-run vehicles), to evaluate the performance of RISC on the urban sensing aspect based on the data from a regular vehicle (i.e., personal car) fleet with 11 thousand vehicles. The evaluation results show that compared to the state-of-the-art solutions, we improved sensing coverage (i.e., the number of road segments covered by sensing vehicles) by 10% on average. 

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5. Fast Simulation of Trees and Forests for Bat-inspired Sonar Sensing

   https://doi.org/10.1109/ICICT55905.2022.00041  

   Wahed, M.; Islam, M.; Wu, X.; Zhu, H. (May 2022, 2022 5th International Conference on Information and Computer Technologies (ICICT))

   To study the sensing mechanism of bat's biosonar system, we propose a fast simulation algorithm to generate natural-looking trees and forest---the primary living habitat of bats. We adopt 3D Lindenmayer system to create the fractal geometry of the trees, and add additional parameters, both globally and locally, to enable random variations of the tree structures. Random forest is then formed by placing simulated trees at random locations of a field according to a spatial point process. By employing a single algorithmic model with different numeric parameters, we can rapidly simulate 3D virtual environments with a wide variety of trees, producing detailed geometry of the foliage such as the leaf locations, sizes, and orientations. Written in C++ and visualized with openGL, our algorithm is fast to implement, easily parallable, and more adaptive to real-time visualization compared with existing alternative approaches. Our simulated environment can be used for general purposes such as studying new sensors or training remote sensing algorithms. 

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