Research on Real-Time Local Rainfall Prediction Based on MEMS Sensors

Chao, Zeyi; Pu, Fangling; Yin, Yuke; Bin, Han; Chen, Xiaoling

doi:10.1155/2018/6184713

Cited by 68 publications

(31 citation statements)

References 13 publications

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“…Finally, the outputs of the memory block are calculated in the output gate. All this process can be formulated as described in Equations (1)-(5) [47].…”

Section: Long Short-term Memory Networkmentioning

confidence: 99%

Artificial Neural Networks, Sequence-to-Sequence LSTMs, and Exogenous Variables as Analytical Tools for NO2 (Air Pollution) Forecasting: A Case Study in the Bay of Algeciras (Spain)

González-Enrique

Ruiz-Aguilar

Moscoso-López

et al. 2021

Sensors

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This study aims to produce accurate predictions of the NO2 concentrations at a specific station of a monitoring network located in the Bay of Algeciras (Spain). Artificial neural networks (ANNs) and sequence-to-sequence long short-term memory networks (LSTMs) were used to create the forecasting models. Additionally, a new prediction method was proposed combining LSTMs using a rolling window scheme with a cross-validation procedure for time series (LSTM-CVT). Two different strategies were followed regarding the input variables: using NO2 from the station or employing NO2 and other pollutants data from any station of the network plus meteorological variables. The ANN and LSTM-CVT exogenous models used lagged datasets of different window sizes. Several feature ranking methods were used to select the top lagged variables and include them in the final exogenous datasets. Prediction horizons of t + 1, t + 4 and t + 8 were employed. The exogenous variables inclusion enhanced the model’s performance, especially for t + 4 (ρ ≈ 0.68 to ρ ≈ 0.74) and t + 8 (ρ ≈ 0.59 to ρ ≈ 0.66). The proposed LSTM-CVT method delivered promising results as the best performing models per prediction horizon employed this new methodology. Additionally, per each parameter combination, it obtained lower error values than ANNs in 85% of the cases.

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“…Finally, the outputs of the memory block are calculated in the output gate. All this process can be formulated as described in Equations (1)-(5) [47].…”

Section: Long Short-term Memory Networkmentioning

confidence: 99%

Artificial Neural Networks, Sequence-to-Sequence LSTMs, and Exogenous Variables as Analytical Tools for NO2 (Air Pollution) Forecasting: A Case Study in the Bay of Algeciras (Spain)

González-Enrique

Ruiz-Aguilar

Moscoso-López

et al. 2021

Sensors

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“…We used historical traffic to train a LSTM model, and then utilized the trained model to predict traffic. LSTM is acknowledged as the state-of-art prediction model for time series prediction [28]. Our LSTM model consists of one recurrent hidden layer and one output layer.…”

Section: The Dynamic Traffic Detectionmentioning

confidence: 99%

Dynamic Traffic Detection and Modeling for Beidou Satellite Networks

Yin

et al. 2020

Journal of Sensors

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Beidou navigation system (BDS) has been developed as an integrated system. The third BDS, BSD-3, will be capable of providing not only global positioning and navigation but also data communication. When the volume of data transmitted through BDS-3 continues to increase, BDS-3 will encounter network traffic congestion, unbalanced resource usage, or security attacks as terrestrial networks. The network traffic monitoring is essential for automatic management and safety assurance of BDS-3. A dynamic traffic detection method including traffic prediction by Long Short-Term Memory (LSTM) and a dynamically adjusting polling strategy is proposed to unevenly sample the traffic of each link. A distributed traffic detection architecture is designed for collection of the detected traffic and its related temporal and spatial information with low delay. A time-varying graph (TVG) model is introduced to represent the dynamic topology, the time-varying link, and its traffic. The BDS-3 network is simulated by STK. The WIDE dataset is used to simulate the traffic between the satellite and ground station. Simulation results show that the dynamic traffic detection method can follow the variation of the traffic of each link with uneven sampling. The detected traffic can be transmitted to the ground station in near real time through the distributed traffic detection architecture. The traffic and its related information are stored by using Neo4j in terms of the TVG model. The nodes, edges, and traffic of BDS-3 can be quickly queried through Neo4j. The presented dynamic traffic detection and representation schemes will support BDS-3 to establish automatic management and security system and develop business.

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“…For instance, in the agriculture fields, to accomplish the task of the remote environment monitoring for the farmland, developers can utilize single-board microcontrollers or computers to connect with various sensors to collect and accumulate a large amount of local environmental data at first. The data can be analyzed to get a more undiscovered relationship between the data via the technology of data mining [6][7][8][9][10][11][12]. After all, those relations can be used to achieve the goal of future predicting.…”

Section: Introductionmentioning

confidence: 99%

Development of a Small Intelligent Weather Station for Agricultural Applications

Chung

Huang

et al. 2021

Adv. technol. innov.

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It is known that climate change causes a decrease in the profit gained from agricultural production. This work designs and establishes weather boxes equipped with functions of rainfall prediction, frosting forecast, and lightning detection. With the wireless connection and the build-in decision mode, weather boxes can deliver early-warning by sending texting messages to the users and actuating the corresponding action to response the extreme climate. To implement rainfall and frosting prognostication, two different datasets are analyzed by the technology of data mining. One of the datasets is acquired from the Central Weather Bureau, and the other is from the proposed weather box monitoring the agricultural environment. From the experimental results, the prediction model constructed from the data which is collected by the proposed weather box exhibits a higher accuracy in rainfall forecasting than those based on the Central Weather Bureau.

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Research on Real-Time Local Rainfall Prediction Based on MEMS Sensors

Cited by 68 publications

References 13 publications

Artificial Neural Networks, Sequence-to-Sequence LSTMs, and Exogenous Variables as Analytical Tools for NO2 (Air Pollution) Forecasting: A Case Study in the Bay of Algeciras (Spain)

Artificial Neural Networks, Sequence-to-Sequence LSTMs, and Exogenous Variables as Analytical Tools for NO2 (Air Pollution) Forecasting: A Case Study in the Bay of Algeciras (Spain)

Dynamic Traffic Detection and Modeling for Beidou Satellite Networks

Development of a Small Intelligent Weather Station for Agricultural Applications

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