Establishing a time series trend structure model to mine potential hydrological information from hydrometeorological time series data

Yao, Jiping; Wang, Puze; Wang, Guoqiang; Shrestha, Sangam; Xue, Baolin; Sun, Wenchao

doi:10.1016/j.scitotenv.2019.134227

Cited by 20 publications

(8 citation statements)

References 39 publications

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“…The rapid acquisition of plants’ water status is of great significance for studies on plant physiological status and precision agricultural irrigation [ 2 , 3 , 4 ]. Plant water information is a kind of time series data, which is closely related to practical application and is also relevant to the field of data mining [ 5 , 6 , 7 ]. Plant information data is obtained through specific sensor monitoring [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

A Time Series Data Filling Method Based on LSTM—Taking the Stem Moisture as an Example

Song

Gao

Zhao

et al. 2020

Sensors

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In order to solve the problem of data loss in sensor data collection, this paper took the stem moisture data of plants as the object, and compared the filling value of missing data in the same data segment with different data filling methods to verify the validity and accuracy of the stem water filling data of the LSTM (Long Short-Term Memory) model. This paper compared the accuracy of missing stem water data for plants under different data filling methods to solve the problem of data loss in sensor data collection. Original stem moisture data was selected from Lagerstroemia Indica which was planted in the Haidian District of Beijing in June 2017. Part of the data which treated as missing data was manually deleted. Interpolation methods, time series statistical methods, the RNN (Recurrent Neural Network), and LSTM neural network were used to fill in the missing part and the filling results were compared with the original data. The result shows that the LSTM has more accurate performance than the RNN. The error values of the bidirectional LSTM model are the smallest among several models. The error values of the bidirectional LSTM are much lower than other methods. The MAPE (mean absolute percent error) of the bidirectional LSTM model is 1.813%. After increasing the length of the training data, the results further proved the effectiveness of the model. Further, in order to solve the problem of one-dimensional filling error accumulation, the LSTM model is used to conduct the multi-dimensional filling experiment with environmental data. After comparing the filling results of different environmental parameters, three environmental parameters of air humidity, photosynthetic active radiation, and soil temperature were selected as input. The results show that the multi-dimensional filling can greatly extend the sequence length while maintaining the accuracy, and make up for the defect that the one-dimensional filling accumulates errors with the increase of the sequence. The minimum MAPE of multidimensional filling is 1.499%. In conclusion, the data filling method based on LSTM neural network has a great advantage in filling the long-lost time series data which would provide a new idea for data filling.

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Section: Introductionmentioning

confidence: 99%

A Time Series Data Filling Method Based on LSTM—Taking the Stem Moisture as an Example

Song

Gao

Zhao

et al. 2020

Sensors

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“…One of the most prominent methodologies for prediction systems is deep learning [39], such as time series analysis, which is used in emergency management to limit the threat of disasters. Combining hierarchy time series with a deep learning algorithm is another method used to solve forecasting problems [40][41][42].…”

Section: Artificial Intelligence and Disaster Managementmentioning

confidence: 99%

Toward an Integrated Disaster Management Approach: How Artificial Intelligence Can Boost Disaster Management

et al. 2021

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Technical and methodological enhancement of hazards and disaster research is identified as a critical question in disaster management. Artificial intelligence (AI) applications, such as tracking and mapping, geospatial analysis, remote sensing techniques, robotics, drone technology, machine learning, telecom and network services, accident and hot spot analysis, smart city urban planning, transportation planning, and environmental impact analysis, are the technological components of societal change, having significant implications for research on the societal response to hazards and disasters. Social science researchers have used various technologies and methods to examine hazards and disasters through disciplinary, multidisciplinary, and interdisciplinary lenses. They have employed both quantitative and qualitative data collection and data analysis strategies. This study provides an overview of the current applications of AI in disaster management during its four phases and how AI is vital to all disaster management phases, leading to a faster, more concise, equipped response. Integrating a geographic information system (GIS) and remote sensing (RS) into disaster management enables higher planning, analysis, situational awareness, and recovery operations. GIS and RS are commonly recognized as key support tools for disaster management. Visualization capabilities, satellite images, and artificial intelligence analysis can assist governments in making quick decisions after natural disasters.

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“…In the process of practical application, the time sequence generally obtained is not the stationary time sequence in the above because it is difficult to analyze the data law. Some scholars put forward the ARIMA model to solve the conflict between practice and theories ( Yao et al, 2020 ). It is also called the differential ARMA model.…”

Section: Research Modelmentioning

confidence: 99%

Applying Deep Learning in the Training of Communication Design Talents Under University-Industrial Research Collaboration

Rui

et al. 2021

Front. Psychol.

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The purpose of the study was to solve the problem of the mismatching between the supply and demand of the talents that universities provide for society, whose major is communication design. The correlations between social post demand and university cultivation, as well as between social post demand and the demand indexes of enterprises for posts, are explored under the guidance of University-Industrial Research Collaboration. The backpropagation neural network (BPNN) is used, and the advantages of the Seasonal Autoregressive Integrated Moving Average model (SARIMA) model are combined to design the SARIMA-BPNN (SARIMA-BP) model after the relevant parameters are adjusted. Through the experimental analysis, it is found that the error of the root mean square of the designed SARIMA-BP model in post prediction is 7.523 and that of the BPNN model is 16.122. The effect of the prediction model that was designed based on deep learning is smaller than that of the previous model based on the neural network, and it can predict future posts more accurately for colleges and universities. Guided by the “University-Industrial Research Collaboration,” students will have more practice in the teaching process in response to social needs. “University-Industrial Research Collaboration” guides the teaching direction for communication design majors and can help to cultivate communication design talents who are competent for the post provided.

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Establishing a time series trend structure model to mine potential hydrological information from hydrometeorological time series data

Cited by 20 publications

References 39 publications

A Time Series Data Filling Method Based on LSTM—Taking the Stem Moisture as an Example

A Time Series Data Filling Method Based on LSTM—Taking the Stem Moisture as an Example

Toward an Integrated Disaster Management Approach: How Artificial Intelligence Can Boost Disaster Management

Applying Deep Learning in the Training of Communication Design Talents Under University-Industrial Research Collaboration

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