scholarly journals Social Risk Early Warning of Environmental Damage of Large-Scale Construction Projects in China Based on Network Governance and LSTM Model

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Junmin Fang ◽  
Dechun Huang ◽  
Jingrong Xu
Keyword(s):  
Early Warning ◽  
Construction Projects ◽  
Large Scale ◽  
Short Term Memory ◽  
Network Governance ◽  
Scale Construction ◽  
Environmental Damage ◽  
Social Risk ◽  
Short Term ◽  
Long Short Term Memory

With the improvement of citizens’ risk perception ability and environmental protection awareness, social conflicts caused by environmental problems in large-scale construction projects are becoming more and more frequent. Traditional social risk prevention management has some defects in obtaining risk data, such as limited coverage, poor availability, and insufficient timeliness, which makes it impossible to realize effective early warning of social risks in the era of big data. This paper focuses on the three environments of diversification of stakeholders, risk media, and big data era. The evolution characteristics of the social risk of environmental damage of large-scale construction projects are analyzed from the four stages of incubation, outbreak, mitigation, and regression in essence. On this basis, a social risk early warning model is constructed， and the multicenter network governance mode of social risk of environmental damage in large-scale construction projects and practical social risk prevention strategies in different stages are put forward. Experiments show that the long short-term memory neural network model is effective and feasible for predicting the social risk trend of environmental damage of large-scale construction projects. Compared with other classical models, the long short-term memory model has the advantages of strong processing capability and high early warning accuracy for time-sensitive data and will have broad application prospects in the field of risk control research. By using the network governance framework and long short-term memory model, this paper studies the environmental mass events of large-scale construction projects on the risk early warning method, providing reference for the government to effectively prevent and control social risk of environmental damage of large-scale construction project in China.

The performance of LSTM models from basin to continental scales

2020 ◽  
Author(s):  
Frederik Kratzert ◽  
Daniel Klotz ◽  
Günter Klambauer ◽  
Grey Nearing ◽  
Sepp Hochreiter
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Short Term Memory ◽  
Regional Scale ◽  
Data Driven ◽  
Hydrological Models ◽  
Short Term ◽  
Term Memory ◽  
Basin Scale ◽  
Long Short Term Memory

<p>Simulation accuracy among traditional hydrological models usually degrades significantly when going from single basin to regional scale. Hydrological models perform best when calibrated for specific basins, and do worse when a regional calibration scheme is used. </p><p>One reason for this is that these models do not (have to) learn hydrological processes from data. Rather, they have a predefined model structure and only a handful of parameters adapt to specific basins. This often yields less-than-optimal parameter values when the loss is not determined by a single basin, but by many through regional calibration.</p><p>The opposite is true for data driven approaches where models tend to get better with more and diverse training data. We examine whether this holds true when modeling rainfall-runoff processes with deep learning, or if, like their process-based counterparts, data-driven hydrological models degrade when going from basin to regional scale.</p><p>Recently, Kratzert et al. (2018) showed that the Long Short-Term Memory network (LSTM), a special type of recurrent neural network, achieves comparable performance to the SAC-SMA at basin scale. In follow up work Kratzert et al. (2019a) trained a single LSTM for hundreds of basins in the continental US, which outperformed a set of hydrological models significantly, even compared to basin-calibrated hydrological models. On average, a single LSTM is even better in out-of-sample predictions (ungauged) compared to the SAC-SMA in-sample (gauged) or US National Water Model (Kratzert et al. 2019b).</p><p>LSTM-based approaches usually involve tuning a large number of hyperparameters, such as the number of neurons, number of layers, and learning rate, that are critical for the predictive performance. Therefore, large-scale hyperparameter search has to be performed to obtain a proficient LSTM network.  </p><p>However, in the abovementioned studies, hyperparameter optimization was not conducted at large scale and e.g. in Kratzert et al. (2018) the same network hyperparameters were used in all basins, instead of tuning hyperparameters for each basin separately. It is yet unclear whether LSTMs follow the same trend of traditional hydrological models to degrade performance from basin to regional scale. </p><p>In the current study, we performed a computational expensive, basin-specific hyperparameter search to explore how site-specific LSTMs differ in performance compared to regionally calibrated LSTMs. We compared our results to the mHM and VIC models, once calibrated per-basin and once using an MPR regionalization scheme. These benchmark models were calibrated individual research groups, to eliminate bias in our study. We analyse whether differences in basin-specific vs regional model performance can be linked to basin attributes or data set characteristics.</p><p>References:</p><p>Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M.: Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks, Hydrol. Earth Syst. Sci., 22, 6005–6022, https://doi.org/10.5194/hess-22-6005-2018, 2018. </p><p>Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., and Nearing, G.: Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets, Hydrol. Earth Syst. Sci., 23, 5089–5110, https://doi.org/10.5194/hess-23-5089-2019, 2019a. </p><p>Kratzert, F., Klotz, D., Herrnegger, M., Sampson, A. K., Hochreiter, S., & Nearing, G. S.: Toward improved predictions in ungauged basins: Exploiting the power of machine learning. Water Resources Research, 55. https://doi.org/10.1029/2019WR026065, 2019b.</p>

scholarly journals Bayesian Long Short-Term Memory Model for Fault Early Warning of Nuclear Power Turbine

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 50801-50813 ◽  
Author(s):  
Gaojun Liu ◽  
Haixia Gu ◽  
Xiaocheng Shen ◽  
Dongdong You
Keyword(s):  
Early Warning ◽  
Nuclear Power ◽  
Short Term Memory ◽  
Memory Model ◽  
Short Term ◽  
Term Memory ◽  
Power Turbine ◽  
Long Short Term Memory

scholarly journals Explicit Knowledge-based Reasoning for Visual Question Answering

2017 ◽  
Author(s):  
Peng Wang ◽  
Qi Wu ◽  
Chunhua Shen ◽  
Anthony Dick ◽  
Anton van den Hengel
Keyword(s):  
Natural Language ◽  
Large Scale ◽  
Question Answering ◽  
Explicit Knowledge ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Visual Question Answering ◽  
Knowledge Based ◽  
Long Short Term Memory

We describe a method for visual question answering which is capable of reasoning about an image on the basis of information extracted from a large-scale knowledge base. The method not only answers natural language questions using concepts not contained in the image, but can explain the reasoning by which it developed its answer. It is capable of answering far more complex questions than the predominant long short-term memory-based approach, and outperforms it significantly in testing. We also provide a dataset and a protocol by which to evaluate general visual question answering methods.

A fast and accurate hybrid simulation model for the large-scale testing of automated driving functions

2019 ◽  
Vol 234 (4) ◽  
pp. 1183-1196 ◽  
Author(s):  
Nicolas Fraikin ◽  
Kilian Funk ◽  
Michael Frey ◽  
Frank Gauterin
Keyword(s):  
Simulation Model ◽  
Large Scale ◽  
Short Term Memory ◽  
Ground Truth ◽  
Computational Effort ◽  
Single Track ◽  
Automated Driving ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

The upcoming market introduction of highly automated driving functions and associated requirements on reliability and safety require new tools for the virtual test coverage to lower development expenses. In this contribution, a computationally efficient and accurate simulation environment for the vehicle’s lateral dynamics is introduced. Therefore, an analytic single track model is coupled with a long-short-term-memory neural network to compensate modelling inaccuracies of the single track model. This ‘Hybrid Vehicle Model’ is parameterized with selected training batches obtained from a complex simulation model serving as a reference to simplify the data acquisition. The single track model is parameterized using given catalogue data. Thereafter, the long-short-term-memory network is trained to cover for the single track model’s shortcomings compared to the ground truth in a closed-loop setup. The evaluation with measurements from the real vehicle shows that the hybrid model can provide accurate long-term predictions with low computational effort that outperform results achieved when using the models isolated.

scholarly journals Discovering microbe-disease associations from the literature using a hierarchical long short-term memory network and an ensemble parser model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yesol Park ◽  
Joohong Lee ◽  
Heesang Moon ◽  
Yong Suk Choi ◽  
Mina Rho
Keyword(s):  
Language Processing ◽  
Large Scale ◽  
Short Term Memory ◽  
Relation Extraction ◽  
Parse Tree ◽  
Short Term ◽  
Term Memory ◽  
Disease Associations ◽  
Memory Network ◽  
Long Short Term Memory

AbstractWith recent advances in biotechnology and sequencing technology, the microbial community has been intensively studied and discovered to be associated with many chronic as well as acute diseases. Even though a tremendous number of studies describing the association between microbes and diseases have been published, text mining methods that focus on such associations have been rarely studied. We propose a framework that combines machine learning and natural language processing methods to analyze the association between microbes and diseases. A hierarchical long short-term memory network was used to detect sentences that describe the association. For the sentences determined, two different parse tree-based search methods were combined to find the relation-describing word. The ensemble model of constituency parsing for structural pattern matching and dependency-based relation extraction improved the prediction accuracy. By combining deep learning and parse tree-based extractions, our proposed framework could extract the microbe-disease association with higher accuracy. The evaluation results showed that our system achieved an F-score of 0.8764 and 0.8524 in binary decisions and extracting relation words, respectively. As a case study, we performed a large-scale analysis of the association between microbes and diseases. Additionally, a set of common microbes shared by multiple diseases were also identified in this study. This study could provide valuable information for the major microbes that were studied for a specific disease. The code and data are available at https://github.com/DMnBI/mdi_predictor.

scholarly journals Early Warning of Internal Leakage in Heat Exchanger Network Based on Dynamic Mechanism Model and Long Short-Term Memory Method

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 378
Author(s):  
Wende Tian ◽  
Nan Liu ◽  
Dongwu Sui ◽  
Zhe Cui ◽  
Zijian Liu ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Heat Exchanger ◽  
Early Warning ◽  
Short Term Memory ◽  
Short Term ◽  
Heat Exchanger Network ◽  
Term Memory ◽  
Organic Matters ◽  
Mechanism Model ◽  
Long Short Term Memory

In the process of butadiene rubber production, internal leakage occurs in heat exchangers due to excessive pressure difference. It leads to the considerable flow of organic matters into the circulating water system. Since these organic matters are volatile and prone to explode in the cold water tower, internal leakage is potentially dangerous for the enterprise. To prevent this phenomenon, a novel intelligent early warning and risk assessment method (DYN-EW-QRA) is proposed in this paper by combining dynamic simulations (DYN), long short-term memory (LSTM), and quantitative risk assessment (QRA). First, an original internal leakage mechanism model of a heat exchanger network is designed and simulated by DYN to obtain datasets. Second, the potential relationships between variables that have a direct impact on the hazards of the accident are deeply learned by LSTM to predict the internal leakage trends. Finally, the QRA method is used to analyze the range and destructive power of potential hazards. The results show that DYN-EW-QRA method has excellent performance.

scholarly journals Comparison of Long Short-Term Memory Networks and Random Forest for Sentinel-1 Time Series Based Large Scale Crop Classification

2021 ◽  
Vol 13 (24) ◽  
pp. 5000
Author(s):  
Felix Reuß ◽  
Isabella Greimeister-Pfeil ◽  
Mariette Vreugdenhil ◽  
Wolfgang Wagner
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Large Scale ◽  
Short Term Memory ◽  
Meteorological Data ◽  
Learning Models ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Crop Classification

To ensure future food security, improved agricultural management approaches are required. For many of those applications, precise knowledge of the distribution of crop types is essential. Various machine and deep learning models have been used for automated crop classification using microwave remote sensing time series. However, the application of these approaches on a large spatial and temporal scale is barely investigated. In this study, the performance of two frequently used algorithms, Long Short-Term Memory (LSTM) networks and Random Forest (RF), for crop classification based on Sentinel-1 time series and meteorological data on a large spatial and temporal scale is assessed. For data from Austria, the Netherlands, and France and the years 2015–2019, scenarios with different spatial and temporal scales were defined. To quantify the complexity of these scenarios, the Fisher Discriminant measurement F1 (FDR1) was used. The results demonstrate that both classifiers achieve similar results for simple classification tasks with low FDR1 values. With increasing FDR1 values, however, LSTM networks outperform RF. This suggests that the ability of LSTM networks to learn long-term dependencies and identify the relation between radar time series and meteorological data becomes increasingly important for more complex applications. Thus, the study underlines the importance of deep learning models, including LSTM networks, for large-scale applications.

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