Outflow sediment concentration forecasting by integrating machine learning approaches and time series analysis in reservoir desilting operation

2020 ◽  
Vol 34 (6) ◽  
pp. 849-866
Author(s):  
Ming-Jui Chang ◽  
Gwo-Fong Lin ◽  
Fong-Zuo Lee ◽  
Yi-Cheng Wang ◽  
Peng-An Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Analysis ◽  
Sediment Concentration ◽  
Learning Approaches ◽  
Series Analysis

scholarly journals Time Series-Analysis Based Engineering of High-Dimensional Wide-Area Stability Indices for Machine Learning

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Raoult Teukam Dabou ◽  
Innocent Kamwa ◽  
C. Y. Chung ◽  
C. F. Mugombozi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Analysis ◽  
Wide Area ◽  
High Dimensional ◽  
Series Analysis ◽  
Stability Indices

Pre-treating GNSS time series using a recurrent neural network to improve the automated detection of jump discontinuities

2021 ◽  
Author(s):  
Luca Tavasci ◽  
Pasquale Cascarano ◽  
Stefano Gandolfi
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Analysis ◽  
Ad Hoc ◽  
Short Term Memory ◽  
Learning Approaches ◽  
Jump Detection ◽  
Series Analysis ◽  
Jump Discontinuities ◽  
Gnss Time Series

<p>Ground motion monitoring is one of the main goals in the geoscientist community and at the time it is mainly performed by analyzing time series of data. Our capability of describing the most significant features characterizing the time evolution of a point-position is affected by the presence of undetected discontinuities in the time series. One of the most critical aspects in the automated time series analysis, which is quite necessary since the amount of data is increasing more and more, is still the detection of discontinuities and in particular the definition of their epoch. A number of algorithms have already been developed and proposed to the community in the last years, following different statistical approaches and different hypotheses on the coordinates behavior. In this work, we have chosen to analyze GNSS time series and to use an already published algorithm (STARS) for jump detection as a benchmark to test our approach, consisting of pre-treating the time series to be analyzed using a neural network. In particular, we chose a Long Short Term Memory (LSTM) neural network belonging to the class of the Recurrent Neural Networks (RNNs), ad hoc modified for the GNSS time series analysis. We focused both on the training algorithm and the testing one. The latter has been the object of a parametric test to find out the number of predicted data that mostly emphasize our capability of detecting jump discontinuities. Results will be presented considering several GNSS time series of daily positions. Finally, a discussion on the possible integration of machine learning approaches and classical deterministic approaches will be done.</p>

scholarly journals Machine Learning Methods for Improved Understanding of a Pumping Test in Heterogeneous Aquifers

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1342 ◽  
Author(s):  
Yong Fan ◽  
Litang Hu ◽  
Hongliang Wang ◽  
Xin Liu
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Analysis ◽  
Analytical Solutions ◽  
Pumping Test ◽  
Learning Methods ◽  
Pumping Tests ◽  
Machine Learning Methods ◽  
Series Analysis ◽  
Pumping Wells

Pumping tests are very important means for investigating aquifer properties; however, interpreting the data using common analytical solutions become invalid in complex aquifer systems. The paper aims to explore the potential of machine learning methods in retrieving the pumping tests information in a field site in the Democratic Republic of Congo. A newly planned mining site with a pumping test of three pumping wells and 28 observation wells over one month was chosen to analyze the significance of machine learning methods in the pumping test analysis. Widely used machine learning methods, including correlation, cluster, time-series analysis, artificial neural network (ANN), support vector machine (SVR), random forest (RF) method, and linear regression, are all used in this study. Correlation and cluster analyses among wells provide visual pictures of possible hydraulic connections. The pathway with the best permeability ranges from the depth of 250 m to 350 m. Time-series analysis perfectly captured changes of drawdowns within the three pumping wells. The RF method is found to have the higher accuracy and the lower sensitivity to model parameters than ANN and SVR methods. The coupling of the linear regressive model and analytical solutions is applied to estimate hydraulic conductivities. The results found that ML methods can significantly and effectively improve our understanding of pumping tests by revealing inherent information hidden in those tests.

scholarly journals Time-Series Growth Prediction Model Based on U-Net and Machine Learning in Arabidopsis

2021 ◽  
Vol 12 ◽  
Author(s):  
Sungyul Chang ◽  
Unseok Lee ◽  
Min Jeong Hong ◽  
Yeong Deuk Jo ◽  
Jin-Baek Kim
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Developmental Stages ◽  
Series Data ◽  
Future Application ◽  
Yield Prediction ◽  
Flowering Stage ◽  
Series Analysis

Yield prediction for crops is essential information for food security. A high-throughput phenotyping platform (HTPP) generates the data of the complete life cycle of a plant. However, the data are rarely used for yield prediction because of the lack of quality image analysis methods, yield data associated with HTPP, and the time-series analysis method for yield prediction. To overcome limitations, this study employed multiple deep learning (DL) networks to extract high-quality HTTP data, establish an association between HTTP data and the yield performance of crops, and select essential time intervals using machine learning (ML). The images of Arabidopsis were taken 12 times under environmentally controlled HTPP over 23 days after sowing (DAS). First, the features from images were extracted using DL network U-Net with SE-ResXt101 encoder and divided into early (15–21 DAS) and late (∼21–23 DAS) pre-flowering developmental stages using the physiological characteristics of the Arabidopsis plant. Second, the late pre-flowering stage at 23 DAS can be predicted using the ML algorithm XGBoost, based only on a portion of the early pre-flowering stage (17–21 DAS). This was confirmed using an additional biological experiment (P < 0.01). Finally, the projected area (PA) was estimated into fresh weight (FW), and the correlation coefficient between FW and predicted FW was calculated as 0.85. This was the first study that analyzed time-series data to predict the FW of related but different developmental stages and predict the PA. The results of this study were informative and enabled the understanding of the FW of Arabidopsis or yield of leafy plants and total biomass consumed in vertical farming. Moreover, this study highlighted the reduction of time-series data for examining interesting traits and future application of time-series analysis in various HTPPs.

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