A Prediction Method of Groundwater Table Fluctuations in Landslide Area by Neural Networks.

Landslides ◽  
1995 ◽  
Vol 31 (4) ◽  
pp. 9-15 ◽  
Author(s):  
Hiroyuki YOSHIMATSU ◽  
Akira MUKAI
Keyword(s):  
Neural Networks ◽  
Prediction Method ◽  
Groundwater Table ◽  
Landslide Area

scholarly journals 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)

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1770
Author(s):  
Javier González-Enrique ◽  
Juan Jesús Ruiz-Aguilar ◽  
José Antonio Moscoso-López ◽  
Daniel Urda ◽  
Lipika Deka ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Short Term Memory ◽  
Prediction Method ◽  
Monitoring Network ◽  
Exogenous Variables ◽  
Prediction Horizon ◽  
Artificial Neural ◽  
Input Variables ◽  
Analytical Tools

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.

scholarly journals How to Predict Seawater Temperature for Sustainable Marine Aquaculture (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13887-13888
Author(s):  
Masahito Okuno ◽  
Takanobu Otsuka
Keyword(s):  
Neural Networks ◽  
Seawater Temperature ◽  
Prediction Method ◽  
Accurate Prediction ◽  
Average Error ◽  
Marine Aquaculture ◽  
Global Demand ◽  
Environment Control ◽  
Marine Products ◽  
Using Data

The increasing global demand for marine products has turned attention to marine aquaculture. In marine aquaculture, appropriate environment control is important for a stable supply. The influence of seawater temperature on this environment is significant and accurate prediction is therefore required. In this paper, we propose and describe the implementation of a seawater prediction method using data acquired from real aquaculture areas and neural networks. Our evaluation experiment showed that hourly next-day prediction has an average error of about 0.2 to 0.4 ◦C and daily prediction of up to one week has an average error of about 0.2 to 0.5 ◦C. This is enough to meet actual worker need, which is within 1 ◦C error, thus confirming that our seawater prediction method is suitable for actual sites.

scholarly journals A Novel Power Curve Prediction Method for Horizontal-Axis Wind Turbines Using Artificial Neural Networks

2021 ◽  
Vol 118 (3) ◽  
pp. 507-516
Author(s):  
Vin Cent Tai ◽  
Yong Chai Tan ◽  
Nor Faiza Abd Rahman ◽  
Chee Ming Chia ◽  
Mirzhakyp Zhakiya ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Turbines ◽  
Prediction Method ◽  
Horizontal Axis ◽  
Power Curve ◽  
Horizontal Axis Wind Turbines ◽  
Artificial Neural

scholarly journals Computational Prediction of Intrinsically Disordered Proteins Based on Protein Sequences and Convolutional Neural Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Hao He ◽  
Yong Yang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Intrinsically Disordered Proteins ◽  
Prediction Method ◽  
Computational Prediction ◽  
Disordered Proteins ◽  
Biological Functions ◽  
Intrinsically Disordered ◽  
Central Residue

Intrinsically disordered proteins (IDPs) possess at least one region that lacks a single stable structure in vivo, which makes them play an important role in a variety of biological functions. We propose a prediction method for IDPs based on convolutional neural networks (CNNs) and feature selection. The combination of sequence and evolutionary properties is used to describe the differences between disordered and ordered regions. Especially, to highlight the correlation between the target residue and adjacent residues, multiple windows are selected to preprocess the protein sequence through the selected properties. The shorter windows reflect the characteristics of the central residue, and the longer windows reflect the characteristics of the surroundings around the central residue. Moreover, to highlight the specificity of sequence and evolutionary properties, they are preprocessed, respectively. After that, the preprocessed properties are combined into feature matrices as the input of the constructed CNN. Our method is training as well as testing based on the DisProt database. The simulation results show that the proposed method can predict IDPs effectively, and the performance is competitive in comparison with IsUnstruct and ESpritz.

scholarly journals GraphProt2: A novel deep learning-based method for predicting binding sites of RNA-binding proteins

2019 ◽  
Author(s):  
Michael Uhl ◽  
Van Dinh Tran ◽  
Rolf Backofen
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Deep Learning ◽  
Binding Sites ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
State Of The Art ◽  
Prediction Method ◽  
Superior Performance

AbstractCLIP-seq is the state-of-the-art technique to experimentally determine transcriptome-wide binding sites of RNA-binding proteins (RBPs). However, it relies on gene expression which can be highly variable between conditions, and thus cannot provide a complete picture of the RBP binding landscape. This necessitates the use of computational methods to predict missing binding sites. Here we present GraphProt2, a computational RBP binding site prediction method based on graph convolutional neural networks (GCN). In contrast to current CNN methods, GraphProt2 supports variable length input as well as the possibility to accurately predict nucleotide-wise binding profiles. We demonstrate its superior performance compared to GraphProt and a CNN-based method on single as well as combined CLIP-seq datasets.

scholarly journals A Novel Remaining Useful Life Prediction Method for Hydrogen Fuel Cells Based on the Gated Recurrent Unit Neural Network

2022 ◽  
Vol 12 (1) ◽  
pp. 432
Author(s):  
Bing Long ◽  
Kunping Wu ◽  
Pengcheng Li ◽  
Meng Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuel Cells ◽  
Prediction Method ◽  
Remaining Useful Life ◽  
Hydrogen Fuel Cells ◽  
Hydrogen Fuel ◽  
Starting Point ◽  
Useful Life ◽  
Gated Recurrent Unit

The remaining useful life (RUL) prediction for hydrogen fuel cells is an important part of its prognostics and health management (PHM). Artificial neural networks (ANNs) are proven to be very effective in RUL prediction, as they do not need to understand the failure mechanisms behind hydrogen fuel cells. A novel RUL prediction method for hydrogen fuel cells based on the gated recurrent unit ANN is proposed in this paper. Firstly, the data were preprocessed to remove outliers and noises. Secondly, the performance of different neural networks is compared, including the back propagation neural network (BPNN), the long short-term memory (LSTM) network and the gated recurrent unit (GRU) network. According to our proposed method based on GRU, the root mean square error was 0.0026, the mean absolute percentage error was 0.0038 and the coefficient of determination was 0.9891 for the data from the challenge datasets provided by FCLAB Research Federation, when the prediction starting point was 650 h. Compared with the other RUL prediction methods based on the BPNN and the LSTM, our prediction method is better in both prediction accuracy and convergence rate.

scholarly journals Cocrystal Prediction by Artificial Neural Networks

2020 ◽  
Author(s):  
Jan-Joris Devogelaer ◽  
Hugo Meekes ◽  
Paul Tinnemans ◽  
Elias Vlieg ◽  
Rene de Gelder
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Prediction Method ◽  
Network Models ◽  
Data Driven ◽  
Neural Network Models ◽  
Design And Synthesis ◽  
Structural Database ◽  
Artificial Neural ◽  
Artificial Neural Network Models

<div>A significant amount of attention has been given to the design and synthesis of cocrystals by both industry and academia because of its potential to change a molecule’s physicochemical properties. This paper reports on the application of a data-driven cocrystal prediction method, based on two types of artificial neural network models and cocrystal data present in the Cambridge Structural Database. The models accept pairs of coformers and predict whether a cocrystal is likely to form.</div>

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