Conceptual Understanding of Convolutional Neural Network- A Deep Learning Approach

LSTM based long-term energy consumption prediction with periodicity

El Nino-Southern Oscillation (ENSO), which is one of the main drivers of Earth's inter-annual climate variability, often causes a wide range of climate anomalies, and the advance prediction of ENSO is always an important and challenging scientific issue. Since a unified and complete ENSO theory has yet to be established, people often use related indicators, such as the Nino 3.4 index and southern oscillation index (SOI), to predict the development trends of ENSO through appropriate numerical simulation models. However, because the ENSO phenomenon is a highly complex and dynamic model and the Nino 3.4 index and SOI mix many low- and high-frequency components, the prediction accuracy of current popular numerical prediction methods is not high. Therefore, this paper proposed the ensemble empirical mode decomposition-temporal convolutional network (EEMD-TCN) hybrid approach, which decomposes the highly variable Nino 3.4 index and SOI into relatively flat subcomponents and then uses the TCN model to predict each subcomponent in advance, finally combining the sub-prediction results to obtain the final ENSO prediction results. Nino 3.4 index and SOI reanalysis data from 1871 to 1973 were used for model training, and the data for 1984-2019 were predicted 1 month, 3 months, 6 months, and 12 months in advance. The results show that the accuracy of the 1-month-lead Nino 3.4 index prediction was the highest, the 12-month-lead SOI prediction was the slowest, and the correlation coefficient between the worst SOI prediction result and the actual value reached 0.6406. Furthermore, the overall prediction accuracy on the Nino 3.4 index was better than that on the SOI, which may have occurred because the SOI contains too many high-frequency components, making prediction difficult. The results of comparative experiments with the TCN, LSTM, and EEMD-LSTM methods showed that the EEMD-TCN provides the best overall prediction of both the Nino 3.4 index and SOI in the 1-, 3-, 6-, and 12-month-lead predictions among all the methods considered. This result means that the TCN approach performs well in the advance prediction of ENSO and will be of great guiding significance in studying it.

/pdf/temporal-convolutional-networks-for-the-advance-prediction-3094dh77t4.pdf

Temporal Convolutional Networks for the Advance Prediction of ENSO.

Short and mid-term sea surface temperature prediction using time-series satellite data and LSTM-AdaBoost combination approach

Sea surface temperature (SST) prediction is not only theoretically important but also has a number of practical applications across a variety of ocean-related fields. Although a large amount of SST data obtained via remote sensor are available, previous work rarely attempted to predict future SST values from history data in spatiotemporal perspective. This letter regards SST prediction as a sequence prediction problem and builds an end-to-end trainable long short term memory (LSTM) neural network model. LSTM naturally has the ability to learn the temporal relationship of time series data. Besides temporal information, spatial information is also included in our LSTM model. The local correlation and global coherence of each pixel can be expressed and retained by patches with fixed dimensions. The proposed model essentially combines the temporal and spatial information to predict future SST values. Its structure includes one fully connected LSTM layer and one convolution layer. Experimental results on two data sets, i.e., one Advanced Very High Resolution Radiometer SST data set covering China Coastal waters and one National Oceanic and Atmospheric Administration High-Resolution SST data set covering the Bohai Sea, confirmed the effectiveness of the proposed model.

A CFCC-LSTM Model for Sea Surface Temperature Prediction

This letter adopts long short-term memory (LSTM) to predict sea surface temperature (SST), and makes short-term prediction, including one day and three days, and long-term prediction, including weekly mean and monthly mean The SST prediction problem is formulated as a time series regression problem The proposed network architecture is composed of two kinds of layers: an LSTM layer and a full-connected dense layer The LSTM layer is utilized to model the time series relationship The full-connected layer is utilized to map the output of the LSTM layer to a final prediction The optimal setting of this architecture is explored by experiments and the accuracy of coastal seas of China is reported to confirm the effectiveness of the proposed method The prediction accuracy is also tested on the SST anomaly data In addition, the model’s online updated characteristics are presented

Prediction of Sea Surface Temperature Using Long Short-Term Memory

In this letter, we investigated how to apply a deep learning method, in particular convolutional neural networks (CNNs), to an ocean front recognition task. Exploring deep CNNs knowledge to ocean front recognition is a challenging task, because the training data is very scarce. This letter overcomes this challenge using a sequence of transfer learning steps via fine-tuning. The core idea is to extract deep knowledge of the CNN model from a large data set and then transfer the knowledge to our ocean front recognition task on limited remote sensing (RS) images. We conducted experiments on two different RS image data sets, with different visual properties, i.e., colorful and gray-level data, which were both downloaded from the National Oceanic and Atmospheric Administration (NOAA). The proposed method was compared with the conventional handcraft descriptor with bag-of-visual-words, original CNN model, and last-layer fine-tuned CNN model. Our method showed a significantly higher accuracy than other methods in both datasets.

Learning and Transferring Convolutional Neural Network Knowledge to Ocean Front Recognition

The aquaculture of the marine organism affects the flow of the sea to a certain extent. Thus, it also affects the water exchange rate and reduces the supplementary of nutrition salt, which accordingly restricts the growth of marine organisms to some extent. Therefore, studying the seawater resistance produced by the marine organisms in the sea has a practical significance. However, the drag coefficient of the seawater resistance produced by the marine organisms is unknown. The least squares method is a mathematical optimization technique to minimize the square of the error and find the best function matching with a set of data. That is, using an easiest way to obtain the absolutely unknowable true value which makes the error sum of squares minimum. In this paper, we used the least squares method to fit the drag coefficient of the seawater resistance in the ocean model POM to estimate its true value, and then with the coefficient we can determine the exact value of the seawater resistance.

Estimating the Ocean Surface Drag Coefficient Based on the Least Squares Method

Sea water exchange is an important source of nutrient salts. Culture living beings such as kelp in a sea-farming region will bring resistance to the exchange of water flows and therefore affect nutrient supplement. Study of hydrodynamic environment in marine culture zones is important for revealing water exchange conditions and guiding reasonable layout of mariculture regions. In recent years, statistical learning theories represented by Support Vector Machines (SVM) have been well developed. However, no publications are available regarding using SVM to predict marine environment elements related to hydrodynamic and combining these predicted elements with ocean models. In this paper, we use SVM regression to predict water flow velocity based on an improved hydrodynamic models with the resistance by cultivation breeding such as kelp. In particular, we use SVM regression to predict the velocity of following time points at a location with the coordinate in north-south and east-west directions. The experimental results are promising.

Predicting flow velocity affected by seaweed resistance using SVM regression

This paper introduces several methods in solving the wave equations for a loudspeaker. The separation of variables method is very common for solving constant coefficient linear partial differential problems. The finite difference method has been the main method of numerical computation in early work. The direct method is to formulate the boundary integral equation and can be solved using a direct time integration procedure. There are also some transform methods such as Fourier, Laplaces and wavelet transforms etc. to solve the wave equations. The retarded potential technique to solve the wave problems numerically was first used in the early 1960s. Since then, many authors have worked on the method and its applications.

/pdf/methods-in-solving-the-wave-equations-for-a-loudspeaker-2qc7e86rcx.pdf

Methods in Solving the Wave Equations for A Loudspeaker

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