Water Quality Prediction in the Luan River Based on 1-DRCNN and BiGRU Hybrid Neural Network Model

Yan, Jianzhuo; Liu, Jiaxue; Yu, Yongchuan; Xu, Hongxia

doi:10.3390/w13091273

Cited by 54 publications

(35 citation statements)

References 32 publications

(36 reference statements)

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“…Such solutions combining data-driven and theory-driven approaches are considered hybrid. Other works have focused on hybrid neural networks but in terms of the combination of multiple neural architectures (Chen and Zhang, 2021;Sharma et al, 2021;Yan et al, 2021). Towell and Shavlik (1994) defined hybrid learning techniques as "methods that use theoretical knowledge of a domain and a set of examples to develop a method for accurately classifying unseen examples during training."…”

Section: Hybrid Neural Networkmentioning

confidence: 99%

Infusing Expert Knowledge Into a Deep Neural Network Using Attention Mechanism for Personalized Learning Environments

Tato

Nkambou

2022

Front. Artif. Intell.

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Machine learning models are biased toward data seen during the training steps. The models will tend to give good results in classes where there are many examples and poor results in those with few examples. This problem generally occurs when the classes to predict are imbalanced and this is frequent in educational data where for example, there are skills that are very difficult or very easy to master. There will be less data on students that correctly answered questions related to difficult skills and who incorrectly answered those related to skills easy to master. In this paper, we tackled this problem by proposing a hybrid architecture combining Deep Neural Network architectures— especially Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN)—with expert knowledge for user modeling. The proposed solution uses attention mechanism to infuse expert knowledge into the Deep Neural Network. It has been tested in two contexts: knowledge tracing in an intelligent tutoring system (ITS) called Logic-Muse and prediction of socio-moral reasoning in a serious game called MorALERT. The proposed solution is compared to state-of-the-art machine learning solutions and experiments show that the resulting model can accurately predict the current student's knowledge state (in Logic-Muse) and thus enable an accurate personalization of the learning process. Other experiments show that the model can also be used to predict the level of socio-moral reasoning skills (in MorALERT). Our findings suggest the need for hybrid neural networks that integrate prior expert knowledge (especially when it is necessary to compensate for the strong dependency—of deep learning methods—on data size or the possible unbalanced datasets). Many domains can benefit from such an approach to building models that allow generalization even when there are small training data.

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Section: Hybrid Neural Networkmentioning

confidence: 99%

Infusing Expert Knowledge Into a Deep Neural Network Using Attention Mechanism for Personalized Learning Environments

Tato

Nkambou

2022

Front. Artif. Intell.

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“…Liu et al ( 2021 ) used GMM-HMM and LSTM to predict stock prices. BiGRU and LSTM have several applications outside the financial sector (Talha et al, 2017 ; Yan et al, 2021 ).…”

Section: Related Workmentioning

confidence: 99%

Constructing Equity Investment Strategies Using Analyst Reports and Regime Switching Models

Taguchi

Watanabe

Sakaji

et al. 2022

Front. Artif. Intell.

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This study demonstrates whether analysts' sentiments toward individual stocks are useful for stock investment strategies. This is achieved by using natural language processing to create a polarity index from textual information in analyst reports. In this study, we performed time series forecasting for the created polarity index using deep learning, and clustered the forecasted values by volatility using a regime switching model. In addition, we constructed a portfolio from stock data and rebalanced it at each change point of the regime. Consequently, the investment strategy proposed in this study outperforms the benchmark portfolio in terms of returns. This suggests that the polarity index is useful for constructing stock investment strategies.

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“…The training gradient and activation function parameters of the GRNN were the same as those of the Elman model. To compare the performance of the models, this paper used Sklearn lib based on Python to analyze the data and RMSE, R 2 , and mean absolute percentage error (MAPE) criteria [43] which were commonly applied in agricultural research to evaluate the effect of the soil moisture prediction of the litchi orchard. The equations are expressed as:…”

Section: Generalized Regression Neural Network (Grnn) Modelmentioning

confidence: 99%

Modeling for the Prediction of Soil Moisture in Litchi Orchard with Deep Long Short-Term Memory

et al. 2021

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Soil moisture is an important factor determining yield. With the increasing demand for agricultural irrigation water resources, evaluating soil moisture in advance to create a reasonable irrigation schedule would help improve water resource utilization. This paper established a continuous system for collecting meteorological information and soil moisture data from a litchi orchard. With the acquired data, a time series model called Deep Long Short-Term Memory (Deep-LSTM) is proposed in this paper. The Deep-LSTM model has five layers with the fused time series data to predict the soil moisture of a litchi orchard in four different growth seasons. To optimize the data quality of the soil moisture sensor, the Symlet wavelet denoising algorithm was applied in the data preprocessing section. The threshold of the wavelets was determined based on the unbiased risk estimation method to obtain better sensor data that would help with the model learning. The results showed that the root mean square error (RMSE) values of the Deep-LSTM model were 0.36, 0.52, 0.32, and 0.48%, and the mean absolute percentage error (MAPE) values were 2.12, 2.35, 1.35, and 3.13%, respectively, in flowering, fruiting, autumn shoots, and flower bud differentiation stages. The determination coefficients (R2) were 0.94, 0.95, 0.93, and 0.94, respectively, in the four different stages. The results indicate that the proposed model was effective at predicting time series soil moisture data from a litchi orchard. This research was meaningful with regards to acquiring the soil moisture characteristics in advance and thereby providing a valuable reference for the litchi orchard’s irrigation schedule.

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Water Quality Prediction in the Luan River Based on 1-DRCNN and BiGRU Hybrid Neural Network Model

Cited by 54 publications

References 32 publications

Infusing Expert Knowledge Into a Deep Neural Network Using Attention Mechanism for Personalized Learning Environments

Infusing Expert Knowledge Into a Deep Neural Network Using Attention Mechanism for Personalized Learning Environments

Constructing Equity Investment Strategies Using Analyst Reports and Regime Switching Models

Modeling for the Prediction of Soil Moisture in Litchi Orchard with Deep Long Short-Term Memory

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