A ConvLSTM Conjunction Model for Groundwater Level Forecasting in a Karst Aquifer Considering Connectivity Characteristics

Guo, Fang; Li, Hu; Li, Gang; Zhang, Zhuo

doi:10.3390/w13192759

Cited by 7 publications

(4 citation statements)

References 40 publications

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“…First, it is illustrated how the number of CNN layers impacts the performance of the prediction systems. Second, the results from proposed method compared with DeepYield [4], ConvLSTM [49], 3DCNN [50] and CNN-LSTM [38].…”

Section: Resultsmentioning

confidence: 99%

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Multispectral Crop Yield Prediction Using 3D-Convolutional Neural Networks and Attention Convolutional LSTM Approaches

Nejad

Abbasi‐Moghadam

Sharifi

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In recent years, national economies are highly affected by Crop Yield Predictions. By early prediction, the market price can be predicted, importing and exporting plan can be provided, social and economic effects of waste products can be minimized, and a program can be presented for humanitarian food aid. In addition, agricultural fields are constantly growing to generate products required. The use of machine learning methods in this sector can lead to the efficient production and highquality agricultural products. Traditional predictive machine models were unable to find non-linear relationships between data. Recently, there has been a revolution in prediction systems via the advancement of machine learning which can be used to achieve highly accurate decision-making networks. Thus far, many strategies have been used to evaluate agricultural products, such as DeepYield, CNN-LSTM, and ConvLSTM. However, preferable prediction accuracy is required. In this study, two architectures have been proposed. The first model includes 2D-CNN, skip connections, and LSTM-Attentions. The second model comprises 3D-CNN, skip connections, and ConvLSTM Attention. The Input data given from MODIS products such as Land-Cover, Surface-Temperature, and MODIS-Land-surface from 2003 to 2018 on the county level over 1800 counties where soybean is mainly cultivated in the USA. The proposed methods have been compared with the most recent models. Then, the results showed that the second proposed method notably outperformed the other techniques. In case of MAE, the second proposed method,

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Section: Resultsmentioning

confidence: 99%

“…4) Crop Yield Perdition Comparing Approach: The results from proposed methodologies compared with DeepYield [4], ConvLSTM [49], 3DCNN [50], and CNN-LSTM [38]. Simulations have been repeated in the same condition for all competitive methods to have a fair comparison.…”

Section: Resultsmentioning

confidence: 99%

Multispectral Crop Yield Prediction Using 3D-Convolutional Neural Networks and Attention Convolutional LSTM Approaches

Nejad

Abbasi‐Moghadam

Sharifi

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The results are underwhelming when it comes to the actual performance of the models in this study compared to previous studies. For instance, Malakar, Wunch, Solgi, Afan and Guo [25,26,39,61,62], all report far superior performance scores across several metrics for deep learning models including LSTMs. Even the GBDT model in this study demonstrated much lower performance scores than previous studies using classical and ensemble machine learning approaches.…”

Section: Discussionmentioning

confidence: 99%

A Comparison of Ensemble and Deep Learning Algorithms to Model Groundwater Levels in a Data-Scarce Aquifer of Southern Africa

et al. 2022

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Machine learning and deep learning have demonstrated usefulness in modelling various groundwater phenomena. However, these techniques require large amounts of data to develop reliable models. In the Southern African Development Community, groundwater datasets are generally poorly developed. Hence, the question arises as to whether machine learning can be a reliable tool to support groundwater management in the data-scarce environments of Southern Africa. This study tests two machine learning algorithms, a gradient-boosted decision tree (GBDT) and a long short-term memory neural network (LSTM-NN), to model groundwater level (GWL) changes in the Shire Valley Alluvial Aquifer. Using data from two boreholes, Ngabu (sample size = 96) and Nsanje (sample size = 45), we model two predictive scenarios: (I) predicting the change in the current month’s groundwater level, and (II) predicting the change in the following month’s groundwater level. For the Ngabu borehole, GBDT achieved R2 scores of 0.19 and 0.14, while LSTM achieved R2 scores of 0.30 and 0.30, in experiments I and II, respectively. For the Nsanje borehole, GBDT achieved R2 of −0.04 and −0.21, while LSTM achieved R2 scores of 0.03 and −0.15, in experiments I and II, respectively. The results illustrate that LSTM performs better than the GBDT model, especially regarding slightly greater time series and extreme GWL changes. However, closer inspection reveals that where datasets are relatively small (e.g., Nsanje), the GBDT model may be more efficient, considering the cost required to tune, train, and test the LSTM model. Assessing the full spectrum of results, we concluded that these small sample sizes might not be sufficient to develop generalised and reliable machine learning models.

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“…Nevertheless, there was a study to predict river flow rates with high accuracy by providing guidelines for input training data [4]. In addition, real-time groundwater-level prediction [29], extreme water-level prediction by highest tide [30,31], and real-time urban-river-flood-level prediction, using various DNN models, have been effectively performed recently [32]. However, until recently, there were not many studies to improve the accuracy of predicting water levels with very high temporal variability.…”

Section: Introductionmentioning

confidence: 99%

Development of Deep Learning Models to Improve the Accuracy of Water Levels Time Series Prediction through Multivariate Hydrological Data

Park

Jung

Seong

et al. 2022

Water

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Since predicting rapidly fluctuating water levels is very important in water resource engineering, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) were used to evaluate water-level-prediction accuracy at Hangang Bridge Station in Han River, South Korea, where seasonal fluctuations were large and rapidly changing water levels were observed. The hydrological data input to each model were collected from the Water Resources Management Information System (WAMIS) at the Hangang Bridge Station, and the meteorological data were provided by the Seoul Observatory of the Meteorological Administration. For high-accuracy high-water-level prediction, the correlation between water level and collected hydrological and meteorological data was analyzed and input into the models to determine the priority of the data to be trained. Multivariate input data were created by combining daily flow rate (DFR), daily vapor pressure (DVP), daily dew-point temperature (DDPT), and 1-hour-max precipitation (1HP) data, which are highly correlated with the water level. It was possible to predict improved high water levels through the training of multivariate input data of LSTM and GRU. In the prediction of water-level data with rapid temporal fluctuations in the Hangang Bridge Station, the accuracy of GRU’s predicted water-level data was much better in most multivariate training than that of LSTM. When multivariate training data with a large correlation with the water level were used by the GRU, the prediction results with higher accuracy (R2=0.7480−0.8318; NSE= 0.7524−0.7965; MRPE= 0.0807−0.0895) were obtained than those of water-level prediction results by univariate training.

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A ConvLSTM Conjunction Model for Groundwater Level Forecasting in a Karst Aquifer Considering Connectivity Characteristics

Cited by 7 publications

References 40 publications

Multispectral Crop Yield Prediction Using 3D-Convolutional Neural Networks and Attention Convolutional LSTM Approaches

Multispectral Crop Yield Prediction Using 3D-Convolutional Neural Networks and Attention Convolutional LSTM Approaches

A Comparison of Ensemble and Deep Learning Algorithms to Model Groundwater Levels in a Data-Scarce Aquifer of Southern Africa

Development of Deep Learning Models to Improve the Accuracy of Water Levels Time Series Prediction through Multivariate Hydrological Data

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