Spatiotemporal forecasting for groundwater level using a WT-LSTM model

2020 ◽  
Author(s):  
wei qin ◽  
Chengpeng Lu ◽  
Long Sun ◽  
Jiayun Lu
Keyword(s):  
Neural Network ◽  
Groundwater Level ◽  
Wavelet Transforms ◽  
Groundwater Resources ◽  
Arima Model ◽  
Discrete Wavelet ◽  
Linear Regression Method ◽  
Ann Model ◽  
Periodic Term ◽  
Proposed Model

<p>Accurate groundwater level forecasting models is essential to ensure the sustainable utilization and efficient protection of groundwater resources. In this paper, a novel method for groundwater level forecasting is proposed on the basis of coupling discrete wavelet transforms (WT) and long and short term memory neural network (LSTM) . In this model, the wavelet transform is used to decompose the cumulative displacement into the term of trend and term of periodicity . The trend term reflects the long-term tendency of groundwater level variation, which is simulated by a linear regression method. The periodic term driven by external factors such as rainfall, the river stage and the distance from river, is modelled using a LSTM method. The distance from river and the distance from observation wells are used for spatiotemporal model interpretation. Finally, the trend term and periodic term are superposed to achieve the cumulative spatiotemporal prediction of groundwater level. A typical study area located in Haihe basin is taken as an example to validate the performance of the proposed model. The proposed mode (WT-LSTM) is compared with the regular artificial neural network (ANN) model and autoregressive integrated moving average (ARIMA) model. The results show that the prediction accuracy of WT-LSTM model is higher than ANN model and ARIMA model, especially during the flood period. Furthermore, the spatiotemporal groundwater level forecasting is not only included the observation of groundwater and precipitation, but should also take the influence factors of surface water into consideration. The proposed model gives a new sight in the prediction of groundwater level.</p>

scholarly journals Artificial Neural Network Optimized with a Genetic Algorithm for Seasonal Groundwater Table Depth Prediction in Uttar Pradesh, India

2020 ◽  
Vol 12 (21) ◽  
pp. 8932
Author(s):  
Kusum Pandey ◽  
Shiv Kumar ◽  
Anurag Malik ◽  
Alban Kuriqi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Groundwater Level ◽  
Groundwater Resources ◽  
Uttar Pradesh ◽  
Groundwater Table ◽  
Post Monsoon ◽  
Groundwater Table Depth ◽  
Ann Models

Accurate information about groundwater level prediction is crucial for effective planning and management of groundwater resources. In the present study, the Artificial Neural Network (ANN), optimized with a Genetic Algorithm (GA-ANN), was employed for seasonal groundwater table depth (GWTD) prediction in the area between the Ganga and Hindon rivers located in Uttar Pradesh State, India. A total of 18 models for both seasons (nine for the pre-monsoon and nine for the post-monsoon) have been formulated by using groundwater recharge (GWR), groundwater discharge (GWD), and previous groundwater level data from a 21-year period (1994–2014). The hybrid GA-ANN models’ predictive ability was evaluated against the traditional GA models based on statistical indicators and visual inspection. The results appraisal indicates that the hybrid GA-ANN models outperformed the GA models for predicting the seasonal GWTD in the study region. Overall, the hybrid GA-ANN-8 model with an 8-9-1 structure (i.e., 8: inputs, 9: neurons in the hidden layer, and 1: output) was nominated optimal for predicting the GWTD during pre- and post-monsoon seasons. Additionally, it was noted that the maximum number of input variables in the hybrid GA-ANN approach improved the prediction accuracy. In conclusion, the proposed hybrid GA-ANN model’s findings could be readily transferable or implemented in other parts of the world, specifically those with similar geology and hydrogeology conditions for sustainable planning and groundwater resources management.

scholarly journals Gated Recurrent Unit Networks and Discrete Wavelet Transforms Applied to Forecasting and Trading in the Stock Market

2020 ◽  
Author(s):  
Victor Biazon ◽  
Reinaldo Bianchi
Keyword(s):  
Stock Market ◽  
Wavelet Transforms ◽  
Stock Price ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transforms ◽  
Time Step ◽  
Proposed Model ◽  
Gated Recurrent Unit ◽  
Novel Model ◽  
Unit Network

Trading in the stock market always comes with the challenge of deciding the best action to take on each time step. The problem is intensified by the theory that it is not possible to predict stock market time series as all information related to the stock price is already contained in it. In this work we propose a novel model called Discrete Wavelet Transform Gated Recurrent Unit Network (DWT-GRU). The model learns from the data to choose between buying, holding and selling, and when to execute them. The proposed model was compared to other recurrent neural networks, with and without wavelets preprocessing, and the buy and hold strategy. The results shown that the DWT-GRU outperformed all the set baselines in the analysed stocks of the Brazilian stock market.

A WAVELET-BASED SPEAKER VERIFICATION ALGORITHM

2010 ◽  
Vol 08 (06) ◽  
pp. 905-912 ◽  
Author(s):  
MICHEL ALVES LACERDA ◽  
RODRIGO CAPOBIANCO GUIDO ◽  
LEONARDO MENDES DE SOUZA ◽  
PAULO RICARDO FRANCHI ZULATO ◽  
JUSSARA RIBEIRO ◽  
...  
Keyword(s):  
Neural Network ◽  
Wavelet Transforms ◽  
Finite Impulse Response ◽  
Speaker Verification ◽  
Fir Filters ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transforms ◽  
Verification Algorithm ◽  
Support Size ◽  
Artificial Neural Network Ann

This paper presents a study on wavelets and their characteristics for the specific purpose of serving as a feature extraction tool for speaker verification (SV), considering a Radial Basis Function (RBF) classifier, which is a particular type of Artificial Neural Network (ANN). Examining characteristics such as support-size, frequency and phase responses, amongst others, we show how Discrete Wavelet Transforms (DWTs), particularly the ones which derive from Finite Impulse Response (FIR) filters, can be used to extract important features from a speech signal which are useful for SV. Lastly, an SV algorithm based on the concepts presented is described.

scholarly journals Neural Network Characterization of Reflectarray Antennas

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Angelo Freni ◽  
Marco Mussetta ◽  
Paola Pirinoli
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimization Procedure ◽  
Antenna Design ◽  
Ann Model ◽  
Design And Optimization ◽  
Full Wave ◽  
Proposed Model ◽  
Artificial Neural Network Ann

An efficient artificial neural network (ANN) approach for the modeling of reflectarray elementary components is introduced to improve the numerical efficiency of the different phases of the antenna design and optimization procedure, without loss in accuracy. The comparison between the results of the analysis of the entire reflectarray designed using the simplified ANN model or adopting a full-wave characterization of the unit cell finally proves the effectiveness of the proposed model.

scholarly journals Forecasting Rice Production of Bangladesh Using ARIMA and Artificial Neural Network Models

2020 ◽  
Vol 68 (2) ◽  
pp. 143-147
Author(s):  
Abira Sultana ◽  
Murshida Khanam
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Time Series Data ◽  
Arima Model ◽  
Rice Production ◽  
Series Data ◽  
Ann Model ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Estimated Error

Forecasting behavior of Econometric and Machine Learning models has recently attracted much attention in the research sector. In this study an attempt has been made to compare the forecasting behavior of Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) using univariate time series data of annual rice production (1972 to 2013) of Bangladesh. Here, suitable ARIMA has been chosen from several selected ARIMA models with the help of AIC and BIC values. A simple ANN model using backpropagation algorithm with appropriate number of nodes or neurons in a single hidden layer, adjustable threshold value and learning rate, has been constructed. Based on the RMSE, MAE and MAPE values, the results showed that the estimated error of ANN is much higher than the estimated error of chosen ARIMA. So, according to this study, it can be said that the ARIMA model is better than ANN model for forecasting the rice production in Bangladesh. Dhaka Univ. J. Sci. 68(2): 143-147, 2020 (July)

scholarly journals Prediction of groundwater level in basement complex terrain using artificial neural network: a case of Ijebu-Jesa, southwestern Nigeria

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
K. A. N. Adiat ◽  
O. F. Ajayi ◽  
A. A. Akinlalu ◽  
I. B. Tijani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Groundwater Level ◽  
Complex Terrain ◽  
Regression Coefficient ◽  
Empirical Relationship ◽  
Electrode Spacing ◽  
Ann Model ◽  
Basement Complex ◽  
Artificial Neural

AbstractEmpirical relationship between geoelectric parameters and groundwater level in boreholes/wells has not been established. Also, prediction of groundwater level from geoelectric parameters had hitherto not been reported. In order to overcome these challenges, the capability of artificial neural network (ANN) to model nonlinear system was explored in this study to predict groundwater level from geoelectric parameters. To achieve the above objectives, the ground water level (GWL) of all the accessible wells in the study area was obtained and this was used as the output parameter for the ANN model. A total of fifty-one (51) parametric vertical electrical soundings (VES) stations were occupied at each of the well location by adopting Schlumberger array configuration with electrode spacing (AB/2) ranging from 1 to 100 m. The VES data were quantitatively interpreted to generate geoelectric parameters believed to be controlling the groundwater flow and storage in the area. These parameters served as input for ANN model. The capability of ANN as a nonlinear modeling system was thereafter applied to produce a model that can predict the GWL from the input parameters. The efficiency of the model was evaluated by estimating the mean square error (MSE) and the regression coefficient (R) for the model. The results established that seasonal variation has little effect on the water fluctuation in the wells. Two aquifer types, weathered and fractured basement aquifer types, were delineated in the area. The results of the ANN model validation showed low MSE of 0.0014286 and the high regression coefficient (R) of 0.98731. This indicates that ANN can be used to predict GWL in a basement complex terrain with reasonably good accuracy. It is concluded that the ANN can effectively predict GWL from geoelectric parameters.

Comparative Study between Wavelet Artificial Neural Network (WANN) and Artificial Neural Network (ANN) Models for Groundwater Level Forecasting

2019 ◽  
Author(s):  
Ananda Kumar ◽  
B Maheshwara Babu ◽  
U Satish Kumar ◽  
G.V Srinivasa Reddy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Groundwater Level ◽  
Model Performance ◽  
Substantial Improvement ◽  
Ann Model ◽  
Groundwater Level Fluctuations ◽  
Calibration And Validation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Groundwater level fluctuation modeling is a prime need for effective utilization and planning the conjunctive use in any basin.The application of Artificial Neural Network (ANN) and hybrid Wavelet ANN (WANN) models was investigated in predicting Groundwater level fluctuations. The RMSE of ANN model during calibration and validation were found to be 0.2868 and 0.3648 respectively, whereas for the WANN model the respective values were 0.1946 and 0.1695. Efficiencies during calibration and validation for ANN model were 0.8862 per cent and 0.8465 per cent respectively, whereas for WANN model were found to be much higher with the respective values of 0.9436 per cent and 0.9568 per cent indicating substantial improvement in the model performance. Hence hybrid ANN model is the promising tool to predict water table fluctuation as compared to ANN model.

scholarly journals Wavelet analysis–artificial neural network conjunction models for multi-scale monthly groundwater level predicting in an arid inland river basin, northwestern China

2016 ◽  
Vol 48 (6) ◽  
pp. 1710-1729 ◽  
Author(s):  
Xiaohu Wen ◽  
Qi Feng ◽  
Ravinesh C. Deo ◽  
Min Wu ◽  
Jianhua Si
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Analysis ◽  
River Basin ◽  
Groundwater Level ◽  
Climatic Data ◽  
Ann Model ◽  
Multi Scale ◽  
Inland River ◽  
Inland River Basin

Abstract In this study, the ability of a wavelet analysis–artificial neural network (WA-ANN) conjunction model for multi-scale monthly groundwater level forecasting was evaluated in an arid inland river basin, northwestern China. The WA-ANN models were obtained by combining discrete wavelet transformation with ANN. For WA-ANN model, three different input combinations were trialed in order to optimize the model performance: (1) ancient groundwater level only, (2) ancient climatic data, and (3) ancient groundwater level combined with climatic data to forecast the groundwater level for two wells in Zhangye basin. Based on the key statistical measures, the performance of the WA-ANN model was significantly better than ANN model. However, WA-ANN model with ancient groundwater level as its input yielded the best performance for 1-month groundwater forecasts. For 2- and 3-monthly forecasts, the performance of the WA-ANN model with integrated ancient groundwater level and climatic data as inputs was the most superior. Notwithstanding this, the WA-ANN model with only ancient climatic data as its inputs also exhibited accurate results for 1-, 2-, and 3-month groundwater forecasting. It is ascertained that the WA-ANN model is a useful tool for simulation of multi-scale groundwater forecasting in the current study region.

scholarly journals Conjunction of SOM-based feature extraction method and hybrid wavelet-ANN approach for rainfall–runoff modeling

2013 ◽  
Vol 15 (3) ◽  
pp. 829-848 ◽  
Author(s):  
Vahid Nourani ◽  
Masoumeh Parhizkar
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Model Building ◽  
Self Organizing Map ◽  
Rainfall Runoff ◽  
Ann Model ◽  
Two Stage ◽  
Runoff Modeling ◽  
Proposed Model ◽  
Runoff Discharge

In rainfall–runoff modeling, the wavelet-ANN model, which includes a wavelet transform to capture multi-scale features of the process, as well as an artificial neural network (ANN) to predict the runoff discharge, is a beneficial approach. One of the essential steps in any ANN-based development process is determination of dominant input variables. This paper presents a two-stage procedure to model the rainfall–runoff process of the Delaney Creek and Payne Creek Basins, Florida, USA. The two-stage procedure includes data pre-processing and model building stages. In the data pre-processing stage, a wavelet transform is used to decompose the rainfall and runoff time series into several sub-series at different scales. Subsequently, independent sub-series are chosen via a self-organizing map (SOM). In the model building stage, selected sub-series are imposed as input data to a feed-forward neural network (FFNN) to forecast runoff discharge. To make a better interpretation of the model efficiency, the proposed model is compared with the Auto Regressive Integrated Moving Average with eXogenous input (ARIMAX) and with the ad hoc FFNN methods, without any data pre-processing. The results proved that the proposed model leads to better outcome especially in term of determination coefficient for detecting peak points (DCpeak).

scholarly journals A Conceptual Artificial Neural Network Model in Warehouse Receiving Management

2021 ◽  
Vol 11 (2) ◽  
pp. 130-136
Author(s):  
Judy X Yang ◽  
◽  
Lily D Li ◽  
Mohammad G. Rasul
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mechanical Design ◽  
Joint Probability ◽  
Initial Result ◽  
Ann Model ◽  
Accuracy Rate ◽  
Proposed Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The purpose of this research is to explore a suitable Artificial Neural Network (ANN) method applying to warehouse receiving management. A conceptual ANN model is proposed to perform identification and counting of components. The proposed model consists of a standard image library, an ANN system to present objects for identification from the real-time images and to count the number of objects in the image. The authors adopted four basic mechanical design shapes as the attributes of images for shape analysis and pre-defined features; the joint probability from Bayes theorem and image pixel values for object counting is applied in this research. Compared to other ANNs, the proposed conceptual model is straightforward to perform classification and counting. The model is tested by employing a mini image dataset which is industrial enterprise relevant. The initial result shows that the proposed model has achieved an accuracy rate of 80% in classification and a 97% accuracy rate in counting. The development of the model is associated with a few challenges, including exploring algorithms to enhance the accuracy rate for component identification and testing the model in a larger dataset.

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