Research on groundwater Level Prediction of Naoli River Basin based on Elman wavelet neural networks

2011 ◽  
Author(s):  
Wu Chang-you
Keyword(s):  
Neural Networks ◽  
River Basin ◽  
Groundwater Level ◽  
Wavelet Neural Networks ◽  
Groundwater Level Prediction

scholarly journals Groundwater Level Prediction of Shahrood Plain using RBF Neural Networks

2016 ◽  
Vol 7 (13) ◽  
pp. 118-104 ◽  
Author(s):  
فرزانه اکبرزاده ◽  
حمید حسن پور ◽  
صمد امامقلی زاده ◽  
◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Groundwater Level ◽  
Rbf Neural Networks ◽  
Groundwater Level Prediction

scholarly journals Standard Precipitation Index Drought Forecasting Using Neural Networks, Wavelet Neural Networks, and Support Vector Regression

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
A. Belayneh ◽  
J. Adamowski
Keyword(s):  
Neural Networks ◽  
Support Vector Regression ◽  
River Basin ◽  
Precipitation Index ◽  
Data Driven ◽  
Support Vector ◽  
Lead Times ◽  
Wavelet Neural Networks ◽  
Standard Precipitation Index ◽  
Awash River Basin

Drought forecasts can be an effective tool for mitigating some of the more adverse consequences of drought. Data-driven models are suitable forecasting tools due to their rapid development times, as well as minimal information requirements compared to the information required for physically based models. This study compares the effectiveness of three data-driven models for forecasting drought conditions in the Awash River Basin of Ethiopia. The Standard Precipitation Index (SPI) is forecast and compared using artificial neural networks (ANNs), support vector regression (SVR), and wavelet neural networks (WN). SPI 3 and SPI 12 were the SPI values that were forecasted. These SPI values were forecast over lead times of 1 and 6 months. The performance of all the models was compared using RMSE, MAE, andR2. The forecast results indicate that the coupled wavelet neural network (WN) models were the best models for forecasting SPI values over multiple lead times in the Awash River Basin in Ethiopia.

scholarly journals Deep learning shows declining groundwater levels in Germany until 2100 due to climate change

2021 ◽  
Author(s):  
Andreas Wunsch ◽  
Tanja Liesch ◽  
Stefan Broda
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Neural Networks ◽  
Groundwater Level ◽  
Groundwater Resources ◽  
Anthropogenic Factors ◽  
Climate Projections ◽  
Climate Signal ◽  
Groundwater Levels ◽  
Groundwater Level Prediction

Abstract In this study we investigate how climate change will directly influence the groundwater resources in Germany during the 21st century. We apply a machine learning groundwater level prediction framework, based on convolutional neural networks to 118 sites well distributed over Germany to assess the groundwater level development under the RCP8.5 scenario, based on six selected climate projections, which represent 80% of the bandwidth of the possible future climate signal for Germany. We consider only direct meteorological inputs, while highly uncertain anthropogenic factors such as groundwater extractions are excluded. We detected significant declining trends of groundwater levels for most of the sites, revealing a spatial pattern of stronger decreases especially in the northern and eastern part of Germany, emphasizing already existing decreasing trends in these regions. We can further show an increased variability and longer periods of low groundwater levels during the annual cycle towards the end of the century.

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