scholarly journals Groundwater Level Trend Analysis for Long-term Prediction Basedon Gaussian Process Regression

2016 ◽  
Vol 21 (4) ◽  
pp. 30-41
Author(s):  
Hyo Geon Kim ◽  
Eungyu Park ◽  
Jina Jeong ◽  
Weon Shik Han ◽  
Kue-Young Kim
Keyword(s):  
Gaussian Process ◽  
Trend Analysis ◽  
Groundwater Level ◽  
Gaussian Process Regression ◽  
Term Prediction ◽  
Long Term Prediction

Long-term prediction of karst water recovery process based on two different approaches in a former mining area, Transdanubian Range, Hungary

2020 ◽  
Author(s):  
Kamilla Modrovits ◽  
András Csepregi ◽  
József Kovács
Keyword(s):  
Time Series ◽  
Water Level ◽  
Trend Analysis ◽  
Recovery Process ◽  
Karst Water ◽  
Deterministic Approach ◽  
Term Prediction ◽  
Transdanubian Range ◽  
Long Term Prediction

<p>The Transdanubian Range is located in the mid-western part of Hungary and contains Mesozoic, mainly Triassic formations with the total thickness of 1.5-2 km. From 1950 to 1990 coal and bauxite mining took place with different centres in this area, therefor large amount of karst water was extracted for preventative purpose. Thus, the water levels decreased from ten to more than a hundred of meters. Since the mining was stopped in the beginning of the 1990s, the natural recharge exceeded the amount of extraction and the recovery of the karst water began. Since then the system is on the way to return to its original – undisturbed – state. Because of the rising water level, economic and technical engineering problems have occurred, which requires the better understanding of the process.</p><p>Water level changes are often predicted with a deterministic approach using different modelling software (e.g. MODFLOW, FEFLOW, etc.). However, stochastic approaches (e.g. trend estimation), which have so far been little used in forecast of groundwater, can also be applied for certain hydrogeological problems. The aims of the research were (i) to find the most accurate trend function describing the recovery process (ii) in order to make a long-term prediction, (iii) and compare the results with the results deterministic modelling. For this purpose, decades of time series from 107 monitoring wells were investigated.</p><p>As a result of the research, it was identified that the karst water time series from the Transdanubian Range can be properly estimated (R<sup>2</sup> > 0.9 in the 82.24% of the cases) by growth and logistic curves, especially by the so-called Richards and “63%” ones. These curves gave the best fit in 57.95% of the cases based on the R<sup>2</sup> value obtained by fitting the 10 examined models. Both the deterministic approach modelling (MODFLOW) and the stochastic approach trend analysis are suitable for estimating and predicting the water level rise in the karst aquifer, but the results are slightly different. Modelling with the MODFLOW software can be affected by the accuracy of input parameters (infiltration, yield of springs, etc.) and the realness of the conceptual model. First and foremost, more and better-quality water level data series are needed for trend analysis, and based on our prior knowledge, it is essential to provide an accurate expected maximum water level (upper limit). The comparison of the two methods unveiled, that growth and logistic curves can also be successfully used in the prediction of groundwater levels. As a conclusion, the number of methods which may be used for such research can be expanded.</p><p>This research is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 810980.</p>

scholarly journals Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression

2021 ◽  
Vol 15 (1) ◽  
pp. 1147-1158
Author(s):  
Shahab S. Band ◽  
Essam Heggy ◽  
Sayed M. Bateni ◽  
Hojat Karami ◽  
Mobina Rabiee ◽  
...  
Keyword(s):  
Wavelet Analysis ◽  
Gaussian Process ◽  
Groundwater Level ◽  
Gaussian Process Regression ◽  
Arid Areas ◽  
Groundwater Level Prediction

scholarly journals Novel robust time series analysis for long-term and short-term prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroshi Okamura ◽  
Yutaka Osada ◽  
Shota Nishijima ◽  
Shinto Eguchi
Keyword(s):  
Robust Regression ◽  
Simulated Data ◽  
New Method ◽  
Short Term ◽  
Least Absolute Deviations ◽  
Term Prediction ◽  
Regression Approach ◽  
Short Term Prediction ◽  
Long Term Prediction

AbstractNonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.

Long term prediction of forest resources

1985 ◽  
Vol 12 ◽  
pp. 176
Author(s):  
H Kurth ◽  
B Anders ◽  
G Lucas
Keyword(s):  
Forest Resources ◽  
Term Prediction ◽  
Long Term Prediction
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