Groundwater budget forecasting, using hybrid wavelet-ANN-GP modelling: a case study of Azarshahr Plain, East Azerbaijan, Iran

Meticulous prediction of hydrological processes, especially water budget, has an individual importance in environmental management plans. On the other hand, conservation of groundwater, a fundamental resource in arid and semi-arid areas, needs to be considered as a great priority in development plans. Prediction of a groundwater budget utilizing artificial intelligence was the scope of this study. For this aim, the Azarshahr Plain aquifer, East Azerbaijan, Iran, was selected because of its great dependence on groundwater and the necessity of cognizance of its budget in future programs. The long-term fluctuations of the water table in 13 piezometers were simulated by a wavelet-based artificial neural network (WANN) hybrid model, and their statistical gaps were covered. Then, the modelled water table was predicted for the next 12 months using genetic programming. The results of simulation and prediction were assessed by performance evaluation criteria such as R2, root mean squared error, mean absolute error and Nash–Sutcliffe efficiency. Thiessen polygons were then utilized, plotting the predicted unit hydrograph of the study area. The predicted water table from September 2012 to August 2013 revealed about 0.12 m depletion. Regarding the area of the Azarshahr Plain aquifer and its average storage coefficient, the aquifer budget will be reduced by about 0.3557 million cubic metres during this period.

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Comparison of ANFIS and FAHP-FGP methods for supplier selection

Kybernetes ◽

10.1108/k-09-2014-0195 ◽

2016 ◽

Vol 45 (3) ◽

pp. 474-489 ◽

Cited By ~ 6

Author(s):

Moloud sadat Asgari ◽

Abbas Abbasi ◽

Moslem Alimohamadlou

Keyword(s):

Performance Measures ◽

Supplier Selection ◽

Mean Squared Error ◽

Selection Process ◽

Evaluation Criteria ◽

Absolute Error ◽

Global Market ◽

Content Type ◽

Squared Error ◽

Fuzzy Delphi

Purpose – In the contemporary global market, supplier selection represents a crucial process for enhancing firms’ competitiveness. This is a multi-criteria decision-making problem that involves consideration of multiple criteria. Therefore this requires reliable methods to select the best suppliers. The purpose of this paper is to examine and propose appropriate method for selecting suppliers. Design/methodology/approach – ANFIS and fuzzy analytic hierarchy process-fuzzy goal programming (FAHP-FGP) are new methods for evaluating and selecting the best suppliers. These methods are used in this study for evaluating suppliers of dairy industries and the results obtained from methods are compared by performance measures such as Mean Squared Error, Root Mean Squared Error, Normalized Root Men Squared Error, Mean Absolute Error, Normalized Root Men Squared Error, Minimum Absolute Error and R2. Findings – The results indicate that the ANFIS method provides better performance compared to the FAHP-FGP method in terms of the selected suppliers scoring higher in all the performance measures. Practical implications – The proposed method could help companies select the best supplier, by avoiding the influence of personal judgment. Originality/value – This study uses the well-structured method of the fuzzy Delphi in order to determine the supplier evaluation criteria as well as the most recent ANFIS and FAHP-FGP methods for supplier selection. In addition, unlike most other studies, it performs the selection process among all available suppliers.

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Statistical modelling of agrometeorological time series by exponential smoothing

International Agrophysics ◽

10.1515/intag-2015-0076 ◽

2016 ◽

Vol 30 (1) ◽

pp. 57-65 ◽

Cited By ~ 5

Author(s):

Małgorzata Murat ◽

Iwona Malinowska ◽

Holger Hoffmann ◽

Piotr Baranowski

Keyword(s):

Time Series ◽

Mean Squared Error ◽

Statistical Modelling ◽

Absolute Error ◽

Exponential Smoothing ◽

Growth And Yield ◽

Climate Change Scenarios ◽

Autocorrelation Functions ◽

Using Data

Abstract Meteorological time series are used in modelling agrophysical processes of the soil-plant-atmosphere system which determine plant growth and yield. Additionally, long-term meteorological series are used in climate change scenarios. Such studies often require forecasting or projection of meteorological variables, eg the projection of occurrence of the extreme events. The aim of the article was to determine the most suitable exponential smoothing models to generate forecast using data on air temperature, wind speed, and precipitation time series in Jokioinen (Finland), Dikopshof (Germany), Lleida (Spain), and Lublin (Poland). These series exhibit regular additive seasonality or non-seasonality without any trend, which is confirmed by their autocorrelation functions and partial autocorrelation functions. The most suitable models were indicated by the smallest mean absolute error and the smallest root mean squared error.

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Reconstructing Spectra from RGB Images by Relative Error Least-Squares Regression

Color and Imaging Conference ◽

10.2352/issn.2169-2629.2020.28.42 ◽

2020 ◽

Vol 2020 (28) ◽

pp. 264-269

Author(s):

Yi-Tun Lin ◽

Graham D. Finlayson

Keyword(s):

Least Squares ◽

Relative Error ◽

Mean Squared Error ◽

Evaluation Criteria ◽

Absolute Error ◽

Percentage Error ◽

Least Squares Regression ◽

Worst Case ◽

Regression Methods ◽

Spectral Reconstruction

Spectral reconstruction (SR) algorithms attempt to map RGB- to hyperspectral-images. Classically, simple pixel-based regression is used to solve for this SR mapping and more recently patch-based Deep Neural Networks (DNN) are considered (with a modest performance increment). For either method, the 'training' process typically minimizes a Mean-Squared-Error (MSE) loss. Curiously, in recent research, SR algorithms are evaluated and ranked based on a relative percentage error, so-called MeanRelative-Absolute Error (MRAE), which behaves very differently from the MSE loss function. The most recent DNN approaches - perhaps unsurprisingly - directly optimize for this new MRAE error in training so as to match this new evaluation criteria.<br/> In this paper, we show how we can also reformulate pixelbased regression methods so that they too optimize a relative spectral error. Our Relative Error Least-Squares (RELS) approach minimizes an error that is similar to MRAE. Experiments demonstrate that regression models based on RELS deliver better spectral recovery, with up to a 10% increment in mean performance and a 20% improvement in worst-case performance depending on the method.

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Modified Liu estimators in the linear regression model: An application to Tobacco data

PLoS ONE ◽

10.1371/journal.pone.0259991 ◽

2021 ◽

Vol 16 (11) ◽

pp. e0259991

Author(s):

Iqra Babar ◽

Hamdi Ayed ◽

Sohail Chand ◽

Muhammad Suhail ◽

Yousaf Ali Khan ◽

...

Keyword(s):

Linear Regression ◽

Mean Squared Error ◽

Prediction Interval ◽

Evaluation Criteria ◽

Absolute Error ◽

Vital Role ◽

Predictor Variables ◽

Linear Regression Models ◽

Squared Error ◽

Optimal Value

Background The problem of multicollinearity in multiple linear regression models arises when the predictor variables are correlated among each other. The variance of the ordinary least squared estimator become unstable in such situation. In order to mitigate the problem of multicollinearity, Liu regression is widely used as a biased method of estimation with shrinkage parameter ‘d’. The optimal value of shrinkage parameter plays a vital role in bias-variance trade-off. Limitation Several estimators are available in literature for the estimation of shrinkage parameter. But the existing estimators do not perform well in terms of smaller mean squared error when the problem of multicollinearity is high or severe. Methodology In this paper, some new estimators for the shrinkage parameter are proposed. The proposed estimators are the class of estimators that are based on quantile of the regression coefficients. The performance of the new estimators is compared with the existing estimators through Monte Carlo simulation. Mean squared error and mean absolute error is considered as evaluation criteria of the estimators. Tobacco dataset is used as an application to illustrate the benefits of the new estimators and support the simulation results. Findings The new estimators outperform the existing estimators in most of the considered scenarios including high and severe cases of multicollinearity. 95% mean prediction interval of all the estimators is also computed for the Tobacco data. The new estimators give the best mean prediction interval among all other estimators. The implications of the findings We recommend the use of new estimators to practitioners when the problem of high to severe multicollinearity exists among the predictor variables.

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Absolute and relative measures for evaluating the forecasting performance of time series models for daily streamflows

Hydrology Research ◽

10.2166/nh.2006.008 ◽

2006 ◽

Vol 37 (3) ◽

pp. 205-215 ◽

Cited By ~ 5

Author(s):

T. Astatkie

Keyword(s):

Time Series ◽

Mean Squared Error ◽

Model Performance ◽

Absolute Error ◽

Time Series Models ◽

Streamflow Forecasting ◽

Daily Streamflow ◽

Forecasting Performance ◽

Competing Models

Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) are widely used measures for evaluating the forecasting performance of time series models. Although these absolute measures can be used to compare the performance of competing models, one needs a reference to judge the goodness of the forecasts. In this paper, two relative measures, coefficient of efficiency (E) and index of agreement (d), and their modified versions (EM, EMP, dM and dMP) with desired values of closer to one are presented. These measures are illustrated by comparing the modeling ability and validation forecasting performance of a Nonlinear Additive Autoregressive with Exogenous variables (NAARX), Nested Threshold Autoregressive (NeTAR), and Multiple Nonlinear Inputs Transfer Function (MNITF) models developed for the Jökulsá eystri daily streamflow data. The results suggest that NeTAR describes the system best, and gives better 1- and 2-day ahead validation forecasts. MNITF gives better forecasts for 3-day ahead, and NeTAR and NAARX give comparable performance for 4- and 5-day ahead forecasting. The values of E and d were larger than those of the modified versions, giving a false sense of model performance, and unlike the modified versions, they decreased as forecast lead times increased. Differences among the values of these six relative measures can reveal the sensitiveness of competing models to outliers, and their potential for long-term forecasting. Accordingly, NeTAR was the least sensitive to outliers and NAARX was the most sensitive, with MNITF in between; and NAARX showed the most potential for long-term streamflow forecasting.

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Hydrogeological responses in tropical mountainous springs

Memorias del I Congreso Internacional de Ciencias Exactas y Naturales ◽

10.15359/cicen.1.37 ◽

2019 ◽

Author(s):

Ricardo Sánchez-Murillo

Keyword(s):

Costa Rica ◽

Isotope Composition ◽

Central Valley ◽

Distribution Functions ◽

Geochemical Composition ◽

Transit Times ◽

Management Plans ◽

Best Fit ◽

Term Data

This study presents a hydrogeochemical analysis of spring responses (2013-2017) in the tropical mountainous region of the Central Valley of Costa Rica. The isotopic distribution of δ18O and δ2H in rainfall resulted in a highly significant meteoric water line: δ2H = 7.93×δ18O + 10.37 (r2=0.97). Rainfall isotope composition exhibited a strong dependent seasonality. The isotopic variation (δ18O) of two springs within the Barva aquifer was simulated using the FlowPC program to determine mean transit times (MTTs). Exponential-piston and dispersion distribution functions provided the best-fit to the observed isotopic composition at Flores and Sacramento springs, respectively. MTTs corresponded to 1.23±0.03 (Sacramento) and 1.42±0.04 (Flores) years. The greater MTT was represented by a homogeneous geochemical composition at Flores, whereas the smaller MTT at Sacramento is reflected in a more variable geochemical response. The results may be used to enhance modelling efforts in central Costa Rica, whereby scarcity of long-term data limits water resources management plans.

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Drawdowns at the Free Water Table During Pumping Tests in Artesian and Semiartesian Aquifers

Hydrology Research ◽

10.2166/nh.1980.0020 ◽

1980 ◽

Vol 11 (3-4) ◽

pp. 159-168 ◽

Cited By ~ 1

Author(s):

Henrik Kærgaard

Keyword(s):

Water Table ◽

Free Water ◽

Pumping Test ◽

Water Withdrawal ◽

Specific Yield ◽

Minor Role ◽

Long Term Effects ◽

Long Term Storage ◽

Term Storage

In an earlier paper I have shown an example of how long term drawdowns can be used for the computation of long term storage in artesian and semiartesian areas. In most cases the long term storage is more or less equivalent to the specific yield at the water table; the storage mechanisms of consolidation playing a minor role in long term situations. The specific yield in artesian areas is a very important parameter in the prediction of long term effects of ground water withdrawal. Especially the stream depletion will often mainly be governed by draw-downs in upper nonpumped aquifers near the water table, and these drawdowns depend to a great extent on the specific yield at the water table. A determination of long term storage will often necessitate long term draw-down data, however, under certain circumstances a determination can be made on the basis of a pumping test of limited duration (3-5 weeks) provided drawdown observations at the water table can be made. In this paper some formulas dealing with water table drawdowns in different geohydrologic systems are reviewed, and two cases in which these formulas have been used in practice are presented.

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Consistent Long-Term Monthly Coastal Wetland Vegetation Monitoring Using a Virtual Satellite Constellation

Remote Sensing ◽

10.3390/rs13030438 ◽

2021 ◽

Vol 13 (3) ◽

pp. 438

Author(s):

Subrina Tahsin ◽

Stephen C. Medeiros ◽

Arvind Singh

Keyword(s):

Vegetation Index ◽

Coastal Wetland ◽

Thermal Emission ◽

Mean Squared Error ◽

Wetland Vegetation ◽

Coefficient Of Determination ◽

Landsat 8 ◽

Vegetation Monitoring ◽

Time Space

Long-term monthly coastal wetland vegetation monitoring is the key to quantifying the effects of natural and anthropogenic events, such as severe storms, as well as assessing restoration efforts. Remote sensing data products such as Normalized Difference Vegetation Index (NDVI), alongside emerging data analysis techniques, have enabled broader investigations into their dynamics at monthly to decadal time scales. However, NDVI data suffer from cloud contamination making periods within the time series sparse and often unusable during meteorologically active seasons. This paper proposes a virtual constellation for NDVI consisting of the red and near-infrared bands of Landsat 8 Operational Land Imager, Sentinel-2A Multi-Spectral Instrument, and Advanced Spaceborne Thermal Emission and Reflection Radiometer. The virtual constellation uses time-space-spectrum relationships from 2014 to 2018 and a random forest to produce synthetic NDVI imagery rectified to Landsat 8 format. Over the sample coverage area near Apalachicola, Florida, USA, the synthetic NDVI showed good visual coherence with observed Landsat 8 NDVI. Comparisons between the synthetic and observed NDVI showed Root Mean Squared Error and Coefficient of Determination (R2) values of 0.0020 sr−1 and 0.88, respectively. The results suggest that the virtual constellation was able to mitigate NDVI data loss due to clouds and may have the potential to do the same for other data. The ability to participate in a virtual constellation for a useful end product such as NDVI adds value to existing satellite missions and provides economic justification for future projects.

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A Novel Computational Intelligence Approach for Coal Consumption Forecasting in Iran

Sustainability ◽

10.3390/su13147612 ◽

2021 ◽

Vol 13 (14) ◽

pp. 7612

Author(s):

Mahdis sadat Jalaee ◽

Alireza Shakibaei ◽

Amin GhasemiNejad ◽

Sayyed Abdolmajid Jalaee ◽

Reza Derakhshani

Keyword(s):

Hybrid Method ◽

Computational Intelligence ◽

Energy Demand ◽

Mean Squared Error ◽

Absolute Error ◽

Coal Consumption ◽

Squared Error ◽

Artificial Neural ◽

Socio Economic Variables ◽

Intelligence Approach

Coal as a fossil and non-renewable fuel is one of the most valuable energy minerals in the world with the largest volume reserves. Artificial neural networks (ANN), despite being one of the highest breakthroughs in the field of computational intelligence, has some significant disadvantages, such as slow training, susceptibility to falling into a local optimal points, sensitivity of initial weights, and bias. To overcome these shortcomings, this study presents an improved ANN structure, that is optimized by a proposed hybrid method. The aim of this study is to propose a novel hybrid method for predicting coal consumption in Iran based on socio-economic variables using the bat and grey wolf optimization algorithm with an artificial neural network (BGWAN). For this purpose, data from 1981 to 2019 have been used for modelling and testing the method. The available data are partly used to find the optimal or near-optimal values of the weighting parameters (1980–2014) and partly to test the model (2015–2019). The performance of the BGWAN is evaluated by mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), standard deviation error (STD), and correlation coefficient (R^2) between the output of the method and the actual dataset. The result of this study showed that BGWAN performance was excellent and proved its efficiency as a useful and reliable tool for monitoring coal consumption or energy demand in Iran.

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Estimating Physical Activity Energy Expenditure Using an Ensemble Model-Based Patch-Type Sensor Module

Electronics ◽

10.3390/electronics10070861 ◽

2021 ◽

Vol 10 (7) ◽

pp. 861

Author(s):

Kyeung Ho Kang ◽

Mingu Kang ◽

Siho Shin ◽

Jaehyo Jung ◽

Meina Li

Keyword(s):

Physical Activity ◽

Energy Expenditure ◽

Mean Squared Error ◽

Absolute Error ◽

Ensemble Model ◽

Direct Calorimetry ◽

Patch Type ◽

Mortality And Morbidity ◽

Squared Error ◽

Sensor Module

Chronic diseases, such as coronary artery disease and diabetes, are caused by inadequate physical activity and are the leading cause of increasing mortality and morbidity rates. Direct calorimetry by calorie production and indirect calorimetry by energy expenditure (EE) has been regarded as the best method for estimating the physical activity and EE. However, this method is inconvenient, owing to the use of an oxygen respiration measurement mask. In this study, we propose a model that estimates physical activity EE using an ensemble model that combines artificial neural networks and genetic algorithms using the data acquired from patch-type sensors. The proposed ensemble model achieved an accuracy of more than 92% (Root Mean Squared Error (RMSE) = 0.1893, R2 = 0.91, Mean Squared Error (MSE) = 0.014213, Mean Absolute Error (MAE) = 0.14020) by testing various structures through repeated experiments.

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