scholarly journals Modified Group Method of Data Handling for Flood Quantile Prediction at Ungauged Site

2021 ◽  
Vol 10 (6) ◽  
pp. 57
Author(s):  
Basri Badyalina ◽  
Ani Shabri ◽  
Nurkhairany Amyra Mokhtar ◽  
Mohamad Faizal Ramli ◽  
Muhammad Majid ◽  
...  
Keyword(s):  
Simulation Study ◽  
Transfer Functions ◽  
Extreme Event ◽  
Principal Component ◽  
Cauchy Distribution ◽  
Prediction Performance ◽  
Group Method ◽  
Data Handling ◽  
Data Set ◽  
Ungauged Sites

Handling flood quantile with little data is essential in managing water resources. In this paper, we propose a potential model called Modified Group Method of Data Handling (MGMDH) to predict the flood quantile at ungauged sites in Malaysia. In this proposed MGMDH model, the principal component analysis (PCA) method is matched to the group method of data handling (GMDH) with various transfer functions. The MGMDH model consists of four transfer functions: polynomial, sigmoid, radial basis function, and hyperbolic tangent sigmoid transfer functions. The prediction performance of MGMDH models is compared to the conventional GMDH model. The appropriateness and effectiveness of the proposed models are demonstrated with a simulation study. Cauchy distribution is used in the simulation study as a disturbance error. The implementation of Cauchy Distribution as an error disturbance in artificial data illustrates the performance of the proposed models if the extreme value or extreme event occurs in the data set. The simulation study may say that the MGMDH model is superior to other comparison models, namely LR, NLR, GMDH and ANN models. Another beauty of this proposed model is that it shows a strong prediction performance when multicollinearity is absent in the data set.

scholarly journals Streamflow Estimation at Ungauged Basin using Modified Group Method of Data Handling

2021 ◽  
Vol 50 (9) ◽  
pp. 2765-2779
Author(s):  
Basri Badyalina ◽  
Ani Shabri ◽  
Muhammad Fadhil Marsani
Keyword(s):  
Water Resource Management ◽  
Transfer Functions ◽  
Model Performance ◽  
Principal Component ◽  
Peninsular Malaysia ◽  
Group Method ◽  
Data Handling ◽  
Ungauged Basins ◽  
Ungauged Sites ◽  
Hydrological Variable

Among the foremost frequent and vital tasks for hydrologist is to deliver a high accuracy estimation on the hydrological variable, which is reliable. It is essential for flood risk evaluation project, hydropower development and for developing efficient water resource management. Presently, the approach of the Group Method of Data Handling (GMDH) has been widely applied in the hydrological modelling sector. Yet, comparatively, the same tool is not vastly used for the hydrological estimation at ungauged basins. In this study, a modified GMDH (MGMDH) model was developed to ameliorate the GMDH model performance on estimating hydrological variable at ungauged sites. The MGMDH model consists of four transfer functions that include polynomial, hyperbolic tangent, sigmoid and radial basis for hydrological estimation at ungauged basins; as well as; it incorporates the Principal Component Analysis (PCA) in the GMDH model. The purpose of PCA is to lessen the complexity of the GMDH model; meanwhile, the implementation of four transfer functions is to enhance the estimation performance of the GMDH model. In evaluating the effectiveness of the proposed model, 70 selected basins were adopted from the locations throughout Peninsular Malaysia. A comparative study on the performance was done between the MGMDH and GMDH model as well as with other extensively used models in the area of flood quantile estimation at ungauged basins known as Linear Regression (LR), Nonlinear Regression (NLR) and Artificial Neural Network (ANN). The results acquired demonstrated that the MGMDH model possessed the best estimation with the highest accuracy comparatively among all models tested. Thus, it can be deduced that MGMDH model is a robust and efficient instrument for flood quantiles estimation at ungauged basins.

scholarly journals Investigating the capability of two hybrid intelligence methods to predict bedform dimensions of alluvial channels

2017 ◽  
Vol 18 (5) ◽  
pp. 1706-1718
Author(s):  
Kourosh Qaderi ◽  
Mohammad Reza Maddahi ◽  
Majid Rahimpour ◽  
Mojtaba Masoumi Shahr-babak
Keyword(s):  
Empirical Formula ◽  
Transfer Functions ◽  
Harmony Search ◽  
Group Method ◽  
Data Set ◽  
Dimensionless Parameters ◽  
Significant Difference ◽  
Intelligence Models ◽  
Hybrid Intelligence ◽  
Input Variables

Abstract Dimensions of river bedforms have an effect on total roughness. The complexity of bedform development causes empirical methods to differentiate from each other in predicting bedform dimensions. In this paper, two novel hybrid intelligence models based on a combination of the group method of data handling (GMDH) with the harmony search (HS) algorithm and shuffled complex evolution (SCE) have been developed for predicting bedform dimensions. A data set of 446 field and laboratory measurements were used to evaluate the ability of the developed models. The results were compared to conventional GMDH models with two kinds of transfer functions and an empirical formula. Also, five different combinations of dimensionless parameters as input variables were examined for predicting bedform dimensions. Results reveal that GMDH-HS and GMDH-SCE have good performance in predicting bedform dimensions, and all artificial intelligence methods were dramatically different to the empirical formula of van Rijn showing that using these methods is a key to solving complexity in predicting bedform dimensions. Also, comparing different combinations of dimensionless parameters reveals that there is no significant difference between the accuracy of each combination in predicting bedform dimensions.

scholarly journals FEASIBILITY OF USING GROUP METHOD OF DATA HANDLING (GMDH) APPROACH FOR HORIZONTAL COORDINATE TRANSFORMATION

2020 ◽  
Vol 46 (2) ◽  
pp. 55-66
Author(s):  
Bernard Kumi-Boateng ◽  
Yao Yevenyo Ziggah
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Coordinate Transformation ◽  
Machine Learning Algorithms ◽  
Group Method ◽  
Data Handling ◽  
Learning Models ◽  
Data Set ◽  
Functional Relationships ◽  
Machine Learning Models

Machine learning algorithms have emerged as a new paradigm shift in geoscience computations and applications. The present study aims to assess the suitability of Group Method of Data Handling (GMDH) in coordinate transformation. The data used for the coordinate transformation constitute the Ghana national triangulation network which is based on the two-horizontal geodetic datums (Accra 1929 and Leigon 1977) utilised for geospatial applications in Ghana. The GMDH result was compared with other standard methods such as Backpropagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), 2D conformal, and 2D affine. It was observed that the proposed GMDH approach is very efficient in transforming coordinates from the Leigon 1977 datum to the official mapping datum of Ghana, i.e. Accra 1929 datum. It was also found that GMDH could produce comparable and satisfactory results just like the widely used BPNN and RBFNN. However, the classical transformation methods (2D affine and 2D conformal) performed poorly when compared with the machine learning models (GMDH, BPNN and RBFNN). The computational strength of the machine learning models’ is attributed to its self-adaptive capability to detect patterns in data set without considering the existence of functional relationships between the input and output variables. To this end, the proposed GMDH model could be used as a supplementary computational tool to the existing transformation procedures used in the Ghana geodetic reference network.

Climatic reconstruction from tree rings at Banff

1988 ◽  
Vol 18 (7) ◽  
pp. 888-900 ◽  
Author(s):  
E. O. Robertson ◽  
L. A. Jozsa
Keyword(s):  
Tree Ring ◽  
Transfer Functions ◽  
Principal Component ◽  
Regression Equations ◽  
Climate Variables ◽  
Climate Data ◽  
Data Set ◽  
Tree Ring Data ◽  
Variable Data ◽  
Component Scores

This study describes new techniques of tree-ring data preparation and data analysis for deriving proxy climate data from senescent Douglas-fir (Pseudotsugamenziesii var. glauca (Beissn.) Franco) trees from the Canadian Rockies, near Banff, Alberta. Fifteen annual tree-ring variables were measured by X-ray densitometry for 429 years (1550–1978) for 12 increment cores. Ring variable data were reduced to standard indexes using a 99-year normally weighted digital filter. Missing ring values were estimated using correlation with younger and more vigorous specimens, and each ring variable data set (12 cores × 429 years) was reduced to its first and second principal component score, to be used in the development of response and transfer functions. Factor analysis identified six subsets of ring variable principal component scores. The best multiple regression equations for transferring tree-ring variable principal components into reconstruction of climate were identified by screening all possible combinations of principal component scores between factor groups. Annual climate variables, such as total precipitation, did not transfer as successfully as did the shorter-term climate variables like June–July precipitation (R2 = 0.36 compared with 0.51). Verified transfer functions were developed for five climate variables which can now be reconstructed to 1550 a.d. (429 years).

Efficiency of different marker systems for molecular characterization of subtropical carrot germplasm

2010 ◽  
Vol 148 (2) ◽  
pp. 171-181 ◽  
Author(s):  
T. JHANG ◽  
M. KAUR ◽  
P. KALIA ◽  
T. R. SHARMA
Keyword(s):  
Genetic Variability ◽  
Ssr Markers ◽  
Dna Markers ◽  
Principal Component ◽  
Group Method ◽  
Data Set ◽  
Breeding Lines ◽  
Pair Group ◽  
Ssr Loci ◽  
Simple Sequence

SUMMARYGenetic variability in carrots is a consequence of allogamy, which leads to a high level of inbreeding depression, affecting the development of new varieties. To understand the extent of genetic variability in 40 elite indigenous breeding lines of subtropical carrots, 48 DNA markers consisting of 16 inter simple sequence repeats (ISSRs), 10 universal rice primers (URPs), 16 random amplification of polymorphic DNA (RAPD) and six simple sequence repeat (SSR) markers were used. These 48 markers amplified a total of 591 bands, of which 569 were polymorphic (0·96). Amplicon size ranged from 200 to 3500 base pairs (bp) in ISSR, RAPD and URPs markers and from 100 to 300 bp in SSR markers. The ISSR marker system was found to be most efficient with (GT)n motifs as the most abundant SSR loci in the carrot genome. The unweighted pair group method with arithmetic mean (UPGMA) analysis of the combined data set of all the DNA markers obtained by four marker systems classified 40 genotypes in two groups with 0·45 genetic similarity with high Mantel matrix correlation (r=0·92). The principal component analysis (PCA) of marker data also explained 0·55 of the variation by first three components. Molecular diversity was very high and non-structured in these open-pollinated genotypes. The study demonstrated for the first time that URPs can be used successfully in genetic diversity analysis of tropical carrots. In addition, an entirely a new set of microsatellite markers, derived from the expressed sequence tags (ESTs) sequences of carrots, has been developed and utilized successfully.

FLOOD ESTIMATION AT UNGAUGED SITES USING GROUP METHOD OF DATA HANDLING IN PENINSULAR MALAYSIA

2015 ◽  
Vol 76 (1) ◽  
Author(s):  
Basri Badyalina ◽  
Ani Shabri
Keyword(s):  
Absolute Error ◽  
Medical Diagnostics ◽  
Peninsular Malaysia ◽  
Annual Rainfall ◽  
Group Method ◽  
Data Handling ◽  
Prediction Ability ◽  
Flood Peak ◽  
Ungauged Sites ◽  
Flood Estimation

Group Method of Data Handling (GMDH) have been successful in many fields such as economy, ecology, medical diagnostics, signal processing, and control systems but given a little attention in hydrology field especially for flood estimation at ungauged sites.  Ungauged site basically mean the site of interest is no flood peak data available. This paper presented application of GMDH model at ungauged site to predict flood quantile for T=10 year and T=100 year. There five catchment characteristics implement in this study that are catchment area, elevation, longest drainage path, slope of the catchment and mean maximum annual rainfall. The total number of catchment used for this study is 70 catchments in Peninsular Malaysia. Four quantitative standard statistical indices such as mean absolute error (MAE), root mean square error (RMSE) and Nash-Sutcliffe coefficient of efficiency (CE) are employed. Based on these results, it was found that the GMDH model outperforms the prediction ability of the traditional LR model.

Customer-Related Social Stressors

2015 ◽  
Vol 14 (4) ◽  
pp. 165-181 ◽  
Author(s):  
Sarah Dudenhöffer ◽  
Christian Dormann
Keyword(s):  
Service Providers ◽  
Principal Component ◽  
Well Being ◽  
Emotional Dissonance ◽  
Social Stressors ◽  
Data Set ◽  
Confirmatory Factor Analyses ◽  
Confirmatory Factor ◽  
Service Jobs ◽  
Single Data

Abstract. The purpose of this study was to replicate the dimensions of the customer-related social stressors (CSS) concept across service jobs, to investigate their consequences for service providers’ well-being, and to examine emotional dissonance as mediator. Data of 20 studies comprising of different service jobs (N = 4,199) were integrated into a single data set and meta-analyzed. Confirmatory factor analyses and explorative principal component analysis confirmed four CSS scales: disproportionate expectations, verbal aggression, ambiguous expectations, disliked customers. These CSS scales were associated with burnout and job satisfaction. Most of the effects were partially mediated by emotional dissonance. Further analyses revealed that differences among jobs exist with regard to the factor solution. However, associations between CSS and outcomes are mainly invariant across service jobs.

Comparison of Principal Component Solutions in Two Populations

Methodology ◽  
2016 ◽  
Vol 12 (1) ◽  
pp. 11-20 ◽  
Author(s):  
Gregor Sočan
Keyword(s):  
Simulation Study ◽  
Principal Components ◽  
Common Factor ◽  
Principal Component ◽  
Component Model ◽  
Bootstrap Procedure ◽  
Factor Loadings ◽  
Component Loadings ◽  
Principal Component Model ◽  
Two Populations

Abstract. When principal component solutions are compared across two groups, a question arises whether the extracted components have the same interpretation in both populations. The problem can be approached by testing null hypotheses stating that the congruence coefficients between pairs of vectors of component loadings are equal to 1. Chan, Leung, Chan, Ho, and Yung (1999) proposed a bootstrap procedure for testing the hypothesis of perfect congruence between vectors of common factor loadings. We demonstrate that the procedure by Chan et al. is both theoretically and empirically inadequate for the application on principal components. We propose a modification of their procedure, which constructs the resampling space according to the characteristics of the principal component model. The results of a simulation study show satisfactory empirical properties of the modified procedure.

Spectroscopy-Based Mapping with Scanning Microwave Impedance Microscopy

2018 ◽  
Author(s):  
Peter De Wolf ◽  
Zhuangqun Huang ◽  
Bede Pittenger
Keyword(s):  
Single Point ◽  
Electrical Characterization ◽  
Principal Component ◽  
High Sensitivity ◽  
Data Cube ◽  
Nanometer Scale ◽  
Learning Approaches ◽  
Data Set ◽  
3D Data ◽  
Higher Dimensional

Abstract Methods are available to measure conductivity, charge, surface potential, carrier density, piezo-electric and other electrical properties with nanometer scale resolution. One of these methods, scanning microwave impedance microscopy (sMIM), has gained interest due to its capability to measure the full impedance (capacitance and resistive part) with high sensitivity and high spatial resolution. This paper introduces a novel data-cube approach that combines sMIM imaging and sMIM point spectroscopy, producing an integrated and complete 3D data set. This approach replaces the subjective approach of guessing locations of interest (for single point spectroscopy) with a big data approach resulting in higher dimensional data that can be sliced along any axis or plane and is conducive to principal component analysis or other machine learning approaches to data reduction. The data-cube approach is also applicable to other AFM-based electrical characterization modes.

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