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 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.

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.

Predictably Predictable -  The Role of Catchment Characteristics and Complexity.

2021 ◽  
Author(s):  
Sophia Eugeni ◽  
Eric Vaags ◽  
Steven V. Weijs
Keyword(s):  
Water Resource Management ◽  
Performance Metrics ◽  
Complexity Analysis ◽  
Model Performance ◽  
Principal Component ◽  
The United States ◽  
Hydrologic Model ◽  
Hydrologic Models ◽  
Hydrologic Modelling

<p>Accurate hydrologic modelling is critical to effective water resource management. As catchment attributes strongly influence the hydrologic behaviors in an area, they can be used to inform hydrologic models to better predict the discharge in a basin. Some basins may be more difficult to accurately predict than others. The difficulty in predicting discharge may also be related to the complexity of the discharge signal. The study establishes the relationship between a catchment’s static attributes and hydrologic model performance in those catchments, and also investigates the link to complexity, which we quantify with measures of compressibility based in information theory. </p><p>The project analyzes a large national dataset, comprised of catchment attributes for basins across the United States, paired with established performance metrics for corresponding hydrologic models. Principal Component Analysis (PCA) was completed on the catchment attributes data to determine the strongest modes in the input. The basins were clustered according to their catchment attributes and the performance within the clusters was compared. </p><p>Significant differences in model performance emerged between the clusters of basins. For the complexity analysis, details of the implementation and technical challenges will be discussed, as well as preliminary results.</p>

Estimating the Lengthy Missing Log Interval Using Group Method of Data Handling (GMDH) Technique

2014 ◽  
Vol 695 ◽  
pp. 850-853 ◽  
Author(s):  
Mohammed Abdallah Ayoub ◽  
Ahmed Abdelhafeez Mohamed
Keyword(s):  
Oil And Gas ◽  
Model Performance ◽  
Oil And Gas Industry ◽  
Group Method ◽  
Generic Model ◽  
Data Sets ◽  
Data Handling ◽  
Gas Industry ◽  
Information Sets ◽  
Code Group

Estimation of well log properties is crucial in identifying the trends and the properties in oil and gas industry, which will enable firms to avoid problems during operation procedures. In this paper, Group Method of Data Handling (GMDH) technique is utilized to generate a generic model with superior prediction capabilities. NeuraLogTM software is used in order to convert the scanned image logs into digit one that can be used by MATLAB code. Group Method of Data Handling is utilized to predict the same missing interval. An aggregate of 601 field data sets were utilized to create the model. These information sets were separated into training, cross validation and testing sets in the degree of 2:1:1. Trend analyses as well as graphical and statistical tools have been utilized in order to assess the model performance.

scholarly journals Data-driven catchment classification: application to the pub problem

2011 ◽  
Vol 15 (6) ◽  
pp. 1921-1935 ◽  
Author(s):  
M. Di Prinzio ◽  
A. Castellarin ◽  
E. Toth
Keyword(s):  
Input Data ◽  
Cross Validation ◽  
Principal Component ◽  
Annual Runoff ◽  
Data Driven ◽  
Ungauged Basins ◽  
Ungauged Sites ◽  
Streamflow Regime ◽  
Flood Quantiles ◽  
Unsupervised Neural Networks

Abstract. A promising approach to catchment classification makes use of unsupervised neural networks (Self Organising Maps, SOM's), which organise input data through non-linear techniques depending on the intrinsic similarity of the data themselves. Our study considers ∼300 Italian catchments scattered nationwide, for which several descriptors of the streamflow regime and geomorphoclimatic characteristics are available. We compare a reference classification, identified by using indices of the streamflow regime as input to SOM, with four alternative classifications, which were identified on the basis of catchment descriptors that can be derived for ungauged basins. One alternative classification adopts the available catchment descriptors as input to SOM, the remaining classifications are identified by applying SOM to sets of derived variables obtained by applying Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) to the available catchment descriptors. The comparison is performed relative to a PUB problem, that is for predicting several streamflow indices in ungauged basins. We perform an extensive cross-validation to quantify nationwide the accuracy of predictions of mean annual runoff, mean annual flood, and flood quantiles associated with given exceedance probabilities. Results of the study indicate that performing PCA and, in particular, CCA on the available set of catchment descriptors before applying SOM significantly improves the effectiveness of SOM classifications by reducing the uncertainty of hydrological predictions in ungauged sites.

APPLICATION OF ONE WEEK AHEAD WAVE PREDICTION FOR USING GROUP METHOD OF DATA HANDLING

2020 ◽  
Vol 76 (2) ◽  
pp. I_229-I_234
Author(s):  
Keishiro CHIYONOBU ◽  
Sooyoul KIM ◽  
Masahide TAKEDA ◽  
Chisato HARA ◽  
Hajime MASE ◽  
...  
Keyword(s):  
Group Method ◽  
Data Handling ◽  
Wave Prediction

scholarly journals SHETRAN and HEC HMS Model Evaluation for Runoff and Soil Moisture Simulation in the Jičinka River Catchment (Czech Republic)

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 872
Author(s):  
Vesna Đukić ◽  
Ranka Erić
Keyword(s):  
Soil Moisture ◽  
Czech Republic ◽  
Hydrological Model ◽  
Flash Flood ◽  
Model Performance ◽  
Rainfall Runoff ◽  
The Czech Republic ◽  
River Catchment ◽  
Hydrological Variable ◽  
Model Structures

Due to the improvement of computation power, in recent decades considerable progress has been made in the development of complex hydrological models. On the other hand, simple conceptual models have also been advanced. Previous studies on rainfall–runoff models have shown that model performance depends very much on the model structure. The purpose of this study is to determine whether the use of a complex hydrological model leads to more accurate results or not and to analyze whether some model structures are more efficient than others. Different configurations of the two models of different complexity, the Système Hydrologique Européen TRANsport (SHETRAN) and Hydrologic Modeling System (HEC-HMS), were compared and evaluated in simulating flash flood runoff for the small (75.9 km2) Jičinka River catchment in the Czech Republic. The two models were compared with respect to runoff simulations at the catchment outlet and soil moisture simulations within the catchment. The results indicate that the more complex SHETRAN model outperforms the simpler HEC HMS model in case of runoff, but not for soil moisture. It can be concluded that the models with higher complexity do not necessarily provide better model performance, and that the reliability of hydrological model simulations can vary depending on the hydrological variable under consideration.

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