Enhanced group method of data handling (GMDH) for permeability prediction based on the modified Levenberg Marquardt technique from well log data

This study introduces parametric Group Method of Data Handling (GMDH) into regional NSW, Australia, for the first time with the intent to assess the impact of climate change through rainfall modelling of publicly available data. The modelling presented the opportunity to investigate improvements to GMDH for rainfall modelling, with an empirical assessment undertaken. State variable distribution, their classification within the context of fuzzy, and the need to integrate Zadeh’s principle of incompatibility into the GMDH modelling format are all assessed. The mathematical foundations of GMDH are discussed within the heuristic framework of data partitioning, partial description synthesis and the limitations of least squares coefficient determination, Gödel’s incompleteness theorem and the necessity for an external criterion in the selection procedure for the Ivakhnenko polynomial. Methods for modelling improvement include the potential for hybridisation with least square support vector machines (LSSVM), the application of Kalman filters for parameter estimation, and the combination with signal processing techniques; ensemble empirical mode decomposition (EEMD), wavelet transformation (WT), and wavelet packet transformation (WPT) being investigated as is the implementation of enhanced GMDH (eGMDH) and fuzzy GMDH (FGMDH). The inclusion of exogeneous data is also discussed and whether application presents within the GMDH modelling paradigm. The study concludes with recommendations made to enhance the potential for future rainfall modelling study success.

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“…The integration of LM with GMDH is presented in pseudocode (Transtrum et al, 2011;Mulashani et al, 2021).…”

Section: Neuron Inputs Are Not Binary Limitedmentioning

confidence: 99%

Limitations of Parametric Group Method of Data Handling and Empirical Improvements for the Application of Rainfall Modelling

Lake

Shaeri

Senevirathna

2022

Preprint

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“…There are a variety of applications of the GMDH, including flood susceptibility prediction (Dodangeh et al, 2020), groundwater level prediction (Moosavi et al, 2020), daily river flow prediction (Aghelpour et al, 2020), and monthly streamflow prediction (Adnan et al, 2021). Mulashani et al (2022) used GMDH to predict permeability. With the GMDH, processing time was reduced, and accuracy was increased.…”

Section: Roushanger and Ghasempour (2017)mentioning

confidence: 99%

Application of Group Method of Data Handling and New Optimization Algorithms for Predicting Sediment Transport Rate under Vegetation Cover

Mirzakhani¹,

Ghanbari-Adivi²,

Fattahi³

et al. 2022

Preprint

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Planting vegetation is one of the practical solutions for reducing sediment transfer rates. Increasing vegetation cover decreases environmental pollution and sediment transport rate (STR). Since sediments and vegetation interact complexly, predicting sediment transport rates is challenging. This study aims to predict sediment transport rate under vegetation cover using new and optimized versions of the group method of data handling (GMDH). Additionally, this study introduces a new ensemble model for predicting sediment transport rates. Model inputs include wave height, wave velocity, density cover, wave force, D50, the height of vegetation cover, and cover stem diameter. A standalone GMDH model and optimized GMDH models, including GMDH- honey badger algorithm (HBA), GMDH- rat swarm algorithm (RSOA), GMDH- sine cosine algorithm (SCA), and GMDH- particle swarm optimization (GMDH-PSO), were used to predict sediment transport rates. As the next step, the outputs of standalone and optimized GMDH were used to construct an ensemble model. The MAE of the ensemble model was 0.145 m3/s, while the MAEs of GMDH-HBA, GMDH-RSOA, GMDH-SCA, GMDH-PSOA, and GMDH in the testing level were 0.176 m3/s, 0.312 m3/s, 0.367 m3/s, 0.498 m3/s, and 0.612 m3/s, respectively. The Nash–Sutcliffe coefficient (NSE) of ensemble model, GMDH-HBA, GMDH-RSOA, GMDH-SCA, GMDH-PSOA, and GHMDH were 0.95 0.93, 0.89, 0.86, 0.82, and 0.76, respectively. Additionally, this study demonstrated that vegetation cover decreased sediment transport rate by 90%. The results indicated that the ensemble and GMDH-HBA models could accurately predict sediment transport rates. Based on the results of this study, sediment transport rate can be monitored using the IMM and GMDH-HBA. These results are useful for managing and planning water resources in large basins.

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“…Apart from these studies, artificial intelligence models have become very widespread recently, and many similar prediction models are available in the literature. (Dalkiliç and Hashimi (2020), Okkan (2011), Yesilyurt and Dalkilic (2021), Okkan (2012), Damla et al (2020), Temiz et al (2021), Mulashani et al (2022), de Faria et al ( 2021)). With this study, it has been determined that artificial intelligence methods can successfully be found the nonlinear rainfall-flow relationship of basins in the estimation of flow values according to parametric methods without the need for intense data.…”

Section: Introductionmentioning

confidence: 97%

Comparison of Different Artificial Neural Network Methods in Determining Reservoir Capacity

Temiz

DAMLA²,

KESKİN

2022

Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Making estimation of river flow with hydrological and methodological data of the past period and using in water resources project studies have been used for a long time in many studies in our country and in the world. In addition to this, along with frequent drought problems in recent years, it is also important to store and use the water in the reservoirs correctly. This study aims to research how two separate Artificial-Neural-Network-Functions, which are generated based on the study of neural networks in the human brain, can be used in areas with a certain reservoir capacity such as dams or ponds and to provide an example of the most appropriate ANN model for predicting the level changes that will occur depending on the next years. In this context, two separate functions have been evaluated. These are the Gradient-Descent-with-Momentum (GDM) and Levenberg-Marquardt-(LM) training functions. Here, instead of the best results obtained from the models, the best three model outputs were evaluated and the models were considered from a wide frame. Gökçe Dam Basin, which is located in the Marmara region was chosen as the study area. With the different architectures prepared for the period of 31 December 2019 -1 January 2000, monthly basin capacities for 2019 have been tried to be estimated. 95% of the data belonging to the selected period was used for training and the remaining 5% for testing purposes. The flow rates entering and leaving the dam basin and the average precipitation, evaporation, and dam seepage amount were used as model inputs. While evaluating the model performances, Mean squared error (MSE), Mean absolute percent error (MAPE), Mean absolute error (MAE), and coefficients of determination were taken into consideration. As a result; It can be stated that the LM training models are more successful in estimating the dam basin levels and converge to the real values.

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Enhanced group method of data handling (GMDH) for permeability prediction based on the modified Levenberg Marquardt technique from well log data

Cited by 55 publications

References 67 publications

Limitations of Parametric Group Method of Data Handling and Empirical Improvements for the Application of Rainfall Modelling

Limitations of Parametric Group Method of Data Handling and Empirical Improvements for the Application of Rainfall Modelling

Application of Group Method of Data Handling and New Optimization Algorithms for Predicting Sediment Transport Rate under Vegetation Cover

Comparison of Different Artificial Neural Network Methods in Determining Reservoir Capacity

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