State-of-art of genetic programming applications in water-resources systems analysis

Mohammad-Azari, Sahar; Bozorg‐Haddad, Omid; Loáiciga, Hugo A.

doi:10.1007/s10661-019-8040-9

Cited by 26 publications

(12 citation statements)

References 63 publications

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“…EC methods refer to a class of computational method inspired by natural processes of evolution. EC could apply in the form of GA, GP, or GEP models (Mohammad-Azari et al, 2020). The GP and GEP models have become two of the most successful models when confronted with hydrological modeling among various data-driven models (Koza, 1994;Heydari et al, 2016;Mohammad-Azari et al, 2020).…”

Section: Overview Of Genetic Modelsmentioning

confidence: 99%

“…EC could apply in the form of GA, GP, or GEP models (Mohammad-Azari et al, 2020). The GP and GEP models have become two of the most successful models when confronted with hydrological modeling among various data-driven models (Koza, 1994;Heydari et al, 2016;Mohammad-Azari et al, 2020). The GP model has a tree-based structure that uses nodes and branches to show the relations between input and output data.…”

Section: Overview Of Genetic Modelsmentioning

confidence: 99%

“…In recent years, several studies have documented hydrological forecasting using one or more datadriven methods and time series models (Nourani et al, 2014). Among various data-driven models, Genetic models such as Genetic Programming (GP) and Genetic Expression Programming (GEP) could produce incredibly accurate results in a wide range of hydrological modeling (Mohammad-Azari et al, 2020). Based on previous studies, Genetic models (GP and GEP) in the simulation and prediction of rainfall-runoff, water table depth variations, precipitation, suspended sediment, evaporation, lake water level variations, reservoir operation, water quality, water demand, flood routing, and streamflow have performed better than ANN, ANFIS, and SVM models (Kisi & Shiri, 2011;Shiri & Kişi, 2011;Kisi et al, 2012;Al-Juboori & Guven, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Assessment of probable groundwater changes under representative concentration pathway (RCP) scenarios through the wavelet–GEP model

et al. 2021

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The main objective of this research was to evaluate the possible impact of climate change on groundwater levels in the Tasuj Plain, Iran. To accomplish this, the values of precipitation for a near future period was projected through four General Circulation Models (GCM). Then, the groundwater level variations through the Genetic Expression Programming (GEP) model were evaluated. The projection results indicated that the average annual precipitation with 33.55 mm in the base period would decline to 20.51 mm, 20.11 mm, and 19.14 mm under three Representative Concentration Pathway (RCP) scenarios, namely RCP2.6, RCP4.5, RCP8.5, respectively. The values of the determination coefficient range from 0.92-0.99; the root mean square error between 0.12 and 0.61, and mean absolute error from 0.08 to 0.54 showed that in the forecasting of groundwater level through the GEP model, all models have acceptable results. The evaluation of groundwater level simulation demonstrated that the developed model through the precipitation and previous groundwater level performs better. Prediction of groundwater level for a future period based on climate change scenarios indicated that the average groundwater level in Tasuj Plain at the beginning of the period would experience a gradual reduction and would then increase very slightly to the end of the period. Overall, it was found that the declining precipitation under climate change has no significant impacts on the groundwater level in the Tasuj Plain, and other parameters like anthropogenic activities could be the primary reason for groundwater depletion.

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Section: Overview Of Genetic Modelsmentioning

confidence: 99%

Section: Overview Of Genetic Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessment of probable groundwater changes under representative concentration pathway (RCP) scenarios through the wavelet–GEP model

et al. 2021

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“…In all types, a population of random solutions (programs) is formed at the outset and then, the genetic items of each program are progressively changed to achieve the desired solution. Hydrologists have frequently used GP as a symbolic regression tool (Danandeh Mehr et al 2018;Mohammad-Azari et al 2020). Examples include the use of GP for rainfall-runoff modeling (Babovic and Keijzer 2002;Havlíček et al 2013), groundwater simulation (Fallah-Mehdipour et al 2014), forecasting meteorological variables (Kisi and Shiri 2011;Citakoglu et al 2020), water quality prediction (Bozorg-Haddad et al 2017), soil temperature modeling (Kisi et al 2017), and spatial distribution of flow depth in fluvial rivers (Yan et al 2021).…”

Section: Introductionmentioning

confidence: 99%

A new evolutionary time series model for streamflow forecasting in boreal lake-river systems

Mehr

Ghadimi

Marttila

et al. 2022

Theor Appl Climatol

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Genetic programming (GP) is an evolutionary regression method that has received considerable interest to model hydro-environmental phenomena recently. Considering the sparseness of hydro-meteorological stations on northern areas, this study investigates the benefits and downfalls of univariate streamflow modeling at high latitudes using GP and seasonal autoregressive integrated moving average (SARIMA). Furthermore, a new evolutionary time series model, called GP-SARIMA, is introduced to enhance streamflow forecasting accuracy at long-term horizons in a lake-river system. The paper includes testing the new model for one-step-ahead forecasts of daily mean, weekly mean, and monthly mean streamflow in the headwaters of the Oulujoki River, Finland. The results showed that a combination of correlogram and average mutual information (AMI) analysis might yield in the selection of the optimum lags that are needed to be used as the predictors of streamflow models. With Nash-Sutcliffe efficiency values of more than 99%, both GP and SARIMA models exhibited good performance for daily streamflow prediction. However, they were not able to precisely model the intramonthly snow water equivalent in the long-term forecast. The proposed ensemble model, which integrates the best GP and SARIMA models with the most efficient predictor, may eliminate one-fourth of root mean squared errors of standalone models. The GP-SARIMA also showed up to three times improvement in the accuracy of the standalone models based on the Nash-Sutcliff efficiency measure.

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“…Moreover, a comparison among ANN, ARMA (Autoregressive Moving Average), and SVM models that were conducted by Lin et al (2006) revealed that the SVM model can give a more accurate prediction of long-term flow discharges than the others. A comprehensive review of the applications of genetic programming (GP) in the analysis of water resources systems was conducted by Mohammad-Azari et al (2020). The review indicates the capability and superiority of the model for solving a wide variety of water-related problems such as modeling rainfall-runoff, streamflow, sedimentation, flood, evaporation, water quality, water demand, and water distribution systems.…”

Section: Introductionmentioning

confidence: 99%

Prediction of daily water level using new hybridized GS-GMDH and ANFIS-FCM models

Ebtehaj

Sammen

Sidek

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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Accurate prediction of water level (WL) is essential for the optimal management of different water resource projects. The development of a reliable model for WL prediction remains a challenging task in water resources management. In this study, novel hybrid models, namely, Generalized Structure-Group Method of Data Handling (GS-GMDH) and Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means (ANFIS-FCM) were proposed to predict the daily WL at Telom and Bertam stations located in Cameron Highlands of Malaysia. Different percentage ratio for data division i.e. 50%-50% (scenario-1), 60%-40% (scenario-2), and 70%-30% (scenario-3) were adopted for training and testing of these models. To show the efficiency of the proposed hybrid models, their results were compared with the standalone models that include the Gene Expression Programming (GEP) and Group Method of Data Handling (GMDH). The results of the investigation revealed that the hybrid GS-GMDH and ANFIS-FCM models outperformed the standalone GEP and GMDH models for the prediction of daily WL at both study sites. In addition, the results indicate the best performance for WL prediction was obtained in scenario-3 (70%-30%). In summary, the results highlight the better suitability and supremacy of the proposed hybrid GS-GMDH and ANFIS-FCM models in daily WL prediction, and can, serve as robust and reliable predictive tools for the study region.

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State-of-art of genetic programming applications in water-resources systems analysis

Cited by 26 publications

References 63 publications

Assessment of probable groundwater changes under representative concentration pathway (RCP) scenarios through the wavelet–GEP model

Assessment of probable groundwater changes under representative concentration pathway (RCP) scenarios through the wavelet–GEP model

A new evolutionary time series model for streamflow forecasting in boreal lake-river systems

Prediction of daily water level using new hybridized GS-GMDH and ANFIS-FCM models

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