Genetic programming in water resources engineering: A state-of-the-art review

Mehr, Ali Danandeh; Nourani, Vahid; Kahya, Ercan; Hrnjica, Bahrudin; Sattar, Ahmed A.; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher

doi:10.1016/j.jhydrol.2018.09.043

Cited by 130 publications

(37 citation statements)

References 167 publications

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“…In recent years, a number of investigations into the implementation of machine learning (ML) models for evaporation estimation have been conducted across different regions (Abghari, Ahmadi, Besharat, & Rezaverdinejad, 2012;Baydaroǧlu & Koçak, 2014;Di et al, 2019;Fallah-Mehdipour, Bozorg Haddad, & Mariño, 2013;Fotovatikhah, Herrera, Shamshirband, Ardabili, & Piran, 2018;Lu et al, 2018;Majhi, Naidu, Mishra, & Satapathy, 2019;Moazenzadeh et al, 2018;Tabari, Marofi, & Sabziparvar, 2010). Several versions of ML models have been developed for evaporation modeling, including evolutionary computing, classical neural networks, kernel models, fuzzy logic, decision trees, deep learning, complementary wavelet-machine learning, and hybrid machine learning, among others (Danandeh Mehr et al, 2018;Fahimi, Yaseen, & El-shafie, 2016;Jing et al, 2019;Yaseen, Sulaiman, Deo, & Chau, 2019). The performance of these models and their hybrid combinations has been impressive in terms of prediction accuracy (Ghorbani, Deo, Karimi, Yaseen, & Terzi, 2017;Yaseen et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…ML models such as the classification and regression tree (CART), the cascade correlation neural network (CCNN), gene expression programming (GEP), and the support vector machine (SVM) have achieved significant advancements in hydrologic modeling (Danandeh Mehr et al, 2018;Fahimi et al, 2016;Jing et al, 2019;Yaseen et al, 2019). These models can efficiently mimic and solve the stochasticity of different complex hydro-climatological processes.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Al-Juboori

Beyaztaş

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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Evaporation, one of the fundamental components of the hydrology cycle, is differently influenced by various meteorological variables in different climatic regions. The accurate prediction of evaporation is essential for multiple water resources engineering applications, particularly in developing countries like Iraq where the meteorological stations are not sustained and operated appropriately for in situ estimations. This is where advanced methodologies such as machine learning (ML) models can make valuable contributions. In this research, evaporation is predicted at two different meteorological stations located in arid and semi-arid regions of Iraq. Four different ML models for the prediction of evaporation-the classification and regression tree (CART), the cascade correlation neural network (CCNNs), gene expression programming (GEP), and the support vector machine (SVM)were developed and constructed using various input combinations of meteorological variables. The results reveal that the best predictions are achieved by incorporating sunshine hours, wind speed, relative humidity, rainfall, and the minimum, mean, and maximum temperatures. The SVM was found to show the best performance with wind speed, rainfall, and relative humidity as inputs at Station I (R 2 = .92), and with all variables as inputs at Station II (R 2 = .97). All the ML models performed well in predicting evaporation at the investigated locations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Al-Juboori

Beyaztaş

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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“…A detailed review of the applications of GP algorithm and some of its variants (e.g. gene expression programming) in the field of water resources research is provided by Danandeh Mehr et al (2018). The traditional/conventional GP algorithm involves several main steps as listed below (Koza 1992).…”

Section: Genetic Programmingmentioning

confidence: 99%

“…Mean square error and root mean square error are widely used to measure fitness (e.g. Parasuraman et al 2007;Danandeh Mehr et al 2018) Model selection criterion for creating mating pool Basis on which models are selected for performing genetic operations such as mutation, crossover and replication. Roulette wheel selection, tournament selection and lexictour are examples for some of the selection criteria in use Probability of mutation Likelihood of replacing a sub-tree of a model (a part of a model) with a new sub-tree.…”

Section: Parallel Multi Population Genetic Programmingmentioning

confidence: 99%

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Machine learning for downscaling: the use of parallel multiple populations in genetic programming

Sachindra

Kanae

2019

Stoch Environ Res Risk Assess

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In the implementation of traditional GP algorithm as models are evolved in a single deme (an environment in which a population of models is evolved) it may tend to produce sub-optimal models with poor generalisation skills due to lack of model diversity. As a solution to above issue, in this study the potential of evolving models in parallel multiple demes with different genetic attributes (parallel heterogeneous environments) and subsequent further evolution of some of the fittest models selected from each deme in another deme called the master deme was investigated, in relation to downscaling of large-scale climate data to daily minimum temperature (T min) and daily maximum temperature (T max). It was discovered that independent of the climate regime (i.e. warm or cold) and the geographic location of the observation station, a fraction of the fittest models (e.g. 25%) obtained from the last generation of each deme alone are sufficient for the formulation of a diverse initial population of models for the master deme. Also, independent of the climate regime and the geographic location of the observation station, both daily T min and T max downscaling models developed with the parallel multipopulation genetic programming (PMPGP) algorithm showed better generalisation skills compared to that of models developed with the traditional single deme GP, even when the amount of redundant information in the data of predictors was high. The models developed for daily T min and T max with the PMPGP algorithm simulated fewer unphysically large outliers compared to that of models developed with the GP algorithm.

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Bio‐Inspired Morphological Evolution of Metastructures with New Operation Modalities

Zhang

Barri

et al. 2023

Advanced Intelligent Systems

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Harnessing the power of natural evolution for automated exploration of novel forms of metastructures is likely to be the next technological revolution of the material science. Herein, the principles of evolution into the metamaterial design and discovery process to directly evolve thousands of metastructures with hitherto-unknown structures and new modalities of operation are embedded. In this so-called evolving metamaterial (EM) concept, evolution takes place by randomly creating an initial population of parent metamaterial entities that pass on their genetic material to their offspring through variation, reproduction, and selection. The metamaterial configurations with desired response emerge during this evolutionary process. The EM concept presents a different approach for direct morphological evolution of metamaterial microstructures using merely a piece of matter. For the biologically inspired evolution of mechanical metamaterials, this piece is chosen to be a representative unit cell to launch the design process. This paradigm shift by creating an evolutionary computational framework for the exploration of a series of proof-of-concept 2D mechanical metamaterial structures with maximum bulk modulus, maximum shear modulus, and minimum Poisson's ratio is studied. The capability of the proposed approach for discovering 3D is examined by exploring a suite of 3D configurations with maximum bulk modulus.

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Genetic programming in water resources engineering: A state-of-the-art review

Cited by 130 publications

References 167 publications

Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models

Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models

Machine learning for downscaling: the use of parallel multiple populations in genetic programming

Bio‐Inspired Morphological Evolution of Metastructures with New Operation Modalities

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