Ensemble gene expression programming: a new approach for evolution of parsimonious streamflow forecasting model

Rahmani-Rezaeieh, Aidin; Mohammadi, Mirali; Mehr, Ali Danandeh

doi:10.1007/s00704-019-02982-x

Cited by 33 publications

(11 citation statements)

References 46 publications

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“…The results showed that combining the outcomes of classic and symbolic regression models may enhance the precision of the TSP forecasts. This is well in agreement with the findings of the previous studies such as Rahmani-Rezaeieh et al [31] and Danandeh Mehr and Safari [10] where the ensemble GP approach was introduced as the robust modeling technique in hydrological studies. In comparison with simple averaging and linear ensemble, the forecasted values of TSP using the new EGP model were closer to those of observed data set in both training and testing samples.…”

Section: Discussionsupporting

confidence: 92%

An ensemble genetic programming model for seasonal precipitation forecasting

Mehr

2020

SN Appl. Sci.

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Seasonal precipitation forecasting is one of the most challenging tasks in stochastic hydrology. This article proposes a new ensemble model, called EGP, to a season-ahead forecast of total seasonal precipitation. The EGP integrates evolutionary genetic programming (GP) and gene expression programming (GEP) techniques with multiple linear regression to increase forecasting accuracy of standalone GP and GEP models, while it secures their explicit structure. The EGP model was trained and validated using 88 years (1930-2017) of measured precipitation data from Muratpasa Station, Antalya, Turkey. The model performance was evaluated in terms of different statistical error measures and cross-validated with two other ensemble models as well as the state-of-the-art random forest developed in this study as the benchmark. The results showed that the proposed model can increase the forecasting accuracy of the best standalone GP and GEP models up to 30%. The EGP was also found to be superior to random forest, particularly in predicting low and high seasonal precipitation amount. This model is explicit, easy to evolve, and therefore, motivating to be used in practice.

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Section: Discussionsupporting

confidence: 92%

An ensemble genetic programming model for seasonal precipitation forecasting

Mehr

2020

SN Appl. Sci.

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“…[38], was used in this study to develop standalone GP-based SPEI forecasting models. The software has been successful applied in variety of hydrological modelling tasks in the recent studies [e.g., [39][40]. The evolutionary parameters adopted for GPdotNetV5.0 setup were tabulated in Table 2.…”

Section: Rf and Baseline Gp Resultsmentioning

confidence: 99%

Wavelet Packet-Genetic Programming: A New Model for Meteorological Drought Hindcasting

2021

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This study presents developing procedures and verification of a new hybrid model, namely wavelet packet-genetic programming (WPGP) for short-term meteorological drought forecast. To this end, the multi-temporal standardized precipitation evapotranspiration index (SPEI) has been used as the drought quantifying parameter at two meteorological stations at Ankara province, Turkey. The new WPGP model comprises two main steps. In the first step, the wavelet packet, which is a generalization of the well-known wavelet transform, is used to decompose the SPEI series into deterministic and stochastic sub-signals. Then, classic genetic programming (GP) is applied to formulate the deterministic sub-signal considering its effective lags. To characterize the stochastic component, different theoretical probability distribution functions were assessed, and the best one was selected to integrate with the GPevolved function. The efficiency of the new model was cross-validated with the first order autoregressive (AR1), GP, and random forest (RF) models developed as the benchmarks in the present study. The results showed that the WPGP is a robust model, superior to AR1 and RF, and significantly increases the predictive accuracy of the standalone GP model.

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“…The regression cell, in this topology, treats the zero and non-zero flows as a continuum and makes a prediction. Conventionally used models for streamflow forecasting only include the regression cell of the wide network (Cigizoglu, 2005;Kişi, 2009;Makwana and Tiwari, 2014;Rahmani-Rezaeieh et al, 2020). This configuration typically has a single input layer, a hidden layer and an output layer and is referred to as shallow topology (or shallow model) in this study.…”

Section: Wide Topology For Modeling Intermittent Streamflowsmentioning

confidence: 99%

Deep, Wide, or Shallow? Artificial Neural Network Topologies for Predicting Intermittent Flows

Forghanparast

Hernandez

Uddameri

2021

Preprint

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Abstract. Intermittent Rivers and Ephemeral Streams (IRES) comprise 60 % of all streams in the US and about 50 % of the streams worldwide. Furthermore, climate-driven changes are expected to force a shift towards intermittency in currently perennial streams. Most modeling studies have treated intermittent streamflows as a continuum. However, it is better to envision flow data of IRES as a “mixture-type”, comprised of both flow and no-flow regimes. It is therefore hypothesized that data-driven models with both classification and regression cells can improve the streamflow forecasting abilities in these streams. Deep and wide Artificial Neural Networks (ANNs) comprising of classification and regression cells were developed here by stacking them in series and parallel configurations. These deep and wide network architectures were compared against the commonly used single hidden layer ANNs (shallow), as a baseline, for modeling IRES flow series under the continuum assumption. New metrics focused on no-flow persistence and transitions between flow and no-flow states were formulated using contingency tables and Markov chain analysis. Nine IRES across the state of Texas, US, were used as a wide range of testbeds with different hydro-climatic characteristics. Model overfitting and the curse-of-dimensionality were reduced using extreme learning machines (ELM), and balancing training data using the synthetic minority oversampling technique (SMOTE), greedy learning and Least Absolute Shrinkage and Selection Operator (LASSO). The addition of classifier cells greatly improved the ability to distinguish between no-flow and flow states, in turn, improving the ability to capture no-flow persistence (dryness) and transitions to and from flow states (dryness initiation and cessation). The wide network topology provided better results when the focus was on capturing low flows and the deep topology did well in capturing extreme flows (zero and > 75th percentile).

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Ensemble gene expression programming: a new approach for evolution of parsimonious streamflow forecasting model

Cited by 33 publications

References 46 publications

An ensemble genetic programming model for seasonal precipitation forecasting

An ensemble genetic programming model for seasonal precipitation forecasting

Wavelet Packet-Genetic Programming: A New Model for Meteorological Drought Hindcasting

Deep, Wide, or Shallow? Artificial Neural Network Topologies for Predicting Intermittent Flows

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