An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction

Machine learning prediction of mechanical properties of concrete: Critical review

Compressive strength of concrete is one of the most determinant parameters in the design of engineering structures. This parameter is generally determined by conducting several tests at different ages of concrete in spite of the fact that such tests are not only costly but also time-consuming. As an alternative to these tests, machine learning (ML) techniques can be used to estimate experimental results. However, the dependence of compressive strength on different parameters in the fabrication of concrete makes the prediction problem challenging, especially in the case of concrete with partial replacements for cement. In this investigation, an extreme learning machine (ELM) is combined with a metaheuristic algorithm known as grey wolf optimizer (GWO) and a novel hybrid ELM-GWO model is proposed to predict the compressive strength of concrete with partial replacements for cement. To evaluate the performance of the ELM-GWO model, five of the most well-known ML models including an artificial neural network (ANN), an adaptive neuro-fuzzy inference system (ANFIS), an extreme learning machine, a support vector regression with radial basis function (RBF) kernel (SVR-RBF), and another SVR with a polynomial function (Poly) kernel (SVR-Poly) are developed. Finally, the performance of the models is compared with each other. The results of the paper show that combining the ELM model with GWO can efficiently improve the performance of this model. Also, it is deducted that the ELM-GWO model is capable of reaching superior performance indices in comparison with those of the other models.

A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement

Flash flood is a typical natural hazard that occurs within a short time with high flow velocities and is difficult to predict. In this study, we propose and validate a new soft computing approach that is an integration of an Extreme Learning Machine (ELM) and a Particle Swarm Optimization (PSO), named as PSO-ELM, for the spatial prediction of flash floods. The ELM is used to generate the initial flood model, whereas the PSO was employed to optimize the model. A high frequency tropical typhoon area at Northwest of Vietnam was selected as a case study. In this regard, a geospatial database for the study area was constructed with 654 flash flood locations and 12 influencing factors (elevation, slope, aspect, curvature, toposhade, topographic wetness index, stream power index, stream density, NDVI, soil type, lithology, and rainfall). The model performance was validated using several evaluators such as kappa statistics, root-mean-square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and area under the ROC curve (AUC-ROC) and compared to three state-of-the-art machine learning techniques, including multilayer perceptron neural networks, support vector machine, and C4.5 decision tree. The results revealed that the PSO-ELM model has high prediction performance (kappa statistics = 0.801, RMSE = 0.281; MAE = 0.079, R2 = 0.829, AUC-ROC = 0.954) and successfully outperformed the three machine learning models. We conclude that the proposed model is a new tool for the prediction of flash flood susceptibility at high frequency tropical typhoon areas.

A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility mapping

Water quality monitoring is an important component of water resources management. In order to predict two water quality variables, namely dissolved oxygen (DO; mg/L) and chlorophyll-a (Chl-a; µg/L) in the Small Prespa Lake in Greece, two standalone deep learning (DL) models, the long short-term memory (LSTM) and convolutional neural network (CNN) models, along with their hybrid, the CNN–LSTM model, were developed. The main novelty of this study was to build a coupled CNN–LSTM model to predict water quality variables. Two traditional machine learning models, support-vector regression (SVR) and decision tree (DT), were also developed to compare with the DL models. Time series of the physicochemical water quality variables, specifically pH, oxidation–reduction potential (ORP; mV), water temperature (°C), electrical conductivity (EC; µS/cm), DO and Chl-a, were obtained using a sensor at 15-min intervals from June 1, 2012 to May 31, 2013 for model development. Lag times of up to one (t − 1) and two (t − 2) for input variables pH, ORP, water temperature, and EC were used to predict DO and Chl-a concentrations, respectively. Each model’s performance in both training and testing phases was assessed using statistical metrics including the correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE), their normalized equivalents (RRMSE, RMAE; %), percentage of bias (PBIAS), Nash–Sutcliffe coefficient ($$E_{NS}$$), Willmott’s Index, and graphical plots (Taylor diagram, box plot and spider diagram). Results showed that LSTM outperformed the CNN model for DO prediction, but the standalone DL models yielded similar performances for Chl-a prediction. Generally, the hybrid CNN–LSTM models outperformed the standalone models (LSTM, CNN, SVR and DT models) in predicting both DO and Chl-a. By integrating the LSTM and CNN models, the hybrid model successfully captured both the low and high levels of the water quality variables, particularly for the DO concentrations.

Laura Cornejo Bueno

Papers

Predicting compressive strength of lightweight foamed concrete using extreme learning machine model