Hybrid data intelligent models and applications for water level prediction

Artificial intelligence (AI) models have been successfully applied in modeling engineering problems, including civil, water resources, electrical, and structure. The originality of the presented chapter is to investigate a non-tuned machine learning algorithm, called self-adaptive evolutionary extreme learning machine (SaE-ELM), to formulate an expert prediction model. The targeted application of the SaE-ELM is the prediction of river water level. Developing such water level prediction and monitoring models are crucial optimization tasks in water resources management and flood prediction. The aims of this chapter are (1) to conduct a comprehensive survey for AI models in water level modeling, (2) to apply a relatively new ML algorithm (i.e., SaE-ELM) for modeling water level, (3) to examine two different time scales (e.g., daily and monthly), and (4) to compare the inspected model with the extreme learning machine (ELM) model for validation. In conclusion, the contribution of the current chapter produced an expert and highly optimized predictive model that can yield a high-performance accuracy

Design of a new hybrid artificial neural network method based on decision trees for calculating the Froude number in rigid rectangular channels

A vital topic regarding the optimum and economical design of rigid boundary open channels such as sewers and drainage systems is determining the movement of sediment particles. In this study, the incipient motion of sediment is estimated using three datasets from literature, including a wide range of hydraulic parameters. Because existing equationsdo not consider the effect of sediment bed thickness on incipient motion estimation, this parameter is applied in this study along with the multilayer perceptron (MLP), a hybrid method based on decision trees (DT) (MLP-DT), to estimate incipient motion. According to a comparison with the observed experimental outcome, the proposed method performs well (MARE = 0.048, RMSE = 0.134, SI = 0.06, BIAS = –0.036). The performance of MLP and MLP-DT is compared with that of existing regression-based equations, and significantly higher performance over existing models is observed. Finally, an explicit expression for practical engineering is also provided

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

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 regio

Baltische Flora, enthaltend die in Est-, Liv-, und Kurland wilddwachsenden Samenpflanzen und höheren Sporenpflanzen : eine zur Erlangung der Magister-Würde verfasste und mit Bewilligung der Hochverordneten physico-mathematischen Facultät der Kaiserl. Universität zu Dorpat zur öffentlichen Vertheidigung bestimmte Abhandlung

http://tartu.ester.ee/record=b1868533~S1*es

Design of a fuzzy differential evolution algorithm to predict non-deposition sediment transport

Abstract Since the flow entering a sewer contains solid matter, deposition at the bottom of the channel is inevitable. It is difficult to understand the complex, three-dimensional mechanism of sediment transport in sewer pipelines. Therefore, a method to estimate the limiting velocity is necessary for optimal designs. Due to the inability of gradient-based algorithms to train Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for non-deposition sediment transport prediction, a new hybrid ANFIS method based on a differential evolutionary algorithm (ANFIS-DE) is developed. The training and testing performance of ANFIS-DE is evaluated using a wide range of dimensionless parameters gathered from the literature. The input combination used to estimate the densimetric Froude number (Fr) parameters includes the volumetric sediment concentration (C V ), ratio of median particle diameter to hydraulic radius (d/R), ratio of median particle diameter to pipe diameter (d/D) and overall friction factor of sediment (λ s ). The testing results are compared with the ANFIS model and regression-based equation results. The ANFIS-DE technique predicted sediment transport at limit of deposition with lower root mean square error (RMSE = 0.323) and mean absolute percentage of error (MAPE = 0.065) and higher accuracy (R 2 = 0.965) than the ANFIS model and regression-based equations

A new hybrid decision tree method based on two artificial neural networks for predicting sediment transport in clean pipes

A new hybrid decision tree (DT) technique based on two artificial neural networks (ANN), namely multilayer perceptron (MLP) and radial basis function (RBF), is proposed to predict sediment transport in clean pipes (i.e. without deposition). The parameters affecting densimetric Froude number (Fr) prediction were extracted from the literature in order to build the model proposed in this study. The effect of each parameter is first examined using MLP and RBF and a sensitivity analysis. According to the sensitivity analysis, the optimal model indicates that using the volumetric sediment concentration (CV), median diameter of particle size distribution to pipe diameter (d/D) and ratio of median diameter of particle size distribution to hydraulic radius (d/R) parameters yield the best Fr prediction results. Subsequently, the hybrid DT-MLP and DT-RBF model results are compared with MLP and RBF. According to the results, MLP with all models predicted Fr more accurately than RBF, and DT-MLP exhibited the best performance (R2 = 0.975, MARE = 0.063, RMSE = 0.328, SI = 00.081, BIAS = −0.01). Moreover, the comparison between DT-MLP and existing regression-based equations indicates that the models presented in the current study are superior. Keywords: Artificial Neural Network (ANN), Decision Tree (DT), Hybrid model, Multilayer Perceptron (MLP), Radial Basis Function (RBF), Sediment transpor

Short-Term Precipitation Forecasting Based on the Improved Extreme Learning Machine Technique

In this study, an improved version of the Extreme Learning Machine, namely the Improved Weighted Regularization ELM (IWRELM), is proposed for hourly precipitation forecasting that is multi-steps ahead. After finding the optimal values of the proposed method, including the number of hidden neurons, the activation function, the weight function, the regularization parameter, and the effect of orthogonality, the IWRELM model was calibrated and validated. Thereafter, the calibrated IWRELM model was used to estimate precipitation up to ten hours ahead. The results indicated that the proposed IWRELM (R = 0.9996; NSE = 0.9993; RMSE = 0.015; MAE = 0.0005) has acceptable accuracy in short-term hourly precipitation forecasting up to ten hours ahead

Time-Series-Based Air Temperature Forecasting Based on the Outlier Robust Extreme Learning Machine

In this study, an improved version of the outlier robust extreme learning machine (IORELM) is introduced as a new method for multi-step-ahead hourly air temperature forecasting. The proposed method was calibrated and used to estimate the hourly air temperature for one to ten hours in advance after finding its most optimum values (i.e., orthogonality effect, activation function, regularization parameter, and the number of hidden neurons). The results showed that the proposed IORELM has an acceptable degree of accuracy in predicting hourly temperatures ten hours in advance (R = 0.95; NSE = 0.89; RMSE = 3.74; MAE = 1.92)

Novel Hybrid Statistical Learning Framework Coupled with Random Forest and Grasshopper Optimization Algorithm to Forecast Pesticide Use on Golf Courses

Golf course maintenance requires the use of several inputs, such as pesticides and fertilizers, that can be harmful to human health or the environment. Understanding the factors associated with pesticide use on golf courses may help golf-course managers reduce their reliance on these products. In this study, we used a database of about 14,000 pesticide applications in the province of Québec, Canada, to develop a novel hybrid machine learning approach to predict pesticide use on golf courses. We created this proposed model, called RF-SVM-GOA, by coupling a support vector machine (SVM) with random forest (RF) and the grasshopper optimization algorithm (GOA). We applied RF to handle the wide range of datasets and GOA to find the optimal SVM settings. We considered five different dependent variables—region, golf course ID, number of holes, year, and treated area—as input variables. The experimental results confirmed that the developed hybrid RF-SVM-GOA approach was able to estimate the active ingredient total (AIT) with a high level of accuracy (R = 0.99; MAE = 0.84; RMSE = 0.84; NRMSE = 0.04). We compared the results produced by the developed RF-SVM-GOA model with those of four tree-based techniques including M5P, random tree, reduced error pruning tree (REP tree), and RF, as well as with those of two non-tree-based techniques including the generalized structure of group method of data handling (GSGMDH) and evolutionary polynomial regression (EPR). The computational results showed that the accuracy of the proposed RF-SVM-GOA approach was higher, outperforming the other methods. We analyzed sensitivity to find the most effective variables in AIT forecasting. The results indicated that the treated area is the most effective variable in AIT forecasting. The results of the current study provide a method for increasing the sustainability of golf course management