Non-linear input variable selection approach integrated with non-tuned data intelligence model for streamflow pattern simulation

Streamflow modeling is considered as an essential component for water resources planning and management. There are numerous challenges related to streamflow prediction that are facing water resources engineers. These challenges due to the complex processes associated with several natural variables such as non-stationarity, non-linearity, and randomness. In this study, a new model is proposed to predict long-term streamflow. Several lags that cover several years are abstracted using the potential of Extreme Gradient Boosting (XGB) then after the selected inputs variables are imposed into the predictive model (i.e., Extreme Learning Machine (ELM)). The proposed model is compared with the stand-alone schema in which the optimum lags of the variables are supplied into the XGB and ELM models. Hydrological variables including rainfall, temperature and evapotranspiration are used to build the model and predict the streamflow at Goksu-Himmeti basin in Turkey. The results showed that XGB model performed an excellent result in which can be used for predicting the streamflow pattern. Also, it is clear from the attained results that the accuracy of the streamflow prediction using XGB technique could be improved when the high number of lags was used. However, the implementation of the XGB is tree-based technique in which several issues could be raised such as overfitting problem. The proposed schema XGBELM in which XGB approach is selected the correlated inputs and ranking them according to their importance; then after, the selected inputs are supplied into the ELM model for the prediction process. The XGBELM model outperformed the stand-alone schema of both XGB and ELM models and the high-lagged schema of the XGB. It is important to indicate that the XGBELM model found to improve the prediction ability with minimum variables number.Validerad;2019;Nivå 2;2019-10-09 (johcin)</p

The model of local wisdom for smart wellness tourism with optimization multilayer perceptron

This study focuses on the influence of variations in the number of hidden layers in the Artificial Neural Network (ANN) method on model performance and interpretability of results. The method applied involves integrating local wisdom to optimize the Artificial Neural Network (ANN) model. This approach combines locally relevant aspects with a conceptual framework to improve ANN performance. Evaluation of the results involves the performance metrics, MSE, MAE, RMSE, and F2 Score to find the best-hidden layer pattern in the Artificial Neural Network (ANN) model. The test results are based on a dataset with five indicators totaling 30 input layers and tested on the Multi Layer Perceptron (MLP) model. The results of testing a dataset with 30 input layers divided into 5 indicators produced performance metrics MSE 0.01346, MAE 0.09740, and RMSE 0.12094. The concept with a 16-hidden layer model pattern has high complexity and produces better predictions with fewer errors. Additionally, hidden layer 11 performs well, displaying a solid capacity to describe the variance in target data with an R2_Score of 0.17374. This produces two groups of ANN test results: the first group with improved accuracy (MSE, MAE, RMSE), and the second group highlights the optimal performance of hidden layers 16 and 11 (R2 Score). Local wisdom contributes to smart wellness