Search

Current study presents the boosting of two base models, including a Heuristic Search Algorithm for finding the k shortest paths (K*) and an alternating model tree (AM Tree) through combining bagging (BA), dagging (DA), and random subspace (RS) hybridize models, to predict monthly suspended sediment load (SSL) in subtropical monsoon climatic regions. In this study, the rainfall (R), streamflow (Q), water level (W), and SSL with various lag-times were fed into the model as inputs. The study found that each input scenario has a unique ability to predict SSL. Different input scenario both manually and automatically derived to improve the modeling performance and reveal impact of the potential variable on the SSL modeling. Model fit outcomes finally were evaluated using graphical and statistical performance metrics. The most effective input scenario is the one that includes all possible input variables. The study also revealed that the hybridized ensemble models outperformed standalone model. The hybrid model of BAK* performed superior to other models. While the standalone model of K* had the lowest prediction ability. Hybridized ensemble models assist in capturing extreme SSL values, especially the maximum SSL value. Results suggest that BA-K* model is superior to other models with R² = 0.80, RMSE = 772.94 Kg/s, PBIAS = 0.06, and NSE = 0.79. The reliability analysis also confirmed that the BA-K* model provided the optimal performance. [ABSTRACT FROM AUTHOR]

In this study, the main goal is to compare the predictive capability of Support Vector Machines (SVM) with four Bayesian algorithms namely Naïve Bayes Tree (NBT), Bayes network (BN), Naïve Bayes (NB), Decision Table Naïve Bayes (DTNB) for identifying landslide susceptibility zones in Pauri Garhwal district (India). First, landslide inventory map was built using 1295 historical landslide data, then in total sixteen influencing factors were selected and tested for landslide susceptibility modelling. Performance of the model was evaluated and compared using Statistical based index methods, Area under the Receiver Operating Characteristic (ROC) curve named AUC, and Chi-square method. Analysis results show that that the SVM has the highest prediction capability, followed by the NBT, DTNBT, BN and NB, respectively. Thus, this study confirms that the SVM is one of the benchmark models for the assessment of susceptibility of landslides. [ABSTRACT FROM AUTHOR]