Flooding is the main recurring natural disaster in Sungai Pinang catchment, Malaysia. Flash flood susceptibility mapping (FFSM) explains a key component of flood risk analysis and enables efficient estimation of the spatial extent of flood characteristics. The current study applied four machine learning models (i.e., Logistic Regression (LR), K-Nearest Neighbors (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)) ensembled with the Statistical Index (SI) to develop flash flood susceptibility mapping (FFSM). 110 flash flood locations in the Sungai Pinang catchment were used in this study. Genetic algorithm (GA) was combined with Fuzzy Unordered Rules Induction Algorithm (FURIA), Rotation Forest, and Random Subspace for the feature selection method (FSM). The results showed that GA-FURIA outperformed the other two models in terms of accuracy based on the FSM. Twelve flash flood variables were selected by GA-FURIA. The FFSM results showed that the SI-RF model has the highest area under the receiver operating characteristics (AUROC) curve of success rate (0.978), whereas the SI-XGB has the best AUROC in terms of validation rate (0.997). The findings suggest that the twelve ideal conditioning variables may be used to optimize FFSM development.

Geographical Information System (GIS) and image processing techniques can be used for the identification of morphological features and analyzing properties of basin. The morphometric parameters of basin include the linear, aerial and relief aspects. The present study focused on the evaluation and analysis of the morphometric parameters such as Stream Order (Nu), Stream Length (Lu), Bifurcation Ratio (Rb), Drainage Density (Dd), Stream Frequency (Fs), Texture Ratio (T), Elongation Ratio (Re), Circulatory Ratio (Rc) and Form Factor (Rf) e.t.c. The study areas are Asa River Basin and Oyun River Basin. The GIS based morphometric analysis of these River Basins revealed that both the Asa River Basin and Oyun River Basins are fourth order basins which indicates their drainage pattern to be mainly dendritic type thereby showing homogeneity in texture and less of structural control. Total stream number of Asa River Basin is 54, in which 37 are first order, 12 are second order, 4 are third order and 1 is fourth order, and while the total stream number of Oyun River Basin is 56, in which 45 are first order, 7 are second order, 2 are third order and 2 are fourth order streams. The length of stream segment is maximum for first order stream and decreases as the stream order increases. The drainage density (Dd) of study area of Asa River Basin is 0.39km/km2 and Oyun River Basin is 0.36km/km2, an indication that the two River Basins are not much affected by structural disturbance. The study and result from the findings will help in determining peak runoff in the basin which helps in managing the water resources in the basins for sustainable development of the basin area.

Comparative study of Ikirun and Osogbo Slag on concrete grade 20