machine learning algorithm

Flash floods are considered among the most dynamic natural disasters, necessitating measures to minimize economic damages, adverse effects, and their consequences by enhancing flood sensitivity. Therefore, mapping the sensitivity of areas prone to flooding is a critical step towards flood management. Due to the scarcity of information in most of the country's watershed areas, many researchers rely on spatial analyses for hydrological studies and flood mapping. Consequently, identifying the most significant factors influencing the creation and intensification of floods, as well as mapping their sensitivity, can be one of the most important strategies for reducing flood risk. In the current study, the Siah Khor watershed in the northeast of Islam Abad-e Gharb (Kermanshah province) was selected for predicting flood-prone areas and identifying key factors affecting their occurrence. 

 To identify flood-prone areas in this study region, the Random Forest machine learning (ML) algorithm was employed within a Python environment. A map depicting the distribution of past flood events was created to predict future flooding.

Fifty-three flood events were recorded in the area, and forty-nine regions were identified as non-flood zones; 70% of the data was used for modeling and 30% for validation purposes. After reviewing previous studies and surveying the study area, eight influential factors (slope, drainage density, land use, distance from the river, geological characteristics, rainfall, slope direction, and elevation) were selected for flood zoning. The Relative Operational Characteristic (ROC) curve was utilized for model validation and efficiency evaluation. Predicting Flood-Prone Areas Using Machine Learning Methods in the Siahkhor Watershed of Kermanshah Province 

The results indicated that among the eight factors affecting flood zones, rainfall (0.35), distance from the river (0.27), and elevation (0.21) have the most significant impact on flooding in the study area, respectively. Furthermore, the evaluation of model outputs revealed that the Area Under the Curve (AUC) value for the Random Forest (RF) model was 0.98, demonstrating the high efficiency and accuracy of the RF model in mapping flood sensitivity in the study area. The largest area of flood sensitivity in the RF model corresponds to the very low category. The findings suggest that employing the Random Forest machine learning (ML) algorithm can be effectively used in analyzing flood risk.

The background of 3D modeling of fluid inclusion data goes back to use of inverse distance weighting (IDW) method in the Caixiashan Pb and Zn deposit (Sun et al., 2011). This method in spite of having some advantages such as simplicity in basis is associated with disadvantages such as uncertainty in selection of weighting function and ignoring data distribution. Today, new methods have been proposed for estimation including the support vector machine method (Dutta et al., 2010). One of this method’s capabilities is in dealing with small data sets (Dutta, 2006; Zhang et al., 1998). In this study, fluid inclusion thermodynamic parameters have been estimated using support vector regression method. Predictive model of mineralization has been provided acording to 3D models resulted for fluid inclusion data and also assumption of proper thermodynamic conditions for chalcopyrite deposition in the Sungun porphyry copper deposit.

In this study, a total of 173 data sets of fluid inclusions were obtained from 59 locations. This dataset using genetic algorithm method divided into training and testing sets (80% and 20%, respectively). Modeling of fluid inclusion thermodynamic parameters has been done by support vector regression method. The SVR is based on the statistical learning theory and the structural risk minimization.

After preparing and determination of training and test datasets, radial basis kernel function (RBF) was selected in order to estimate and model the fluid inclusion thermodynamic parameters using the support vector regression method. Better functionality was the main reason of using this kernel. In the next step, parameters were needed to be carefully determined to obtain a model with high generalization ability. In this regard, the grid search method with cross validation was used to determine optimal values for the model parameters. Model was then trained using the training dataset and finally evaluated on the test dataset. Then fluid inclusion thermodynamic parameters for each block of deposit were estimated using support vector regression method. According to mineralogical and fluid inclusion studies in the Sungun porphyry copper deposit, it has been determined that chalcopyrite deposition is related to fluids with moderate to high salinity and temperatures of 300-400 °C. The predictive model was prepared based on these conditions and estimated thermodynamic Parameters in block model. In this model, each arbitrary block has been labeled on a scale of 1 to 4 (based on the favorable conditions for chalcopyrite deposition). These labels are possibility index for copper deposition. According to possibility index, proper zones have been determined in 3D model. In order to performance evaluation of support vector regression method, the predictive model was compared with 3D model of copper grade. The results of this comparison showed that prepared predictive 3D model has high consistent with copper grade block model.

In this study, 3D modeling of fluid inclusion data was performed to estimate the thermodynamic parameters affecting mineralization (homogenization and eutectic temperatures and salinity) using support vector regression method to determine potential mineralization points in the area. Using the 3D models, we found the homogenization and eutectic temperatures and fluids salinity (in different ranges of these factors) in the Sungun porphyry copper deposit. To evaluate the 3D modeling efficiency in advancing the exploration process of the porphyry deposits, the conformity between mineralization and thermodynamic variations of the fluid inclusions was investigated and, based on it; a tool called “Predictive Model” was presented for the evaluation of the occurrence of mineralization in different parts of the region. A comparison of the SVR-based predictive model and the copper grade block model shows acceptable conformity in low, medium, and high-grade regions.