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Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, the coronary artery disease (CAD) is considered as a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is the use of medical imaging, e.g., angiography. However, angiography is known for being costly and also associated with a number of side effects. Hence, the purpose of this study is to increase the accuracy of coronary heart disease diagnosis through selecting significant predictive features in order of their ranking. In this study, we propose an integrated method using machine learning. The machine learning methods of random trees (RTs), decision tree of C5.0, support vector machine (SVM), decision tree of Chi-squared automatic interaction detection (CHAID) are used in this study. The proposed method shows promising results and the study confirms that RTs model outperforms other models.

Hydrological droughts are characterized based on their duration, severity, and magnitude. Among the most critical factors, precipitation, evapotranspiration, and runoff are essential in modeling the droughts. In this study, three indices of drought, i.e., Standardized Precipitation Index (SPI), Standardized Streamflow Index (SSI), and Standardized Precipitation Evapotranspiration Index (SPEI), are modeled using Support Vector Regression (SVR), Gene Expression Programming (GEP), and M5 model trees (MT). The results indicate that SPI delivered higher accuracy. Moreover, MT model performed better in predicting SSI by a CC of 0.8195 and a RMSE of 0.8186.

Identification and management of the groundwater quality are of utmost importance for maintaining freshwater resources in arid and semi-arid areas, which is essential for sustainable development. Based on the quality of the groundwater in various areas, local policymakers and water resource managers can allocate the usage of resources for either drinking or agricultural purposes. This research aims to identify suitable areas of water pumping for drinking and agricultural harvest in the Tabriz aquifer, located in East Azerbaijan province, northwest Iran. A groundwater compatibility study was conducted by analyzing Electrical conductivity (EC), total dissolved solids (TDS), Chloride (Cl), Calcium (Ca), Magnesium (Mg), Sodium (Na), Potassium (K), Sulfate (SO4), Total hardness (TH), Bicarbonate (HCO3), pH, carbonate (CO3), the and Sodium Adsorption Ratio (SAR) obtained from 39 wells in the time period from 2003 to 2014. The Water Quality Index (WQI) and irrigation water quality (IWQ) index are respectively utilized due to their high importance in identifying the quality of water resources for irrigation and drinking purposes. The WQI index zoning for drinking classified water as excellent, good, or poor. The study concludes that most drinking water harvested for urban and rural areas is ‘excellent water’ or ‘good water’. The IWQ index average for the study area is reported to be in the range of 25.9 to 34.55. The results further revealed that about 37 percent (296 km2) of groundwater has high compatibility, and 63 percent of the study area (495 km2) has average compatibility for agricultural purposes. The trend of IWQ and WQI indexes demonstrates that groundwater quality has been declining over time.

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