Forest fires caused by different environmental and human factors are responsible for the extensive destruction of natural and economic resources. Modern machine learning techniques have become popular in developing very accurate and precise susceptibility maps of various natural disasters to help reduce the occurrence of such calamities. The present study has applied and tested multiple algorithms to map the areas susceptible to wildfire in the Mediterranean Region of Turkey. Besides, the performance of XGBoost, CatBoost, Gradient Boost, AdaBoost, and LightGBM methods for wildfire susceptibility mapping is also examined. The results have revealed the higher testing accuracy of CatBoost (95.47%) algorithm, followed by LightGBM (94.70%), XGBoost (88.8%), AdaBoost (86.0%), and GBM (84.48%) algorithms. Resultant wildfire susceptibility maps provide proper inventories for forest engineers, planners, and local governments for future policies regarding disaster management in Turkey.

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.

Integrating machine learning technologies into artificial intelligence (AI) is at the forefront of the scientific and technological tools employed to combat the COVID-19 pandemic. This study assesses different uses and deployments of modern technology for combating the COVID-19 pandemic at various levels, such as image processing, tracking of disease, prediction of outcomes, and computational medicine. The results prove that computerized tomography (CT) scans help to diagnose patients infected by COVID-19. This includes two-sided, multilobar ground glass opacification (GGO) by a posterior distribution or peripheral, primarily in the lower lobes, and fewer recurrences in the intermediate lobe. An extensive search of modern technology databases relating to COVID-19 was undertaken. Subsequently, a review of the extracted information from the database search looked at how technology can be employed to tackle the pandemic. We discussed the technological advancements deployed to alleviate the communicability and effect of the pandemic. Even though there are many types of research on the use of technology in combating COVID-19, the application of technology in combating COVID-19 is still not yet fully explored. In addition, we suggested some open research issues and challenges in deploying AI technology to combat the global pandemic.

The current work aims to examine the effect of meteorological parameters as well as the temporal variation on the Burdur Lake surface water body areas in Turkey. The data for the surface area of Burdur Lake was collected over 34 years between 1984 and 2019 by Google Earth Engine. The seasonal variation in the water bodies area was collected using our proposed extraction method and 570 Landsat images. The reduction in the total area of the lake was observed between 206.6 km² in 1984 to 125.5 km² in 2019. The vegetation cover and meteorological parameters collected that affect the observed variation of surface water bodies for the same area include precipitation, evapotranspiration, albedo, radiation, and temperature. The selected meteorological variables influence the reduction in lake area directly during various seasons. Correlations showed a strong positive or negative significant relationship between the reduction and the selected meteorological variables. A factor analysis provided three components that explain 82.15% of the total variation in the data. The data provide valuable references for decision makers to develop sustainable agriculture and industrial water use policies to preserve water resources as well as cope with potential climate changes.

Policy makers and the public are increasingly concerned with the determination of landfill-siting input criteria (DLSIC) in landfill modelling procedures as an area of research. Thus, its procedures are complicated and decision makers are increasingly pressured. These procedures can be considerably develop in order to reduce the negative effect of landfill locations on the environment, economy, and society. In this review article, literature related to the developments of 64 models and their procedures in the past 18 years (from 1997 to 2014) were comprehensively survey. DLSIC are determined through a conventional method. The frequency of criterion usage reflects the limitation of Conventional method for DLSIC. Moreover, some of these studies utilize unrelated criteria that are time-consuming, costly, arduous, and fruitless. Potential improvement in Geographic information systems GIS modelling parameter for landfill sites via utilizing multivariate analysis (MVA) instead of Conventional method (CM) through for DLSIC (e.g., Input variables, Accuracy, objectivity, reliability of criteria, time consumption, cost and comprehensiveness) were emphasize. It can be conclude that expenses can be reduce by implementing MVA in DLSIC for landfill modelling using geographic information systems (GIS) based on the corresponding significant level. Moreover, the determined criteria can be accurate, satisfying, sufficient, and free of bias from experts and human error.

The increasing use of low-cost sensors in monitoring the surrounding environment, requires efficient handling of sensor drift and sensor errors.. Therefore, there is a pressing need to develop lightweight methods to determine and calibrate the sensor’s readings accurately. This paper focuses on the calibration of low-cost sensors using lightweight techniques to effectively detect and correct sensor drifts. The proposed approach combines clustering, which offloads computational burdens to cluster heads, with temporal and spatial estimation among neighboring sensors such as Inverse Distance Weighting (IDW). Additionally, Autoregression (AR) and InterQuartile Range (IQR) techniques are employed to monitor the stability of sensor readings based on previous measurements. Through simulation experiments using a dataset from the Intel Berkeley Research Laboratory (IBRL), the effectiveness of the proposed method in reliably detecting and improving the sensor is demonstrated. These findings contribute to advancing sensor calibration techniques for low-cost sensors, enhancing their reliability and accuracy in environmental monitoring applications.