Comparison of multi-criteria and artificial intelligence models for land-subsidence susceptibility zonation.

Land subsidence (LS) in arid and semi-arid areas, such as Iran, is a significant threat to sustainable land management. The purpose of this study is to predict the LS distribution by generating land subsidence susceptibility models (LSSMs) for the Shahroud plain in Iran using three different multi-criteria decision making (MCDM) and five different artificial intelligence (AI) models. The MCDM models we used are the VlseKriterijumska Optimizacija IKompromisno Resenje (VIKOR), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Complex Proportional Assessment (COPRAS), and the AI models are the extreme gradient boosting (XGBoost), Cubist, Elasticnet, Bayesian multivariate adaptive regression spline (BMARS) and conditional random forest (Cforest) methods. We used the Receiver Operating Characteristic (ROC) curve, Area Under Curve (AUC) and different statistical indices,i.e. accuracy, sensitivity, specificity, F score, Kappa, Mean Absolute Error (MAE) and Nash-Sutcliffe Criteria (NSC)to validate and evaluate the methods. Based on the different validation techniques, the Cforest method yielded the best results with minimum and maximum values of 0.04 and 0.99, respectively. According to the Cforest model, 30.55% of the study area is extremely vulnerable to land subsidence. The results of our research will be of great help to planners and policy makers in the identification of the most vulnerable regions and the implementation of appropriate development strategies in this area.

Analysis of Outbreak and Global Impacts of the COVID-19.

Corona viruses are a large family of viruses that are not only restricted to causing illness in humans but also affect animals such as camels, cattle, cats, and bats, thus affecting a large group of living species. The outbreak of Corona virus in late December 2019 (also known as COVID-19) raised major concerns when the outbreak started getting tremendous. While the first case was discovered in Wuhan, China, it did not take long for the disease to travel across the globe and infect every continent (except Antarctica), killing thousands of people. Since it has become a global concern, different countries have been working toward the treatment and generation of vaccine, leading to different speculations. While some argue that the vaccine may only be a few weeks away, others believe that it may take some time to create the vaccine. Given the increasing number of deaths, the COVID-19 has caused havoc worldwide and is a matter of serious concern. Thus, there is a need to study how the disease has been propagating across continents by numbers as well as by regions. This study incorporates a detailed description of how the COVID-19 outbreak started in China and managed to spread across the globe rapidly. We take into account the COVID-19 outbreak cases (confirmed, recovered, death) in order to make some observations regarding the pandemic. Given the detailed description of the outbreak, this study would be beneficial to certain industries that may be affected by the outbreak in order to take timely precautionary measures in the future. Further, the study lists some industries that have witnessed the impact of the COVID-19 outbreak on a global scale.

A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility.

Land subsidence (LS) is a significant problem that can cause loss of life, damage property, and disrupt local economies. The Semnan Plain is an important part of Iran, where LS is a major problem for sustainable development and management. The plain represents the changes occurring in 40% of the country. We introduce a novel-ensemble intelligence approach (called ANN-bagging) that uses bagging as a meta- or ensemble-classifier of an artificial neural network (ANN) to predict LS spatially on the Semnan Plain in Semnan Province, Iran. The ensemble model's goodness-of-fit (to training data) and prediction accuracy (of the validation data) are compared to benchmarks set by ANN-bagging. A total of 96 locations of LS and 12 LS conditioning factors (LSCFs) were collected. Each feature in the LS inventory map (LSIM) was randomly assigned to one of four groups or folds, each comprising 25% of cases. The novel ensemble model was trained using 75% (3 folds) and validated with the remaining 25% (1 fold) in a four-fold cross-validation (CV) system, which is used to control for the effects of the random selection of the training and validation datasets. LSCFs for LS prediction were selected using the information-gain ratio and multi-collinearity test methods. Factor significance was evaluated using a random forest (RF) model. Groundwater drawdown, land use and land cover, elevation, and lithology were the most important LSCFs. Using the k-fold CV approaches, twelve LS susceptibility maps (LSSMs) were prepared as each fold employed all three models (ANN-bagging, ANN, and bagging). The LS susceptibility mapping showed that between 5.7% and 12.6% of the plain had very high LS susceptibility. All three models produced LS susceptibility maps with acceptable prediction accuracies and goodness-of-fits, but the best maps were produced by the ANN-bagging ensemble method. Overall, LS risk was highest in agricultural areas with high groundwater drawdown in the flat lowlands on quaternary sediments (Qcf). Groundwater extraction rates should be monitored and potentially limited in regions of severe or high LS susceptibility. This investigation details a novel methodology that can help environmental planners and policy makers to mitigate LS to help achieve sustainability.