RESUMO
The main aim of this study was to predict current and future flood susceptibility under three climate change scenarios of RCP2.6 (i.e., optimistic), RCP4.5 (i.e., business as usual), and RCP8.5 (i.e., pessimistic) employing four machine learning models, including Gradient Boosting Machine (GBM), Random Forest (RF), Multilayer Perceptron Neural Network (MLP-NN), and Naïve Bayes (NB). The study was conducted for two watersheds in Canada, namely Lower Nicola River, BC and Loup, QC. Three statistical metrics were used to validate the models: Receiver Operating Characteristic Curve, Figure of Merit, and F1-score. Findings indicated that the RF model had the highest accuracy in providing the flood susceptibility maps (FSMs). Moreover, the provided FSMs indicated that flooding is more likely to occur in the Lower Nicola River watershed than the Loup watershed. Following the RCP4.5 scenario, the area percentages of the flood susceptibility classes in the Loup watershed in 2050 and 2080 have changed by the following percentages from the year 2020 and 2050, respectively: Very Low = -1.68%, Low = -5.82%, Moderate = +6.19%, High = +0.71%, and Very High = +0.6% and Very Low = -1.61%, Low = +2.98%, Moderate = -3.49%, High = +1.29%, and Very High = +0.83%. Likewise, in the Lower Nicola River watershed, the changes between the years 2020 and 2050 and between the years 2050 and 2080 were: Very Low = -0.38%, Low = -0.81%, Moderate = -0.95%, High = +1.72%, and Very High = +0.42% and Very Low = -1.31%, Low = -1.35%, Moderate = -1.81%, High = +2.37%, and Very High = +2.1%, respectively. The impact of climate changes on future flood-prone places revealed that the regions designated as highly and very highly susceptible to flooding, grow in the forecasts for both watersheds. The main contribution of this study lies in the novel insights it provides concerning the flood susceptibility of watersheds in British Columbia and Quebec over time and under various climate change scenarios.
RESUMO
The ongoing outbreak of the COVID-19 as the current global concern threatens lives of many people around the world. COVID-19 is highly contagious so that it has infected more than 1,848,439 people until April 14, 2020 and killed more than 117,217 people. The main aim of this study is to develop an agent-based model (ABM) that simulates the spatio-temporal outbreak of COVID-19. The main innovation of this research is investigating the impacts of various strategies of school and educational center closures, heeding social distancing, and office closures on controlling the COVID-19 outbreak in Urmia city, Iran. In this research, the outbreak of COVID-19 disease was simulated with the help of ABM so that all agents considered in the ABM along with their attributes and behaviors as well as the environment of the ABM were described. Besides, the transmission of COVID-19 between human agents was simulated based on the SEIRD model, and finally, all control strategies applied in Urmia city along with corresponding actions of each control strategy were implemented in the ABM. The results of the ABM indicated that school and educational center closures in Urmia city, reduced the number of infected people by 4.96% each week on average and 49.61% in total from February 21 until May 10. Heeding social distancing by 30% and 70% of people of Urmia city from March 27, led to decrease the number of infected people by 5.24% and 10.07% each week, on average and 31.46% and 60.44% in total, respectively, and if 30% and 70% of civil servants of Urmia city did not go to work, the number of infected people would be decreased by 3.30% and 5.25% each week, on average and 32.98% and 52.48% in total from February 21 until May 10, respectively. As a result of this research, heeding social distancing by the majority of people is recommended for Urmia city in the current situation. The main advantages of disease modeling are to investigate how the disease is likely to evolve amongst the population of society and also assess the impacts of control strategies on controlling the outbreak of disease.
