<|begin_of_text|><|start_header_id|>system<|end_header_id|>You are an expert on climate change, human mobility and migration. Answer the question truthfully.<|eot_id|><|start_header_id|>user<|end_header_id|>Here are the title and abstract of a scientific paper probably related to climate change, human mobility and migration..
Title:"Modeling Dynamics of Community Resilience to Extreme Events with Explainable Deep Learning."
Abstract:"Community resilience provides a paradigm guiding communities' preparedness and mitigation efforts to counter the increasing risks of extreme events (EEs). Knowledge regarding how communities perform during and after EEs can inform proactive resilience enhancement practices. However, the EE impacts on impacted communities are influenced by multiple variables and their implicit interactions, which are difficult to be understood and modeled with conventional mathematical or statistical models. Thus, we calibrate a spatio-temporal deep learning model to capture the dynamics of impacted communities and use explainable artificial intelligence (XAI) approach to interpret the influence of communities' social and physical properties on EE impacts. Specifically, we couple graph convolutional neural network (GCN) and long short-term memory (LSTM) to model the mobility dynamics for 666 census tracts (i.e., communities) in 14 medium-sized US cities affected by recent hurricanes. The model takes both static variables characterizing communities' preexisting conditions and dynamic variables depicting the changing hazard exposure. The interpretation of the model predictions based on DeepLIFT shows that community resilience, inferred from the perturbation of human mobility in this research, is highly event and geography dependent. Variables including walkability, green spaces, and civil participation generally contribute to fewer mobility perturbations, i.e., resilience contributive, while automobile-oriented accessibility and social vulnerability lead to more mobility perturbations when hazard conditions are controlled. This study empirically validates the mediative role of communities' social and physical properties on EE impacts and promotes more data-driven approaches for understanding and anticipating complex, dynamic, and place-specific community resilience.."
I have six questions you need to answer.
Q1: Is this article is about 'human migration mobility': 1 = yes; 0 = no. Answer with a number. Do not give a textual explanation.

Q2: What drivers of migration and mobility does the article study? 0 = 'other', 1 = 'climate', 2 = 'environment', 3 = 'economic', 4 = 'conflict', 5 = 'political', 6 = 'social', 7 = 'family'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q3: What is the overall sentiment of the article?:0 = 'neutral', 1 = 'negative', 2 = 'positive'. Answer with a number. Do not give a textual explanation.

Q4: What type of climate or environmental keywords are explicitly mentioned?:  0 = 'none', 1 = 'algal blooms', 2 = 'infestation', 3 = 'infectious diseases', 4 = 'ocean acidification', 5 = 'sea ice', 6 = 'acid rain', 7 = 'drought', 8 = 'cold wave', 9 = 'heat wave', 10 = 'glacial', 11 = 'wildfire', 12 = 'air pollution', 13 = 'biodiversity loss', 14 = 'coastal erosion', 15 = 'land degradation', 16 = 'deforestation', 17 = 'desertification', 18 = 'water pollution', 19 = 'diebacks', 20 = 'permafrost loss', 21 = 'sand mining', 22 = 'sea level rise', 23 = 'earthquake', 24 = 'tsunami', 25 = 'volcanic', 26 = 'flood', 27 = 'avalanche', 28 = 'landslide', 29 = 'downburst', 30 = 'thunderstorm', 31 = 'sandstorm', 32 = 'haze', 33 = 'storm', 34 = 'cyclone', 35 = 'blizzard', 36 = 'hail', 37 = 'snow', 38 = 'tornado', 39 = 'wind', 40 = 'hurricane', 41 = 'typhoon', 42 = 'other'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q5: What discipline does the article belong to?: 0 = 'other', 1 = 'social science', 2 = 'natural science', 3 = 'arts and humanities', 4 = 'multidiscipline and interdiscipline'. Answer with a number. Do not give a textual explanation.

Q6: Does the article apply quantitative and/or statistical analysis?: 0 = 'no', 1 = 'yes'. Answer with a number. Do not give a textual explanation.

Use the following template: 

Q1 = [];
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Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:
Q1 = [1];
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Q3 = [2];
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Q6 = [1].
Modeling Dynamics of Community Resilience to Extreme Events with Explainable Deep Learning
Community resilience provides a paradigm guiding communities' preparedness and mitigation efforts to counter the increasing risks of extreme events (EEs). Knowledge regarding how communities perform during and after EEs can inform proactive resilience enhancement practices. However, the EE impacts on impacted communities are influenced by multiple variables and their implicit interactions, which are difficult to be understood and modeled with conventional mathematical or statistical models. Thus, we calibrate a spatio-temporal deep learning model to capture the dynamics of impacted communities and use explainable artificial intelligence (XAI) approach to interpret the influence of communities' social and physical properties on EE impacts. Specifically, we couple graph convolutional neural network (GCN) and long short-term memory (LSTM) to model the mobility dynamics for 666 census tracts (i.e., communities) in 14 medium-sized US cities affected by recent hurricanes. The model takes both static variables characterizing communities' preexisting conditions and dynamic variables depicting the changing hazard exposure. The interpretation of the model predictions based on DeepLIFT shows that community resilience, inferred from the perturbation of human mobility in this research, is highly event and geography dependent. Variables including walkability, green spaces, and civil participation generally contribute to fewer mobility perturbations, i.e., resilience contributive, while automobile-oriented accessibility and social vulnerability lead to more mobility perturbations when hazard conditions are controlled. This study empirically validates the mediative role of communities' social and physical properties on EE impacts and promotes more data-driven approaches for understanding and anticipating complex, dynamic, and place-specific community resilience.