Abstract: Earthquakes and tremors are a common occurrence throughout the world, mostly in China, Japan and Indonesia. In Kenya, we experience a lot of tremors and landslides during the rainy seasons that have extensive negative social, economic, and environmental impacts. These damages include loss of human life, financial loss and destruction of infrastructure. This becomes a lagging factor towards achieving the Vision 2030 and Sustainable Development Goals (SDGs). This study used secondary data, obtained from World Wide Standardized Seismograph Station (WWSSSN) in Kilimambogo. Stochastic artificial neural network was adopted to identify prone areas to the said natural disasters, measure the socioeconomic impacts and build a predictive model for landslides, tremor and earthquakes in Kenya. It was evident that landslides are destructive in nature through observable measurable impacts on people. They increase the social and economic burden on the affected people. 64.76% of the measurable impacts affect human beings directly while the rest affect cattle and crops. Along the Great rift valley, most earthquakes and landslides took place. This is attributed to the active seismic activities. Kenya experiences earthquakes of magnitude m < 4. Our model achieved root mean square of 0.435. Furthermore, we got R2=0.80 for testing dataset. This implied that 80% of data was trainable by the model. Therefore, the predictive neural network model is efficient and accurate in forecasting, and more importantly is a good fit model.Abstract: Earthquakes and tremors are a common occurrence throughout the world, mostly in China, Japan and Indonesia. In Kenya, we experience a lot of tremors and landslides during the rainy seasons that have extensive negative social, economic, and environmental impacts. These damages include loss of human life, financial loss and destruction of infrastruct...Show More
Abstract: This survey aims to explore the evaluation of influencing factors of Beijing residents' satisfaction with the effect of waste classification since the implementation of the regulations. The questionnaire was distributed to all residents in Beijing by means of probability and non-probability sampling. Sixteen districts and counties in Beijing were divided into five major districts according to their functions as first-level sampling units. Finally, a total of 1307 questionnaires were collected to explore the influencing factors of residents' satisfaction with waste classification with the help of random forest model. On this basis, ten influencing factors affecting residents' satisfaction with waste classification were found. At the same time, combined with the suggestions given by residents on waste classification in the questionnaire, the effective information is intuitively observed through the text analysis results to see what factors improve residents' satisfaction with waste classification. Through the above research, we find that the residents' cognitive level of waste classification and the evaluation of the implementation of waste classification policies are the main reasons for the low residents' satisfaction with waste classification. In addition, communities have timely formulas for waste classification policies, clear rewards and punishments, and solve the problems of weak publicity and lax supervision to improve residents' satisfaction in waste classification.Abstract: This survey aims to explore the evaluation of influencing factors of Beijing residents' satisfaction with the effect of waste classification since the implementation of the regulations. The questionnaire was distributed to all residents in Beijing by means of probability and non-probability sampling. Sixteen districts and counties in Beijing were d...Show More
