Long short-term memory neural network and improved particle swarm optimization–based modeling and scenario analysis for municipal solid waste generation in Shanghai,China

Accurate estimations of municipal solid waste (MSW) generation are vital to effective MSW management systems. While various single-point estimation approaches have been developed, the non-linearity and multiple site-specific influencing factors associated with MSW management systems make it challenging to forecast MSW generation quantities precisely. To address these concerns, this study developed a two-stage modeling and scenario analysis procedure for MSW generation and taking Shanghai as a test case demonstrated its viability. In the first stage, nine influencing factors were selected, and a hybrid novel forecasting model based on a long short-term memory neural network and an improved particle swarm optimization (IPSO-LSTM) was proposed for the forecasting of the MSW generation quantities, after which actual Shanghai data from 1980 to 2019 were used to test the performance. In the second stage, the future influencing variable values in different scenarios were predicted using an improved grey model, after which the predicted Shanghai MSW generation quantities from 2025 to 2035 were evaluated under various scenarios. It was found that (1) the proposed IPSO-LSTM had higher accuracy than the benchmark models; (2) the MSW generation quantities are expected to respectively increase to 9.971, 9.684, and 9.090 million tons by 2025 and 11.402, 11.285, and 10.240 by 2035 under the low, benchmark, and high scenarios; and (3) the MSW generation differences between the high and medium scenarios were decreasing.

     

城市降雨径流监测自动采样技术研发与应用

海绵城市建设效果评估需要对海绵设施和不同尺度区域的径流过程进行水质监测采样。现有的采样方法和设备难以自动采集到径流产生初始阶段的水样。研发了一套包含无电力驱动天然降雨自动采样器、"零捕获"下垫面径流过程水质监测自动采样终端、智能型自动水质监测采样器等设备的城市降雨径流水质监测自动采样技术,解决了全天候、自动采集天然降雨和径流过程最初水样的难题,并可记录采样过程的具体时间。实际应用表明,该套技术可操作性强,性能可靠,能为相关科学研究和监测评估提供参考。