Performance Evaluation of Soft Computing Approaches for Forecasting COVID-19 Pandemic Cases.

An unexpected outbreak of deadly Covid-19 in later part of 2019 not only endangered the economies of the world but also posed threats to the cultural, social and psychological barriers of mankind. As soon as the virus emerged, scientists and researchers from all over the world started investigating the dynamics of this disease. Despite extensive investments in research, no cure has been officially found to date. This uncertain situation rises severe threats to the survival of mankind. An ultimate need of the time is to investigate the course of disease transfer and suggest a future projection of the disease transfer to be enabled to effectively tackle the always evolving situations ahead. In the present study daily new cases of COVID-19 was predicted using different forecasting techniques; Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing/Error Trend Seasonality (ETS), Artificial Neural Network Models (ANN), Gene Expression Programming (GEP), and Long Short-Term Memory (LSTM) in four countries; Pakistan, USA, India and Brazil. The dataset of new daily confirmed cases of COVID-19 from the date on which first case was registered in the respective country to 30 November 2020 is analyzed through these five forecasting models to forecast the new daily cases up to 31st January 2020. The forecasting efficiency of each model was evaluated using well known statistical parameters R 2, RMSE, and NSE. A comparative analysis of all above-mentioned models was performed. Finally, the study concluded that Long Short-Term Memory (LSTM) neural network-based forecasting model projected the future cases of COVID-19 pandemic best in all the selected four stations. The accuracy of the model ranges from coefficient of determination value of 0.85 in Brazil to 0.96 in Pakistan. NSE value for the model in India is 0. 99, 0.98 in USA and Pakistan and 0.97 in Brazil. This high-accuracy forecast of COVID-19 cases enables the projection of possible peaks in near future in the aforementioned countries and, therefore, prove to be helpful in formulating strategies to get prepared for the potential hard times ahead.

Hydraulic investigation of the impact of retrofitting floating treatment wetlands in retention ponds.

This paper describes the laboratory experimental investigations undertaken to analyse the influence of floating treatment wetlands (FTWs) on the hydraulic performance of a stormwater retention pond. Two experimental series were conducted, each focussed on investigating the influence of placing an FTW in a pond with firstly the inflow entering the retention pond from an inlet positioned 0.25 m offset from the longitudinal axis of the pond, and secondly the inlet positioned at the longitudinal axis of the pond. For both series of experiments, tests were undertaken at 1 l/s and 1.5 l/s, and with and without an artificial FTW installed. This study is the first to investigate the hydraulic impact of FTWs and their root systems on the performance of stormwater retention ponds. The results presented in this study suggest that FTWs are a viable method to minimise hydraulic inefficiencies, thereby increasing retention time and optimising hydraulic performance of stormwater retention ponds. The results highlight the importance of plant root characteristics. The optimal arrangement of root length is LR/DP = 0.5, where LR = root length and DP = pond depth. The results also indicate that the spatial variability of vegetation has a significant impact on the hydraulic performance of the pond.

Effect of baffles on the hydraulic performance of sediment retention ponds.

An investigation of the effect of baffles on retention pond performance using a physical model of an existing sediment retention pond is presented. Analysis of residence time (RTD curves) was used to compare the hydraulic performance of different arrangements of baffles in the pond. Five different arrangements for the design of baffles were studied. The results show that placing a single baffle to deflect the influent to a sediment retention pond does not improve pond performance; rather, it stimulates short-circuiting. This is contradictory to the literature and is considered to be a consequence of the model pond incorporating sloping walls, which is a novel aspect of this study. Most of the previous studies have neglected the effects of battered walls. Conversely, the inclusion of more than two baffles was found to increase the hydraulic performance. The results reported here are limited to small and narrow ponds where a large portion of the pond is batter (i.e. made up of sloping walls). For large area ponds, batter effects may be negligible and are likely to be different from those reported here.