On March 11 th 2020, World Health Organization (WHO) declared the 2019 novel corona virus as global pandemic. Corona virus, also known as COVID-19 was first originated in Wuhan, Hubei province in China around December 2019 and spread out all over the world within few weeks. Based on the public datasets provided by John Hopkins university and Canadian health authority, we have developed a forecasting model of COVID-19 outbreak in Canada using state-of-the-art Deep Learning (DL) models. In this novel research, we evaluated the key features to predict the trends and possible stopping time of the current COVID-19 outbreak in Canada and around the world. In this paper we presented the Long short-term memory (LSTM) networks, a deep learning approach to forecast the future COVID-19 cases. Based on the results of our Long short-term memory (LSTM) network, we predicted the possible ending point of this outbreak will be around June 2020. In addition to that, we compared transmission rates of Canada with Italy and USA. Here we also presented the 2, 4, 6, 8, 10, 12 and 14 th day predictions for 2 successive days. Our forecasts in this paper is based on the available data until March 31, 2020. To the best of our knowledge, this of the few studies to use LSTM networks to forecast the infectious diseases.

/pdf/time-series-forecasting-of-covid-19-transmission-in-canada-1jhrh59xyb.pdf

Time Series Forecasting of COVID-19 transmission in Canada Using LSTM Networks.

Novel Coronavirus pandemic, which negatively affected public health in social, psychological and economical terms, spread to the whole world in a short period of 6 months However, the rate of increase in cases was not equal for every country The measures implemented by the countries changed the daily spreading speed of the disease This was determined by changes in the number of daily cases In this study, the performance of the Random Forest (RF) machine learning algorithm was investigated in estimating the near future case numbers for 190 countries in the world and it is mapped in comparison with actual confirmed cases results The number of confirmed cases between 23/01/2020 - 17/06/2020 were divided into 3 main sub-datasets: training sub-data, testing sub-data (interpolation data) and estimating sub-data (extrapolation data) for the random forest model At the end of the study, it has been found that R2 values for testing sub-data of RF model estimates range between 0843 and 0995 (average R2= 0959), and RMSE values between 14176 and 52618 (mean RMSE = 25938); and that R2 values for estimating sub-data range between 0690 and 0968 (mean R2 = 0914), and RMSE values between 54973 and 250079 (mean RMSE = 90937) These results show that the random forest machine learning algorithm performs well in estimating the number of cases for the near future in case of an epidemic like Novel Coronavirus, which outbreaks suddenly and spreads rapidly

Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm.

Stream water quality prediction using boosted regression tree and random forest models

Taxonomy of influential factors for predicting pollutant first flush in urban stormwater runoff

Rapid evaluation of texture parameters of Tan mutton using hyperspectral imaging with optimization algorithms

Evaluation of random forest and regression tree methods for estimation of mass first flush ratio in urban catchments

Rice growing in paddy fields equipped with irrigation and drainage systems is less affected by unfavorable weather patterns such as prolonged dry periods and severe storms, but for the same reason, the fields discharge water to maintain suitable water depths even between rainfall events. Studies have been done to examine the quality of water drained from rice paddy fields during or after rainfall events. However, it is not clear how drainage affects the water quality of downstream waterbodies, especially during non-rainfall periods. We investigated the water quality characteristics of rainfall and non-rainfall rice paddy drainage, which was characterized by biological oxygen demand (BOD) and chemical oxygen demand (COD). In this study, hydrological variables including water inflow/outflow rates, depth, BOD, and COD were monitored at a paddy field in Korea from 2008 to 2012. This study then quantified how BOD and COD loadings are controlled by irrigation and drainage management practices. Water quality measurements showed that rainfall events that resulted in rainfall depths greater than 50 mm are responsible for half of the overall BOD and COD loads discharged by rainfall events of the fields. On the other hand, a quarter of the total BOD and COD was attributed to events that provided rainfall depths less than 10 mm. The results suggested that the BOD and COD loads of rainfall events can be reduced by up to 25% if water at paddy fields is carefully managed in small rainfall events. Water samples taken during rainfall events had greater nutrient loads only by 6 kg BOD ha−1 (17 kg BOD ha−1 vs. 11 kg BOD ha−1) and 7 kg COD ha−1 (41 kg COD ha−1 vs. 34 kg COD ha−1) compared to those of the non-rainfall periods in the cropping season. Such a finding indicates the significant impacts of paddy field drainage practices on downstream water quality between rainfall events, particularly when considering a long period of non-rainfall. The monitoring data also showed that the standard unit load of 15.5 kg BOD ha−1 established for rice paddy fields by the Ministry of Environment, the environmental authority of Korea, is substantially different from the actual BOD load, because the non-rainfall BOD loads were not considered in the standard. Thus, this study suggests that non-rainfall BOD loads should be counted and managed for successful implementations of total maximum daily load programs.

Characteristics of biochemical oxygen demand and chemical oxygen demand export from paddy fields during rainfall and non-rainfall periods

The unit load of paddy field was estimated by several methods and compared in this study. Four year field study was conducted at the paddy fields located in Yeongsan river and Sumjin river basins. The unit load was estimated by four methods; (1) averaging field monitored load; (2) NIER (National Institute of Environmental Research) method which use EMC(Event Mean Concentration) and runoff ratio of rainfall groups with rainfall weighting factors; (3)method using EMC determined by best fit probability distribution; (4)method using arithmetic mean EMC and runoff ratio. The result of this study showed that the unit loads of water quality constituents by field monitoring was similar to those of NIER method whereas those by probability distribution(log-normal, gamma) method were less than unit loads of NIER method. The unit loads by arithmetic mean EMC and runoff ratio were larger than those by NIER method. The unit load by NIER method showed higher BOD, COD, SS, lesser T-N, and similar T-P values when compared to unit loads determined by MOE in 1995.

https://www.koreascience.kr:443/article/JAKO201533678767624.pdf

Comparison of Unit Load from Paddy Field by Various Estimation Methods

LDC Construction Through Water Quality and Discharge Monitoring for Evaluation of Nonpoint Source Pollution Management

Monitoring provides data and information necessary for water quality assessment, but often it is prohibitive, especially when frequent sampling is required. In this study, we explored feasible sampling intervals for improved efficiency of nonpoint source (NPS) pollution assessment. We compared NPS pollutant loads calculated with concentration samples collected at 1, 2, 3, 4, and 6-hour intervals for the first 24 hours of 13 storm events and investigated the effect of different sampling intervals on load estimation for three watersheds that have different land uses. When compared to load estimates made from concentrations sampled at the reference (1-hour) interval, differences in load estimates were less than 10% in the cases of the 2-hour and 3-hour intervals in the urbanized and agricultural watersheds, respectively, except in the case of suspended solids (SS). When it comes to the total load estimation, up to 3-hour interval sampling provided load estimates with acceptable accuracy, except for SS. Thus, the 3-hour sampling interval was considered feasible for long-term pollutant load assessment, while the 2-hour sampling interval was suggested for SS. Such findings are expected to facilitate NPS pollution assessment by providing information required to improve monitoring efficiency.

/pdf/identifying-feasible-nonpoint-source-pollutant-sampling-17vewoh7jg.pdf

Identifying feasible nonpoint source pollutant sampling intervals for watersheds with paddy field and urban land uses

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