Advanced and accurate forecasting of COVID‐19 cases plays a crucial role in planning and supplying resources effectively. Artificial Intelligence (AI) techniques have proved their capability in time series forecasting non‐linear problems. In the present study, the relationship between weather factor and COVID‐19 cases was assessed, and also developed a forecasting model using long short‐term memory (LSTM), a deep learning model. The study found that the specific humidity has a strong positive correlation, whereas there is a negative correlation with maximum temperature, and a positive correlation with minimum temperature was observed in various geographic locations of India. The weather data and COVID‐19 confirmed case data (1 April to 30 June 2020) were used to optimize univariate and multivariate LSTM time series forecast models. The optimized models were utilized to forecast the daily COVID‐19 cases for the period 1 July 2020 to 31 July 2020 with 1 to 14 days of lead time. The results showed that the univariate LSTM model was reasonably good for the short‐term (1 day lead) forecast of COVID‐19 cases (relative error <20%). Moreover, the multivariate LSTM model improved the medium‐range forecast skill (1–7 days lead) after including the weather factors. The study observed that the specific humidity played a crucial role in improving the forecast skill majorly in the West and northwest region of India. Similarly, the temperature played a significant role in model enhancement in the Southern and Eastern regions of India.
Advanced and accurate forecasting of COVID-19 cases plays a crucial role in planning and supplying resources effectively. Artificial Intelligence (AI) techniques have proved its capability in time series forecasting of the non-linear problems. In the present study, the relationship between weather factor and COVID-19 cases was assessed and also developed a forecasting model using long short term memory (LSTM), a deep learning model. The study found that the specific humidity has a strong
Accurate soil moisture data, critical for many applications such as agriculture and estimation of ground water, is limited worldwide, and particularly over India, by the absence of sustained multisite observations. A long-term sustained soil moisture observation at four vertical levels (5, 15, 50, and 100 cm) is now available at several locations over India under a multiinstitutional program Climate Observations and Modeling Network (COMoN) led by CSIR, India. At the same time, a high resolution (0.1 • × 0.1 • ) daily (moving 5-day mean) surface relative soil moisture data set has now become available from the Advanced Scatterometer (ASCAT). However, there is a need to compare remotely sensed data and in situ observations to ensure consistency and quantify uncertainties. This is particularly true for India characterized by diverse climatic zones. We present a comparative analysis of gridded ASCAT soil moisture data and in situ COMoN station data over six locations in India during the period 2010-2013. A multiscale analysis is carried out involving daily, weekly, monthly, and seasonal timescales at different geographical locations. Analyses show that the two data sets are generally consistent, although there are seasonalities in the agreement; the correlation coefficient is higher for the wet season (summer, autumn), and moderate for dry season (winter, spring). The correlation coefficients are ranged from 0.73 to 0.91 and above 99% significance level. The results quantify the reliability and robustness of ASCAT soil moisture over different climatic regions in India; the results also identify certain differences between the two data sets.Index Terms-Advanced Scatterometer (ASCAT), Climate Observations and Modeling Network (COMoN) data, climatology, data quality, multiscale analysis, soil moisture, soil moisture variability.
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