Predicting Modified Fournier Index by Using Artificial Neural Network in Central Europe.

The Modified Fournier Index (MFI) is one of the indices that can assess the erosivity of rainfall. However, the implementation of the artificial neural network (ANN) for the prediction of the MFI is still rare. In this research, climate data (monthly and yearly precipitation (pi, Ptotal) (mm), daily maximum precipitation (Pd-max) (mm), monthly mean temperature (Tavg) (°C), daily maximum mean temperature (Td-max) (°C), and daily minimum mean temperature (Td-min) (°C)) were collected from three stations in Hungary (Budapest, Debrecen, and Pécs) between 1901 and 2020. The MFI was calculated, and then, the performance of two ANNs (multilayer perceptron (MLP) and radial basis function (RBF)) in predicting the MFI was evaluated under four scenarios. The average MFI values were between 66.30 ± 15.40 (low erosivity) in Debrecen and 75.39 ± 15.39 (low erosivity) in Pecs. The prediction of the MFI by using MLP was good (NSEBudapest(SC3) = 0.71, NSEPécs(SC2) = 0.69). Additionally, the performance of RBF was accurate (NSEDebrecen(SC4) = 0.68, NSEPécs(SC3) = 0.73). However, the correlation coefficient between the observed MFI and the predicted one ranged between 0.83 (Budapest (SC2-MLP)) and 0.86 (Pécs (SC3-RBF)). Interestingly, the statistical analyses promoted SC2 (Pd-max + pi + Ptotal) and SC4 (Ptotal + Tavg + Td-max + Td-min) as the best scenarios for predicting MFI by using the ANN-MLP and ANN-RBF, respectively. However, the sensitivity analysis highlighted that Ptotal, pi, and Td-min had the highest relative importance in the prediction process. The output of this research promoted the ANN (MLP and RBF) as an effective tool for predicting rainfall erosivity in Central Europe.

Spatial distribution and mapping of COVID-19 pandemic in Afghanistan using GIS technique.

Geographic information science (GIS) has emerged as a unique tool that is extremely valuable in various research which involves spatial-temporal aspects. The geographical distribution of the epidemic is considered a significant characteristic that can be analyzed using GIS and spatial statistics. Proper knowledge can assist in controlling, mitigating, and mapping factors for detecting the transmission as well as the disease dynamics, and it provides geographical information of the outbreak and it can also give a glimpse of the disease trend and hotspots as well as provide ways to further evaluate the associated risk. This study analyzed the countries' total confirmed cases, total death cases, and the total recovered cases using an (IDW) geospatial technique which is an inherent tool used in ArcMap for spatial analysis. In order to identify the hotspots for COVID-19 cases, the Getis-Ord Gi* statistic method was applied with a confidence level of 95% in Herat and 90% for Kabul, Kapisa, and Logar provinces. The data considered in this research ranged from the period of 23rd July 2020 to 24th February 2021. All the COVID-19 confirmed, recovered, and death cases were correlated with provincial population density using the Pearson Correlation coefficient. Among the total cases 54,487, 32% cases were reported in the capital of the country (Kabul), and the mortality rate was 31% followed by Herat (18% deaths), Balkh (7% deaths), and Nangarhar (6% deaths). Most of the recoveries were observed in Kabul with (30%) followed by Herat (16%), Bamyan (10%), Balkh (5%), and Kandahar (5%). The results for Global Moran's I showed that the incidence rate of the total COVID-19 cases was in the random pattern, with the Moran Index of - 0.14. Given the z-score of - 1.62, the pattern does not appear to be significantly different than random. There was a strong correlation between the COVID-19 variables and population density [with r(33) = 0.827], [r(33) = 0.819] and [r(33) = 0.817] for the total cases, death cases, and recovered cases, respectively. Even though GIS has limited applicability in detecting the type and its spatial pattern of the epidemic, there is a high potential to use these tools in managing and controlling the pandemic. Moreover, GIS helps us better in comprehending the epidemic and assists us in addressing those fractions of the population and communities which are underserved during the disease outbreak.

Spatio-temporal characteristics of meteorological drought in Khyber Pakhtunkhwa, Pakistan.

This study analysed spatio-temporal fluctuations in rainfall to assess drought and wet spells in Khyber Pakhtunkhwa, Pakistan. Temporal changes in rainfall were assessed using a linear regression method, while aridity conditions at each meteorological station were measured using the United Nations Environment Programme climate aridity index. In this study, drought and wet spell patterns were identified using the Standardized Precipitation Evapotranspiration Index (SPEI). The Spearman's Rho (SR) test was applied to find trends in the temporal 1-month and 12-month SPEI data. Balakot, Dir, Kakul, Kalam, Malam Jabba, Parachinar, Patan and Saidu were humid whereas Cherat and Timergara were sub-humid meteorological stations while Bannu, Chitral, Drosh and Peshawar were semi-arid and D.I. Khan was found to be the only arid meteorological station in the study area. The regression results revealed that the amount of rainfall is decreasing at Balakot, Kakul and Dir, while in the southern part of the province the amount of rainfall is increasing, such as in Parachinar and Cherat. The SPEI results revealed distinct drought spells in 1971-1974, 1984-1989, 1998-2004 and recently in 2017-2018, in almost all met-stations results. The SR results indicated a significant wet trend at met-station Parachinar, located in the west, while a significant drying trend has been noted at Balakot in the north-eastern part of the study area. Detailed knowledge about rainfall variability can provide a foundation for the planning and use of water resources.