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您的检索式：作者名="Mohammad Bannayan"
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| 1 | Estimation of meteorological drought indices based on AgMERRA precipitation data and station-observed precipitation data显示文摘Meteorological drought is a natural hazard that can occur under all climatic regimes. Monitoring the drought is a vital and important part of predicting and analyzing drought impacts. Because no single index can represent all facets of meteorological drought, we took a multi-index approach for drought monitoring in this study. We assessed the ability of eight precipitation-based drought indices(SPI(Standardized Precipitation Index), PNI(Percent of Normal Index), DI(Deciles index), EDI(Effective drought index), CZI(China-Z index), MCZI(Modified CZI), RAI(Rainfall Anomaly Index), and ZSI(Z-score Index)) calculated from the station-observed precipitation data and the Ag MERRA gridded precipitation data to assess historical drought events during the period 1987–2010 for the Kashafrood Basin of Iran. We also presented the Degree of Dryness Index(DDI) for comparing the intensities of different drought categories in each year of the study period(1987–2010). In general, the correlations among drought indices calculated from the Ag MERRA precipitation data were higher than those derived from the station-observed precipitation data. All indices indicated the most severe droughts for the study period occurred in 2001 and 2008. Regardless of data input source, SPI, PNI, and DI were highly inter-correlated(R^2=0.99). Furthermore, the higher correlations(R^2=0.99) were also found between CZI and MCZI, and between ZSI and RAI. All indices were able to track drought intensity, but EDI and RAI showed higher DDI values compared with the other indices. Based on the strong correlation among drought indices derived from the Ag MERRA precipitation data and from the station-observed precipitation data, we suggest that the Ag MERRA precipitation data can be accepted to fill the gaps existed in the station-observed precipitation data in future studies in Iran. In addition, if tested by station-observed precipitation data, the Ag MERRA precipitation data may be used for the data-lacking areas. | Nasrin SALEHNIA Amin ALIZADEH Hossein SANAEINEJAD Mohammad BANNAYAN Azar ZARRIN Gerrit HOOGENBOOM | 2017 | Journal of Arid Land2017，9，6： | 6 |
| 2 | Prediction of wheat moisture content at harvest time through ANN and SVR modeling techniques显示文摘The grain moisture content at harvest time is a key factor that limits harvesting windows.The present study aimed to develop a new methodology to predict wheat moisture content by using multi-layer perceptron(MLP)and support vector regression(SVR)techniques.Five input variables included the number of days after sowing,air temperature,air relative humidity,wind speed on an hourly basis,and precipitation on a 6-hour basis.The study area was Sari County located in the north of Iran.Data were collected from field experiments in two crop years(2016/17 and 2017/18).The results indicated that the developed MLP model outperformed the SVR model in determining wheat moisture content by R2 and RMSE value of 0.92 and 2.09%(wet basis)against 0.79 and 3.09%,respectively.In conclusion,the developed MLP model can be considered a useful method to estimate wheat moisture content at harvest time. | Shamsollah Abdollahpour Armaghan Kosari-Moghaddam Mohammad Bannayan | 2020 | Information Processing in Agriculture2020，7，4： | 3 |
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