Spatial and temporal monitoring of drought conditions using the satellite rainfall estimates and remote sensing optical and thermal measurements

Drought is an important natural disaster that causes devastating impacts on the ecosystem, livestock, environment, and society. So far, various remote-sensing methods have been developed to estimate drought conditions, each of which has advantages and restrictions. This study aims to monitor the real-time drought indices at the field scales via the integration of various earth observations. Our proposed method consists of two steps. In the first step, the relationships between long-term standardized precipitation indices (SPI) derived from PERSIANN-CDR rainfall data and two drought-dependent parameters derived from MODIS products, including normalized NDVI and soil-air temperature gradient, are obtained at the spatial resolution of PERSIANN-CDR grid (approximately 25 km). As the next step, the corresponding relationships are applied to estimate the drought index maps at the spatial resolution of MODIS products (1 km). Numerous analyses are carried out to evaluate the proposed method. The results revealed that, from various drought indices, including SPIs of different timescales (1, 3, 6, and 12-months), SPI-3 and SPI-6 are more appropriate to the proposed method in terms of correlation with temperature and vegetation parameters. The findings also demonstrate the competency of the proposed method in estimating SPI indices with average RMSE 0.67 and the average correlation coefficient of 0.74.     

Variation of foF2 with solar activity

Ionospheric hourly monthly-median values of the F2-layer critical frequency, foF2, from six European stations are correlated with the corresponding 12-month running mean values of each of the six solar indices, the Zurich sunspot number R, the solar radio noise flux at 10.7 cm F, the ionospheric index of solar activity IF2, the index IG, the Australian T index and the Russian RS ionospheric index, using various models. The statistical analysis shows that there is no difference in the degree of correlation in using one index over another. Their statistical behaviour is virtually identical. Furthermore, it is shown that there is a slight degree of favourability for a quadratic relation between foF2 and any index of solar activity.     

A study on possible solar and geomagnetic effects on the precipitation over northwestern Argentina

The precipitation over Tucuman (26.8°S; 65.2°W), which is representative of the Northwestern region of Argentina, is analyzed in search of an association with solar and geomagnetic activity, with the purpose of contributing to the controversial issue on the connection between climate variation and anthropogenic vs. natural forcing. Monthly time series of precipitation, sunspot number (Rz), and aa index were used for the period 1884–2010. A wavelet analysis was performed first which, due to the time series length, shows significant results only for periodicities lower than 32 years. Due to the transient character and non-constant phase of the results, any sustained wavelet coherence between precipitation and either sunspots or aa could be noticed. Moving averages and correlations were also assessed. The 11 and 22-year running mean of precipitation is positively correlated to Rz and aa when the whole period of analysis is considered. However, a shift in the long-term behavior of precipitation is noticed around 1940, which implies different correlation values with Rz and aa when the period before or after this year are considered. The solar cycle length is also considered for this statistical study and partly confirms the results obtained with Rz and aa. We propose plausible physical explanations based on geomagnetic activity and total solar irradiance effects over atmospheric circulation that could support the statistical result. A deeper analysis and broader geographical coverage is needed in order to detect a connection between precipitation and solar variability discernible from greenhouse gases effects. We emphasize the idea of the importance of recognizing and quantifying the different forcing acting on precipitation (or any other climate parameter), which sometimes can be barely evident from a solely statistical analysis.     

Exploration and analysis of the factors influencing GNSS PWV for nowcasting applications

Precipitable water vapor (PWV) can be assimilated into a numerical weather model (NWM) to improve the prediction accuracy of numerical weather prediction. In this study, taking GNSS data for the Beijing Fangshan station (BJFS) as an example, based on the method of Pearson correlation coefficient combined with quantitative analysis, GNSS datasets are used to study the relationships between GNSS-derived PWV (GNSS PWV__Met) and its influencing factors, including the internal influencing factors zenith troposphere delay (ZTD), zenith hydrostatic delay (ZHD), zenith wet delay (ZWD), and surface temperature (T_s), and the external influencing factor haze (mainly PM_2.5). Firstly, based on the strong correlation between PWV__Met and ZTD hourly sequences from the International GNSS Service Network’s BJFS station for DOYS 182–212, 2015, the results of experiment prove that the reliability of GNSS ZTD is used to forecast PWV_Met in short-term forecasting. Secondly, based on hourly data of BJFS in 2016, the correlation between PWV__Met and ZTD, ZWD, ZHD, pressure (P) and T_s is analyzed, and then, with the rate of ZTD variation as the main factor, ZTD variation as auxiliary factor, the prediction success rate is 88.24% from hourly data of precipitation event for DOYs 183–213 in Beijing. The experiment indicates that ZTD can help forecast short-term precipitation. Thirdly, based on data from three hazy periods with relatively stable weather conditions, no heavy rainfall, and relatively continuous data in the past three years, the correlation between GNSS PWV_Met/ZTD and PM_2.5 hourly series is analyzed. The results of the experiments suggests that GNSS ZTD should be considered to assist in haze monitoring. So in the absence of radiosonde stations and meteorological elements, ZTDs on retrieval of GNSS stations have more application value in short-term forecast.     

Temporal extrapolation of TEC using WNN during 2007–2018 and comparison against GIM,IRI2016 and Kriging

In this paper, a new method of temporal extrapolation of the ionosphere total electron content (TEC) is proposed. Using 3-layer wavelet neural networks (WNNs) and particle swarm optimization (PSO) training algorithm, TEC time series are modeled. The TEC temporal variations for next times are extrapolated with the help of training model. To evaluate the proposed model, observations of Tehran GNSS station (35.69°N, 51.33°E) from 2007 to 2018 are used. The efficiency of the proposed model has been evaluated in both low and high solar activity periods. All observations of the 2015 and 2018 have been removed from the training step to test the proposed model. On the other hand, observations of these 2 years are not used in network training. According to the F10.7, the 2015 has high solar activity and the 2018 has quiet conditions. The results of the proposed model are compared with the global ionosphere maps (GIMs) as a traditional ionosphere model, international reference ionosphere 2016 (IRI2016), Kriging and artificial neural network (ANN) models. The root mean square error (RMSE), bias, dVTEC = |VTEC_GPS ? VTEC_Model| and correlation coefficient are used to assess the accuracy of the proposed method. Also, for more accurate evaluation, a single-frequency precise point positioning (PPP) approach is used. According to the results of 2015, the maximum values of the RMSE for the WNN, ANN, Kriging, GIM and IRI2016 models are 5.49, 6.02, 6.34, 6.19 and 13.60 TECU, respectively. Also, the maximum values of the RMSE at 2018 for the WNN, ANN, Kriging, GIM and IRI2016 models are 2.47, 2.49, 2.50, 4.36 and 6.01 TECU, respectively. Comparing the results of the bias and correlation coefficient shows the higher accuracy of the proposed model in quiet and severe solar activity periods. The PPP analysis with the WNN model also shows an improvement of 1 to 12 mm in coordinate components. The results of the analyzes of this paper show that the WNN is a reliable, accurate and fast model for predicting the behavior of the ionosphere in different solar conditions.     

A comparison of ionosonde measured foF2 and IRI-2016 predictions over China

A comparison of the ionospheric F-region critical frequency (foF2) between ionosonde measurements and IRI-2016 predictions is studied over China during the period from January 2008 to October 2016. Four stations are selected, and the latitude coverage starts at 49.4°N and ends at 23.2°N with a sequential latitude interval of about 10°, the corresponding geomagnetic latitudes are from 39.5°N to 13.2°N. The results show that the variability of the observed foF2 versus latitudes, seasons, local time and levels of solar activity could be well reproduced by IRI-2016. However, the daily lowest value of foF2 from the IRI-2016 prediction occurs earlier than that from the ionosonde. Around the sunrise, the IRI-2016 prediction shows a very sharp rise and grows much faster than the observed foF2 in every month. The foF2 difference between the two options (URSI and CCIR) in IRI-2016 increases as the F10.7 index decreases. During 2008–2009, the annual average deviations of URSI and CCIR range from ?5% to ?10% and from 5% to ?5%, respectively. Generally, the CCIR performs better than URSI during postsunset under low solar activity or in Equatorial Ionization Anomaly (EIA) region over China, while it shows no large difference in performance with URSI in other locations or for other time.     

Evaluating the predictability of eight Atmospheric-Oceanic signals affecting Iran’s Droughts,employing intelligence based and stochastic methods

The large-scale atmospheric-oceanic phenomena are among the main effective factors in the droughts in the Middle East, especially in Iran. Since these effects are usually delayed, their relevant signals can be useful for predicting droughts. As a result, the provision of a precise prediction of these signals can be efficient in increasing the drought prediction prospect. The current study predicts 8 cases of the most effective oceanic signals on the droughts which have been investigated in Iran. To do so, the problem-solving method with the time series prediction approach is based on the two model types intelligence-based (including multilayer perceptron [MLP] and support vector machine [SVM]) and stochastic (including Autoregressive Integrated Moving Average [ARIMA]) has been used. The model's input for each index included the time lags of the same index itself, which was determined by the autocorrelation function. Based on the evaluation criteria, the results were indicative of the weak predictability of the North Atlantic Oscillation (NAO) and Arctic Oscillation (AO), while the Extreme Eastern Tropical Pacific sea surface temperature (Niño [1 + 2]), East Central Tropical Pacific sea surface temperature (Niño [3 + 4]), and Oceanic Niño Index (ONI) were predicted with very good accuracy, and there is a high overlap between their predictions and observations (95.9 % < R² < 99.3 %). In the extreme events also, the rate of normalized forecasting error for Niño (1 + 2), Niño (3 + 4), and ONI were in the medium (20–30 %), good (10–20 %), and excellent (0–10 %) ranges, respectively. The comparison between the models also indicates a partial superiority of the ARIMA stochastic model over the SVM and MLP models. The overall results of the study are indicative of the applicability of the predictions of the three mentioned indices as the inputs to increase precipitation and drought forecasting prospects in Iran (as well as all regions affected by them); which have the research value for further studies in terms of drought forecasting.     

SCIAMACHY solar irradiance observation in the spectral range from 240 to 2380 nm

The SCanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY) is part of the payload of ESA’s Environmental Satellite ENVISAT which was launched into a sun-synchronous polar orbit on 2002-03-01. It is the first spaceborne instrument covering a wavelength range from 240 to 2380 nm thus including ultraviolet, visible and near infrared spectral regions.The main purpose of SCIAMACHY is to determine the amount and distribution of a large number of atmospheric trace constituents by measuring the radiance backscattered from the Earth. In addition, several solar observations are performed with daily or orbital frequency.The presented results will cover the following topics: (a) comparison of the solar irradiance measured by SCIAMACHY with data from the instruments SOLSPEC/SOLSTICE/SUSIM and a solar spectrum derived by Kurucz; (b) comparison of the SCIAMACHY solar Mg II index with GOME and NOAA data; (c) correlation of the relative change of solar irradiance measured by SCIAMACHY with the sun spot index.The mean solar irradiance for each of the 8 SCIAMACHY channels agrees with the Kurucz data within ±2–3%. The presented analysis proves that SCIAMACHY is a valuable tool to monitor solar irradiance variations.     

Evaluation of three high-resolution satellite precipitation estimates: Potential for monsoon monitoring over Pakistan

Multi-sensor precipitation datasets including two products from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) and estimates from Climate Prediction Center Morphing Technique (CMORPH) product were quantitatively evaluated to study the monsoon variability over Pakistan. Several statistical and graphical techniques are applied to illustrate the nonconformity of the three satellite products from the gauge observations. During the monsoon season (JAS), the three satellite precipitation products captures the intense precipitation well, all showing high correlation for high rain rates (>30 mm/day). The spatial and temporal satellite rainfall error variability shows a significant geo-topography dependent distribution, as all the three products overestimate over mountain ranges in the north and coastal region in the south parts of Indus basin. The TMPA-RT product tends to overestimate light rain rates (approximately 100%) and the bias is low for high rain rates (about ±20%). In general, daily comparisons from 2005 to 2010 show the best agreement between the TMPA-V7 research product and gauge observations with correlation coefficient values ranging from moderate (0.4) to high (0.8) over the spatial domain of Pakistan. The seasonal variation of rainfall frequency has large biases (100–140%) over high latitudes (36N) with complex terrain for daily, monsoon, and pre-monsoon comparisons. Relatively low uncertainties and errors (Bias ±25% and MAE 1–10 mm) were associated with the TMPA-RT product during the monsoon-dominated region (32–35N), thus demonstrating their potential use for developing an operational hydrological application of the satellite-based near real-time products in Pakistan for flood monitoring.     

Sea state parameters retrieval from cross-polarization Gaofen-3 SAR data

Chinese Gaofen-3 (GF-3) synthetic aperture radar (SAR) acquired in wave mode (WV) and quad-polarization stripmap (QPS) mode default operates in quad-polarization (vertical–vertical (VV), vertical-horizontal (VH), horizontal-horizontal (HH) and horizontal-vertical (HV)) modes. To date, more than GF-3 SAR vignettes following about 110 orbits acquired in WV and QPS mode have been recorded during the mission from April 2016 to December 2017. In the vignettes, ocean surface waves signatures, that are wave-look patterns, are visible in cross-polarization (basically VH). These vignettes are collocated with surface sea state parameters simulated from numerical WAVEWATCH-III (WW3) wave model using a 0.1° grid. There are 11,269 matchups available for studying the relation between sea state parameters and SAR-derived parameters in VH-polarization. A well-known empirical CWAVE model, herein renamed as CPCWAVE_GF3, is adopted for sea state parameter retrieval from GF-3 SAR vignettes with the SAR parameters in the cross-polarization channel. The method yielded a significant correlation coefficient (COR) of 0.79 for wave height (SWH) and 0.72 for second-order cross-zero mean wave period (MWP). Validation against 76 moored buoys resulted in a 0.49 m RMSE of SWH with a 0.21 m scatter index (SI) and validation against 71 moored buoys resulted in a 1.01 s RMSE of MWP with a 0.13 s SI. The comparison of SWH with 116 footprints from the altimeter of Jason-2 also shows a 0.46 m RMSE of SWH with a 0.19 m SI. Our work demonstrates the feasibility of wave retrieval from GF-3 SAR using cross-polarization channels parameters.     

基于时空图卷积网络的无人机网络入侵检测方法

无人机网络相比地面网络具有节点快速移动、拓扑结构变换频繁和通信链路不可靠的特点,传统的针对地面网络的入侵检测方法难以适用。针对无人机网络的时空动态特性进行建模,提出了一种无人机网络的入侵检测方法——基于注意力机制的时空图卷积网络（ATGCN）。将图卷积网络和门控递归单元组合为时空图卷积网络,从复杂多变的数据中提取网络的时空演变特征,通过注意力机制提取和入侵检测最相关的特征,输入支持向量机进行分类预测。多个数据集的实验分析表明:所提方法能够适应无人机网络的动态性和不稳定性,相比传统检测方法准确率高且误报率低,具有良好的鲁棒性和适应性。      

Evaluation of the improvement of IRI-2016 over IRI-2012 at the India low-latitude region during the ascending phase of cycle 24

This paper investigated the performance of the latest International Reference Ionosphere model (IRI-2016) over that of IRI-2012 in predicting total electron content (TEC) at three different stations in the Indian region. The data used were Global Positioning System (GPS) data collected during the ascending phase of solar cycle 24 over three low-latitude stations in India namely; Bangalore (13.02°N Geographic latitude, 77.57°E Geographic longitude), Hyderabad (17.25°N Geographic latitude, 78.30°E Geographic longitude) and Surat (21.16°N Geographic latitude, 72.78°E Geographic longitude). Monthly, the seasonal and annual variability of GPS-TEC have been compared with those derived from International Reference Ionosphere IRI-2016 and IRI-2012 with two different options of topside electron density: NeQuick and IRI01-corr. It is observed that both versions of IRI (i.e., IRI-2012 and IRI-2016) predict the GPS-TEC with some deviations, the latest version of IRI (IRI-2016) predicted the TEC similar to those predicted by IRI-2012 for all the seasons at all stations except for morning hours (0500 LT to 1000?LT). This shows that the effect of the updated version is seen only during morning hours and also that there is no change in TEC values by IRI-2016 from those predicted by IRI-2012 for the rest of the time of the day in the Indian low latitude region. The semiannual variations in the daytime maximum values of TEC are clearly observed from both GPS and model-derived TEC values with two peaks around March-April and September-October months of each year. Further, the Correlation of TEC derived by IRI-2016 and IRI-2012 with EUV and F10.7 shows similar results. This shows that the solar input to the IRI-2016 is similar to IRI 2012. There is no significant difference observed in TEC, bottom-side thickness (B0) and shape (B1) parameter predictions by both the versions of the IRI model. However, a clear improvement is visible in hmF2 and NmF2 predictions by IRI-2016 to that by IRI-2012. The SHU-2015 option of the IRI-2016 gives a better prediction of NmF2 for all the months at low latitude station Ahmedabad compare to AMTB 2013.     

The relationship between ionospheric total electron content (TEC) over East Asia and the tropospheric circulation around the Qinghai-Tibet Plateau obtained with a partial correlation method

This paper reports a study on the relationship between ionospheric total electron content (TEC) over East Asia and the tropospheric circulation around the Qinghai-Tibet Plateau. Ionospheric TEC over East Asia are obtained from 25 observatories during 1996–2004. By applying a partial correlation method which can eliminate the influences of solar and geomagnetic activities, we find no significant correlation between TEC and the Asian zonal circulation index (Iz), but find a positive correlation between the day-to-day variability of TEC and Iz. We suggest that the positive correlation is closely related with the topography of the Qinghai-Tibet Plateau. The dynamical effect on airflow of the plateau can generate vortexes, and the vortexes may continuously excite internal gravity waves (IGWs) which transmit up to the ionosphere and cause regional wave disturbances. This study gives evidence for the dynamical mechanism of ionosphere–troposphere coupling and shows the importance of the Qinghai-Tibet Plateau in the ionosphere–troposphere coupling over East Asia.     

Study the behavior of the ionospheric frequencies at Karachi during three consecutive solar minima of cycle 21, 22 & 23 and their comparison with IRI-2016

The behavior of critical frequencies of ionospheric E and F2 layers (foE & foF2) along with minimum ionospheric frequency (fmin) is studied for solar minima of cycle 21 (1986), 22 (1996) and 23 (2008) over Karachi (24.95$°$N, 67.13$°$E), Pakistan. The station is located at the crest of equatorial ionization anomaly region. Beside seasonal differences, pronounced change in the values of frequencies is noted from one solar minimum to another solar minimum. A strong and direct correlation of foF2 with Smoothed Sunspot Number (SSN) and F10.7?cm solar flux is observed. In the minimum of cycle 23, reduction in foF2 is noted due to reduction of solar EUV as compared to other minima. Also disappearance of semi-annual variations in foF2 is noted in cycle 23 minimum. Unexpectedly higher values of foE and fmin are observed in minimum of cycle 23 as compared to other minima. It is difficult to explain this unusual behavior of fmin and foE along with disappearance of semi-annual variation in foF2. It is possible that during very low solar activity, thermospheric conditions are changed which in turn altered the ionosphere. Further investigation of atmosphere-ionosphere coupling is required to understand this complex behavior. On comparison of observed values with IRI-2016, higher deviations are observed in foE before noon hours while in case of foF2, large deviations are noted during daytime. The absence of foF2 semi-annual variation in cycle 23 is not reproduced by IRI-2016. It is suggested that IRI-2016 need some modification for extremely low solar activity condition.     

基于相关性分析的结构可靠性加严试验方法

针对传统结构可靠性试验的验证多是基于载荷应力和结构强度相互独立的假设问题,从应力和强度数据的相关性分析与度量出发,在二者均为正态随机变量的前提下,建立了一种基于Copula函数相关应力-强度干涉模型的结构可靠性加严试验验证方案设计方法。该方法结合Copula函数和应力-强度干涉模型实现相关条件下原可靠性指标与加严条件下可靠性指标的转化,适用于小样本情况下基于传统成败型试验方法评估其可靠性。研究结果表明:相比独立假设,应力和强度呈负相关时,会增加试验样本量且样本量随负相关程度减弱而减少;呈正相关时,会减少试验样本量且样本量随正相关程度增强而减少。     

Preliminary search for cosmic radiation and solar-terrestrial parameters correlated with the reversal of the solar magnetic field

A statistical study has been made of cosmic ray intensity, as observed by a neutron monitor, and of selected solar and geophysical parameters in a search for phenomena which may be associated with the reversal of the solar magnetic field. The results reported here utilized the Zurich sunspot number and the geomagnetic aa index. There is an intriguing, but not conclusive, result that shows a vast difference in the correlation of the neutron monitor intensity and the aa index between successive periods bounded by solar maxima. Between the 19th solar cycle maximum (March 1958) and the 20th solar cycle maximum (November 1968), and the 20th solar cycle maximum (November 1968) and the 21st solar cycle maximum (assumed to be December 1979 for this study) the correlations are ?0.86 and +0.28 respectively.     

Selection of classification techniques for land use/land cover change investigation

The concerns over land use/land cover (LULC) change have emerged on the global stage due to the realisation that changes occurring on the land surface also influence climate, ecosystem and its services. As a result, the importance of accurate mapping of LULC and its changes over time is on the increase. Landsat satellite is a major data source for regional to global LULC analysis. The main objective of this study focuses on the comparison of three classification tools for Landsat images, which are maximum likelihood classification (MLC), support vector machine and artificial neural network (ANN), in order to select the best method among them. The classifiers algorithms are well optimized for the gamma, penalty, degree of polynomial in case of SVM, while for ANN minimum output activation threshold and RMSE are taken into account. The overall analysis shows that the ANN is superior to the kernel based SVM (linear, radial based, sigmoid and polynomial) and MLC. The best tool (ANN) is then applied on detecting the LULC change over part of Walnut Creek, Iowa. The change analysis of the multi temporal images indicates an increase in urban areas and a major shift in the agricultural practices.     

一种基于白谱法的电离层天气扰动指数

基于一种电离层扰动提取方法——白谱法,利用IGS提供的电离层TEC网格数据,获得电离层J_s指数、J_r指数和J_p指数,分别反映单站、纬度圈（沿经度积分）及行星际尺度下的电离层天气扰动状态.在2015年3月的一次磁暴过程中,J_s指数、J_r指数及J_p指数均很好地反映出电离层响应地磁暴的过程,磁暴前后J_p指数与Dst指数相关系数达到-0.72;J_s图从二维角度很好地表征了电离层天气的扰动过程.在此基础上,统计分析了2011——2014年J_p指数与Dst指数的相关性,结果表明:限定J_p≥2,J_p指数与对应时间Dst指数的相关系数为-0.67;限定J_p≥3,二者相关系数更高,达到-0.87.通过分析不同J_p指数阈值下不同等级磁暴的次数,发现J_p指数可以很好地反映磁暴下的电离层整体扰动,为指示电离层天气状态提供了可能的参数.      

Spatio-temporal dynamics in the phenology of croplands across the Indo-Gangetic Plains

Spatio-temporal dynamics in land surface phenology parameters observed over croplands can inform on crop-climate interactions and, elucidate local to regional scale vulnerabilities either due to climate change or prevailing sub-optimal agricultural practices. Here, we observe spatio-temporal trends in land surface phenology parameters (cropping intensity, length of growing season and productivity) for kharif and rabi cropping seasons from satellite data across the Indo-Gangetic Plains from 1982 to 2006. The productivity of the Indo-Gangetic Plains croplands is of regional importance and is a vital component of Indian national food security efforts. Aside from local and intra-state heterogeneity in observed trends there was a clear west-to-east gradient in cropping intensity. Key observed trends include increasing cropping intensity in the eastern IGP, increasing number of growing days per year in Bihar, Uttar Pradesh and Haryana and increasing productivity in both cropping seasons across the IGP. This information is a crucial input to integrated assessments of the croplands to ensure management of the agricultural system shifts towards a trajectory of climate-resilience and environmental sustainability.     

Flood susceptibility mapping by ensemble evidential belief function and binomial logistic regression model on river basin of eastern India

Flooding is the overflow of water from stream, river, lake and sea that occurs all over the world and has disastrous effects on human society and environment. Frequent severe flood event in eastern India cause of death and damages every year so, the development of flood susceptibility method is needed for identifying the flood vulnerability areas to reduce the damages. Techniques of Remote Sensing (RS) and Geographical Information System (GIS) can help to flood susceptibility modeling by the accrued and analyzing huge amount of data in short time. The main objectives of this study are to determine the effectiveness of Evidence Belief Function (EBF), binomial Logistic Regression (LR) and ensemble of EBF and LR (EBF-LR) model with RS and GIS techniques for flood susceptibility mapping and spatial prediction of flood-susceptible areas in the Koiya river basin of West Bengal, India. Eight flood conditioning factors; Land use and land cover (LULC), soil, rainfall, normalized differences vegetation index (NDVI), distance to river, elevation, topographic wetness index (TWI) and stream power index (SPI) have been used, and total 264 historical flooding points were mapped, and randomly divided in to training (70%) and validating (30%) dataset. Flood susceptibility map has been generated by applying EBF, LR and ensemble EBF-LR method with the help of training and eight causative factors dataset. The maps have been divided in to six classes; extremely low, very low, low, moderate, high and very high. The receiver operating characteristic (ROC) curve has been used to accuracy assessment of the susceptibility map, and the area under curve (AUC) disclosures 87.9%, 85.2% and 84.1% prediction rate for the EBF-LR, EBF and LR model, respectively. This study is helpful to flood management program, dissection makers and planning in local administrative level.