Streamflow modelling is a very important process in the management and planning of water resources. However, com-plex processes associated with the hydro-meteorological variables, such as non-stationarity, non-linearity, and randomness, make the streamflow prediction chaotic. The study developed multi linear regression (MLR) and back propagation neural network (BPNN) models to predict the streamflow of Wadi Hounet sub-basin in north-western Algeria using monthly hy-drometric data recorded between July 1983 and May 2016. The climatological inputs data are rainfall (P) and reference evapotranspiration (ETo) on a monthly scale. The outcomes for both BPNN and MLR models were evaluated using three statistical measurements: Nash–Sutcliffe efficiency coefficient (NSE), the coefficient of correlation (R) and root mean square error (RMSE). Predictive results revealed that the BPNN model exhibited good performance and accuracy in the prediction of streamflow over the MLR model during both training and validation phases. The outcomes demonstrated that BPNN-4 is the best performing model with the values of 0.885, 0.941 and 0.05 for NSE, R and RMSE, respectively. The highest NSE and R values and the lowest RMSE for both training and validation are an indication of the best network. Therefore, the BPNN model provides better prediction of the Hounet streamflow due to its capability to deal with complex nonlinearity procedures.

Drought is known as a normal part of climate and including in a slow-onset natural hazard which may have several im-pacts on hydrology, agriculture, and socioeconomic. Drought monitoring, including its severity, spatial and duration is re-quired and becomes an essential input for establishing drought risk management and mitigation plan. Many drought indices have been introduced and applied in regions with different climate characteristics in the last decades. This paper aims to compare standardized precipitation index (SPI) and rainfall anomaly index (RAI) along with standardized streamflow index (SSI) in Pekalen River Basin, East Java, Indonesia. The statistical association analyses, included the Pearson correlation (r), Kendal tau (τ), and Spearman rho (rs) were performed to examine the degree of consistency between monthly and annual drought index of SPI and RAI. Additionally, the comparative analysis was performed by overlapping both monthly and an-nual drought index from the SPI and RAI with the SSI at hydrological years. The study revealed that the characteristic of the annual drought index between the SPI and RAI exhibits pattern similarity which indicated by the high correlation coeffi-cient between them. Further, the comparative analysis on each hydrological year showed that the SPI and RAI were very well correlated and exhibited a similar pattern with the SSI. Overall, the SPI shows better performance than the RAI for es-timating drought characteristic either monthly or annual basis. Hence, the SPI is considered as a reliable and effective tool for analyzing drought characteristic in the study area.