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The proper design, development, and appropriate tuning of the Hybrid Neural Network architecture, mainly for its parsimoniousity and optimal training can help practitioners to generate a robust predictive tool for modeling several important hydrological processes within the water resources sector. In this paper, the Feedforward Artificial Neural Network (FFANN) and the hybrid WANN model have been developed, and later, coupled with the Gamma and M-tests (GT) approach for forecasting spatio-temporal groundwater fluctuations in a complex alluvial aquifer system. The performance of these hybrid models were evaluated using goodness-of-fit criteria. An analysis of the modeling results indicates that the GT coupled with the WANN model was able to provide significantly improved results, with lower values of the root mean square error (RMSE) and higher values of the NSE metric for the 1-week and 3-week lead times. Hence, utilizing this hybrid model, the groundwater level prediction tests were extended for 6-week and 12-week lead times with the GT approach, coupled with the WANN hybrid model only. The results showed that the accuracy of the GT-WANN hybrid model was better for the unconfined aquifer system compared to the leaky confined aquifer system. Furthermore, the present study also examined the interdependence between different model inputs and output variables for the selected study sites by means of the Wavelet Coherent Analysis (WCA). These results indicated that all the model's input variables have a significant effect on the groundwater level of unconfined aquifers, and confirmed the nature of the aquifers tapped within the present study sites. The study finally concludes that the GT-WANN approach can be a robust predictive tool for modeling spatio-temporal fluctuations of groundwater levels.

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Reliable method of rainfall-runoff modeling is a prerequisite for proper management and mitigation of extreme events such as floods. The objective of this paper is to contrasts the hydrological execution of Emotional Neural Network (ENN) and Artificial Neural Network (ANN) for modelling rainfall-runoff in the Sone Command, Bihar as this area experiences flood due to heavy rainfall. ENN is a modified version of ANN as it includes neural parameters which enhance the network learning process. Selection of inputs is a crucial task for rainfall-runoff model. This paper utilizes cross correlation analysis for the selection of potential predictors. Three sets of input data: Set 1, Set 2 and Set 3 have been prepared using weather and discharge data of 2 raingauge stations and 1 discharge station located in the command for the period 1986-2014.  Principal Component Analysis (PCA) has then been performed on the selected data sets for selection of data sets showing principal tendencies.  The data sets obtained after PCA have then been used in the model development of ENN and ANN models. Performance indices were performed for the developed model for three data sets. The results obtained from Set 2 showed that ENN with R= 0.933, R2 = 0.870, Nash Sutcliffe = 0.8689, RMSE = 276.1359 and Relative Peak Error = 0.00879 outperforms ANN in simulating the discharge. Therefore, ENN model is suggested as a better model for rainfall-runoff discharge in the Sone command, Bihar.

Drought assessment is crucial for effective water resources management in a river basin. Drought frequency has increased worldwide in recent years due to global warming. In this paper, an attempt is made to assess the meteorological drought in the Punpun river basin, India using two globally accepted drought indices namely, Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). The SPI and SPEI at 1-, 3-, 6-, 9-, and 12-month timescale were obtained to analyze the temporal variability of different drought levels. Correlation analysis of available observed data and gridded data has been carried out and the correlation coefficient was found to be 0.956. Hence gridded rainfall data from the year 1991 to 2020 is used for further analysis. Potential evapotranspiration (PET) used in the calculation of SPEI was computed by the Thornthwaite method. Water deficit was observed throughout as there is a decrease in rainfall and an increase in PET during the selected period. The results show that the period 2004 to 2006 and 2009 to 2010 years are observed as drought periods by both indices for almost all timescale. The intensity and duration of drought have increased after 2004. A negative trend of both the indices have been observed in all seasons on all timescale, which clearly shows a transition from near normal to moderately dry during the selected time period. The highest correlation between both the indices is for the 12-month scale with R² value 0.92 and the RMSE value 0.28. The main outcome of this study is that both SPI and SPEI show a strong correlation on same time scales adopted in this study. The dependency of SPEI on temperature is also observed in this study. Doi: 10.28991/cej-2021-03091783 Full Text: PDF

Interaction between the free surface and the bed material in flow over rock chutes under macroroughness conditions leads to a high air entrainment into the flow. The note reports on an experimental study about air diffusion features in the flow over a long rock chute. Air concentration profiles and water depths over a uniform bed material were measured. An empirical equation for the average air concentration in macroroughness condition for steep slopes is proposed. A new Darcy-Weisbach equivalent friction factor for long chutes as a function of the slope and the relative equivalent depth has also been found