Porous graphitic biochar was synthesized by one-step treatment biomass using potassium ferrate (K₂FeO₄) as activator for both carbonization and graphitization processes. The modified biochar (Fe@BC) was applied for the removal of diclofenac sodium (DCF) in an aqueous solution. The as-prepared material possesses a well-developed micro/mesoporous and graphitic structure, which can strengthen its adsorption capacity towards DCF. The experimental results indicated that the maximum adsorption capacity (q_max) of Fe@BC for DCF obtained from Langmuir isotherm simulation was 123.45 mg·L⁻¹ and it was a remarkable value of DCF adsorption in comparison with that of other biomass-based adsorbents previously reported. Thermodynamic quality and effect of ionic strength studies demonstrated that the adsorption was a endothermic process, and higher environmental temperatures may be more favorable for the uptake of DCF onto Fe@BC surface; however, the presence of NaCl in the solution slightly obstructed DCF adsorption. Adsorption capacity was found to be decreased with the increase of solution pH. Additionally, the possible mechanism of the DCF adsorption process on Fe@BC may involve chemical adsorption with the presence of H-bonding and π–π interaction. With high adsorption capacity and reusability, Fe@BC was found to be a promising absorbent for DCF removal from water as well as for water purification applications.

The optimal management of hydropower resources is highly dependent on accurate and reliable hydrological runoff forecasting. The development of a suitable runoff-forecasting model is a challenging task due to the complex and nonlinear nature of runoff. To meet the challenge, this study proposed a three-stage novel hybrid model namely IVG (ICEEMDAN-VMD-GRU), by coupling gated recurrent unit (GRU) with a two-stage signal decomposition methodology, combining improved complete ensemble empirical decomposition with additive noise (ICEEMDAN) and variational mode decomposition (VMD), to forecast the monthly runoff of SWAT river, Pakistan. ICEEMDAN decomposed the runoff time series into subcomponents, and VMD performed further decomposition of the high-frequency component obtained by ICEEMDAN decomposition. Afterward, the GRU network was employed to the decomposed subcomponents for forecasting purposes. The performance of the IVG model was compared with other hybrid models including, ICEEMDAN-VMD-SVM (support vector machine), ICEEMDANGRU, VMD-GRU, ICEEMDAN-SVM, VMD-SVM; and standalone models including GRU and SVM by utilizing statistical indices. Experimental results proved that the IVG model outperformed other models in terms of accuracy and error reduction, which indicates the feasibility of the IVG model to analyze the nonlinear features of runoff time series and for runoff forecasting with applicability for future planning and management of water resources.

Hydrological runoff prediction in a reliable and precise manner contributes significantly to the optimal management of hydropower resources. Considering the importance of runoff prediction, this study proposed a hybrid model, namely VBH (VMD-BP), coupling variational mode decomposition (VMD) technique, and backpropagation (BP) based artificial neural network (ANN), to predict the monthly runoff of Fentang reservoir, China. Two hybrid models, including ensemble empirical mode decomposition-BP (EEMD-BP) and empirical mode decomposition-BP (EMD-BP), and a standalone BP model, were also developed for comparative analysis. The VBH model performed better compared to the EEMD-BP model in reducing mean absolute error (MAE) by 40.263%, root mean square error (RMSE) by 33.634%, and mean absolute percentage error (MAPE) by 52.906%. The improved results for the VBH model compared to the EMD-BP model included 103.716, 82.266, and 158.303% reductions in MAE, RMSE, and MAPE, respectively. The error reductions by the VBH model compared to the BP model were 113.848% for MAE, 122.022% for RMSE, and 143.026% for MAPE. The results highlighted that the proposed model was superior to the hybrid and standalone counterparts for the hydrological runoff prediction. Water resources designers and planners for future planning and management of hydrological assets can exploit the proposed model.