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[1] Flood estimation for ungauged catchments is a challenging task for hydrologists. A modern geographical information system is able to extract a large number of catchment characteristics as input variables for regionalization analysis. Effective and efficient selection of the best input variables is urgently needed in this field. This paper explores a new methodology for selecting the best input variable combination on the basis of the gamma test and leave-one-out cross validation (LOOCV) to estimate the median annual maximum flow (as an index flood). Since the gamma test is capable of efficiently calculating the output variance on the basis of the input without the need to select a model structure type, more effective regionalization models could be developed because there is no need to define an a priori model structure. A case study from 20 catchments in southwest England has been used to illustrate and validate the proposed scheme. It has been found that the gamma test is able to narrow down the search options to be further explored by the LOOCV. The best formula from this approach outperforms the conventional approaches based on cross validation, data filtering with Spearman's rank correlation matrix, and corrected Akaike information criterion. In addition, the developed formula is significantly more accurate than the existing equation used in the Flood Estimation Handbook.

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Multi-walled carbon nanotubes (CNTs) functionalized with a deep eutectic solvent (DES) were utilized to remove mercury ions from water. An artificial neural network (ANN) technique was used for modelling the functionalized CNTs adsorption capacity. The amount of adsorbent dosage, contact time, mercury ions concentration and pH were varied, and the effect of parameters on the functionalized CNT adsorption capacity is observed. The (NARX) network, (FFNN) network and layer recurrent (LR) neural network were used. The model performance was compared using different indicators, including the root mean square error (RMSE), relative root mean square error (RRMSE), mean absolute percentage error (MAPE), mean square error (MSE), correlation coefficient (R2) and relative error (RE). Three kinetic models were applied to the experimental and predicted data; the pseudo second-order model was the best at describing the data. The maximum RE, R2 and MSE were 9.79%, 0.9701 and 1.15 × 10−3, respectively, for the NARX model; 15.02%, 0.9304 and 2.2 × 10−3 for the LR model; and 16.4%, 0.9313 and 2.27 × 10−3 for the FFNN model. The NARX model accurately predicted the adsorption capacity with better performance than the FFNN and LR models.

The simulation elevation-surface area-storage interrelationship of a reservoir is a crucial task in developing ideal water release policies for reservoir and dam operations. In this study, an inclusive (stochastic dynamic programming-artificial neural network (SDP-ANN)) model was established and applied to obtain an ideal reservoir operation strategy for Sg. Langat reservoir in Malaysia. The problems associated with the management of water resources mostly relate to uncertainty and the stochastic nature of the reservoir inflow, and the SDP-ANN model is meant to consider uncertainty in the input parameters such as reservoir inflow and reservoir evaporation losses. The performance of the SDP-ANN model was compared to that of the stochastic dynamic programming-autoregression (AR) model. The primary aim of the model is to decrease the squared deviation from the desired water release, which we determined by comparing the SDP-AR and SDP-ANN model performances. The results indicate that the SDP-ANN model demonstrated greater resilience and reliability with a lower supply deficit. Consequently, the case study results confirm that the SDP-ANN model performs better than the SDP-AR model in obtaining the best parameters for the reservoir operation. Specifically, a comparison of the models shows that the proposed Model 2 increased the reliability and resilience of the system by 7.5% and 6.3%, respectively.

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This work presents the experimental and modeling process for mercury ions removal from water using functionalized multi‐walled carbon nanotube as adsorbent. The modeling procedure has been carried out using nonlinear autoregressive network with an exogenous input (NARX) neural network modeling technique is used for modeling the adsorbent's adsorption capacity using different parameters based on experimental data. The effect of different parameters including mercury ions concentration, pH, amount of adsorbent dosage, and contact time is studied. Three kinetics models such as intraparticle diffusion, pseudo first‐order, and pseudo second order are applied using the experimental and predicted outputs, the pseudo second order was the best to describe. A sensitivity study is conducted using different parameters. Various indicators are applied to examine the accuracy and efficiency of the NARX model such are mean square error (MSE), root mean square error, relative root mean square error, mean absolute percentage error, relative error (RE), and coefficient of determination (R2). The value of the maximum RE was 3.49%, the R2 was 0.9998, and the MSE was 4.28 × 10−6. Based on the used indicators, the NARX model was capable to predict the adsorbent's adsorption capacity by comparing the NARX model outputs to the experimental results.