Predicting sediment transport rate (STR) in the presence of flexible vegetation is a critical task for modelers. Sediment transport modeling methods in the coastal region is equally challenging due to the nonlinearity of the STR–vegetation interaction. In the present study, the kernel extreme learning model (KELM) was integrated with the seagull optimization algorithm (SEOA), the crow optimization algorithm (COA), the firefly algorithm (FFA), and particle swarm optimization (PSO) to estimate the STR in the presence of vegetation cover. The rigidity index, D₅₀/wave height, Newton number, drag coefficient, and cover density were used as inputs to the models. The root mean square error (RMSE), the mean absolute error (MAE), and percentage of bias (PBIAS) were used to evaluate the capability of models. This study applied the novel ensemble model, and the inclusive multiple model (IMM), to assemble the outputs of the KELM models. In addition, the innovations of this study were the introduction of a new IMM model, and the use of new hybrid KELM models for predicting STR and investigating the effects of various parameters on the STR. At the testing level, the MAE of the IMM model was 22, 60, 68, 73, and 76% lower than those of the KELM-SEOA, KELM-COA, KELM-PSO, and KELM models, respectively. The IMM had a PBIAS of 5, whereas the KELM-SEOA, KELM-COA, KELM-PSOA, and KELM had PBIAS of 9, 12, 14, 18, and 21%, respectively. The results indicated that the increasing drag coefficient and D₅₀/wave height had decreased the STR. From the findings, it was revealed that the IMM and KELM-SEOA had higher predictive ability for STR. Since the sediment is one of the most important sources of environmental pollution, therefore, this study is useful for monitoring and controlling environmental pollution.

Evaporation is a crucial component to be established in agriculture management and water engineering. Evaporation prediction is thus an essential issue for modeling researchers. In this study, the multilayer perceptron (MLP) was used for predicting daily evaporation. MLP model is as one of the famous ANN models with multilayers for predicting different target variables. A new strategy was used to enhance the accuracy of the MLP model. Three multi-objective algorithms, namely, the multi-objective salp swarm algorithm (MOSSA), the multi-objective crow algorithm (MOCA), and the multi-objective particle swarm optimization (MOPSO), were respectively and separately coupled to the MLP model for determining the model parameters, the best input combination, and the best activation function. In this study, three stations in Malaysia, namely, the Muadzam Shah (MS), the Kuala Terengganu (KT), and the Kuantan (KU), were selected for the prediction of the respective daily evaporation. The spacing (SP) and maximum spread (MS) indices were used to evaluate the quality of generated Pareto front (PF) by the algorithms. The lower SP and higher MS showed better PF for the models. It was observed that the MOSSA had higher MS and lower SP than the other algorithms, at all stations. The root means square error (RMSE), mean absolute error (MAE), percent bias (PBIAS), and Nash Sutcliffe efficiency (NSE) quantifiers were used to compare the ability of the models with each other. The MLP-MOSSA had reduced RMSE compared to the MLP-MOCA, MLP-MOPSO, and MLP models by 18%, 25%, and 35%, respectively, at the MS station. The MAE of the MLP-MOSSA was 2.7%, 4.1%, and 26%, respectively lower than those of the MLP-MOCA, MLP-MOPSO, and MLP models at the KU station. The MLP-MOSSA showed lower MAE than the MLP-MOCA, MLP-MOPSO, and MLP models by 16%, 18%, and 19%, respectively, at the KT station. An uncertainty analysis was performed based on the input and parameter uncertainty. The results indicated that the MLP-MOSSA had the lowest uncertainty among the models. Also, the input uncertainty was lower than the parameter uncertainty. The general results indicated that the MLP-MOSSA had the high efficiency for predicting evaporation.

Concentrations of different heavy metals (As, Cd, Cr, Cu, Hg, Pb, Zn) were examined in the contour feathers of long-eared owl (Asio otus), little owl (Athene noctua), tawny owl (Strix aluco), barn owl (Tyto alba), Eurasian sparrowhawk (Accipiter nisus), rook (Corvus frugilegus), hooded crow (Corvus cornix), carrion crow (Corvus corone), common buzzard (Buteo buteo) and barn swallow (Hirundo rustica). The samples were collected from the Hortobágyi Madárpark (Bird Hospital Foundation) in Hungary. The bird species were classified into six groups based on their nourishment. Feathers were analysed by inductively coupled plasma optical emission spectroscopy (ICP-OES). The aim of our study was to determine the concentration of the above-mentioned heavy metals in the six different groups and to compare them by the groups, to find a possible connection between the concentrations and the age of birds and to get some information about the heavy metal burden of the environment. The highest As concentration was measured in little owl (0.65 ± 0.56 mg/kg). The highest Cd, Cr and Pb concentration was found in the feathers of barn swallow (0.13 ± 0.06 mg/kg; 1.69 ± 0.44 mg/kg; 5.36 ± 1.46 mg/kg), while the highest Cu and Hg concentration (65.45 ± 17.66 mg/kg; 2.72 ± 1.08 mg/kg) in sparrowhawk feathers and the highest Zn concentration in owls (157.21 ± 57.3 mg/kg). Statistically significant difference has been determined between the juvenile and adult crows in the case of Cd (p = 0.011). The higher concentration was measured in adults (0.14 ± 0.04 mg/kg) than that in juveniles (0.08 ± 0.02 mg/kg). Based on our results, the examined area is not contaminated by these heavy metals on that level, which can cause any adverse effect or poisoning in birds, so this region is safe to wildlife.