Urban flooding presents acute challenges in heritage cities, where dense populations and valuable cultural assets coexist. While Nature-Based Solutions (NbSs) have been widely studied, their implementation in heritage cities remains underexplored due to spatial constraints and cultural sensitivities. This study develops a quantitative evaluative framework integrating the Spatial Multi-Criteria Evaluation (SMCE) and NbSs to address urban flooding in Quanzhou, a UNESCO World Heritage site. In GIS-based spatial analysis, the framework prioritizes high-risk zones by synthesizing hydrological and socio-economic factors. The analysis reveals that the Surface Runoff Coefficient (SRC) contributes 30% to urban flooding exposure, with high building congestion and elevated PM2.5 levels exacerbating risks by 17% and 16.8%, respectively. Vulnerability mapping underscores the critical role of cultural heritage, accounting for 71.1% of the vulnerability index, and highlights priority townships such as Linjiang, Kaiyuan, and Lizhong, with integrated exposure and vulnerability rates of 11.8%, 10.3%, and 9.5%, respectively. This study proposes four NbS models tailored to heritage urban landscapes, with Solution I—direct surface infiltration—identified as the most applicable, covering 170.9 ha, followed by Solution II—subterranean stormwater infiltration—at 52.3 ha. Despite limited spatial feasibility (1.3–33.5% of township areas), the framework demonstrates significant potential for integrating NbSs with existing grey infrastructure, contributing to flood risk mitigation and broader sustainability goals. The findings provide actionable insights for urban planners and policymakers, offering a replicable methodology for the deployment of NbSs in heritage-rich urban contexts worldwide. By bridging flood risk management with cultural preservation, this work advances the discourse on resilient and sustainable urban planning.

Urban heat island (UHI) effects pose significant challenges to sustainable urban development, necessitating innovative modeling techniques to optimize urban morphology for thermal resilience. This study integrates the Pix2Pix and CycleGAN architectures to generate high-fidelity urban morphology models aligned with local climate zones (LCZs), enhancing their applicability to urban climate studies. This research focuses on eight major Chinese coastal cities, leveraging a robust dataset of 4712 samples to train the generative models. Quantitative evaluations demonstrated that the integration of CycleGAN with Pix2Pix substantially improved structural fidelity and realism in urban morphology synthesis, achieving a peak Structural Similarity Index Measure (SSIM) of 0.918 and a coefficient of determination (R2) of 0.987. The total adversarial loss in Pix2Pix training stabilized at 0.19 after 811 iterations, ensuring high convergence in urban structure generation. Additionally, CycleGAN-enhanced outputs exhibited a 35% reduction in relative error compared to Pix2Pix-generated images, significantly improving edge preservation and urban feature accuracy. By incorporating LCZ data, the proposed framework successfully bridges urban morphology modeling with climate-responsive urban planning, enabling adaptive design strategies for mitigating UHI effects. This study integrates Pix2Pix and CycleGAN architectures to enhance the realism and structural fidelity of urban morphology generation, while incorporating the LCZ classification framework to produce urban forms that align with specific climatological conditions. Compared to the model trained by Pix2Pix coupled with LCZ alone, the approach offers urban planners a more precise tool for designing climate-responsive cities, optimizing urban layouts to mitigate heat island effects, improve energy efficiency, and enhance resilience.

Water quality assessment is critical for ensuring the health of aquatic ecosystems and managing water resources effectively. However, accurately predicting key water quality variables remains challenging due to the complex interactions between environmental factors and anthropogenic influences. In the present investigation, a new modelling framework is proposed for better prediction of three water quality variables, namely: (i) dissolved oxygen concentration (DO), (ii) water turbidity (TU), and (iii) water Chlorophyll a (Chl-a). Six machine learning models, i.e., adaptive boosting (AdaBoost), categorical boosting (CatBoost), histogram gradient boosting (HistGBRT), light gradient boosting machine (LightGBM), natural gradient boosting (NGBoost), and extreme gradient boosting (XGBoost), both applied and compared based on the combination of a large number of water quality variables. All models were developed using data collected from three stations: (i) USGS 05543010 Illinois River at Seneca, Illinois County, (ii) USGS 05586300 Illinois River at Florence, Illinois County, and (iii) USGS 05553700 Illinois River at Starved Rock, Illinois County, USA. The SHapley additive explanations (SHAP) was adopted in the present study for model interpretability and feature ranking. Furthermore, all models were compared using various numerical indices and graphical representations. From the obtained results we can draw the following conclusion. DO concentration can be predicted very well with high numerical performances, and the CatBoost model was found to be the best one exhibiting excellent numerical index: RMSE (0.430), MAE (0.326), R (0.980) and NSE (0.961), respectively. For Chl-a, all models were found to be less accurate and the best performances were obtained using the LightGBM with RMSE (5.916), MAE (4.294), R (0.892) and NSE (0.795), respectively. Finally, for water TU, none of the models were found to be accurate and very poor performances were obtained. Finally, the use of the SHAP has significantly helped in better understanding the overall contribution of the various water variables in the finale prediction. • AdaBoost, CatBoost, LightGBM, HistGBRT, NGBoost, and XGBoost for water quality prediction. • Prediction of dissolved oxygen, water turbidity, and water Chlorophyll-a • Global and local model’s interpretability using SHapley additive explanations (SHAP) • CatBoost was the most accurate for DO prediction and LightGBM for Chl-a prediction • All models were failed to accurately predict water TU.

This study enhances the prediction of biochemical oxygen demand (BOD5), a vital water quality parameter, by developing hybrid artificial neural network models integrated with advanced optimization algorithms. Data from two monitoring stations in South Korea were used to create five models, including the innovative ANN-Enhanced Runge Kutta (ANN-ERUN) model. ANN-ERUN achieved the highest accuracy, significantly outperforming other models. At Gong station, it reduced prediction error (root mean square error: 1.24mg/L; mean absolute error: 0.83mg/L) and achieved a determination coefficient of 0.857. Models using eight water quality parameters, including dissolved oxygen and chemical oxygen demand, exhibited superior performance. These findings confirm the effectiveness of ANN-ERUN in precise BOD5 prediction, offering a robust tool for environmental monitoring and sustainable water quality management.

Precise and robust streamflow estimation is crucial for effective water resource management, particularly in mitigating extreme climatic events such as droughts and floods. This study introduces an innovative integration of the Random Vector Functional Link (RVFL) network with an Enhanced Remora Optimization Algorithm (EROA), specifically designed for monthly streamflow prediction. The RVFL-EROA is compared against standalone RVFL and RVFL models optimized using the Gorilla Troops Optimizer (GTO), Whale Optimization Algorithm (WOA), and the original Remora Optimization Algorithm (ROA). Performance is evaluated using statistical indices, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R2), and Nash-Sutcliffe Efficiency (NSE). The methodology is tested on streamflow time series data from the Kunhar River Basin in Pakistan, with input variables derived from antecedent streamflow, air temperature, and rainfall. Results indicate that temperature and streamflow-based inputs yielded higher accuracy compared to rainfall inputs. The RVFL-EROA outperformed other models, achieving improvements in mean RMSE, MAE, R2, and NSE by 8.63-1.77%, 12.08-1.58%, 16.88-3.33%, and 19.03-3.05%, respectively. Moreover, the RVFLEROA demonstrated superior performance in estimating peak streamflow values, which is critical for flood management. These findings highlight the potential of temperature-based inputs and RVFL-EROA models for streamflow prediction in data-scarce regions, particularly in developing countries. The proposed approach offers a reliable solution for enhancing hydrological forecasting and supports efficient water resource planning.

Construction and development projects worldwide heavily rely on concrete as their primary building material, making it an essential component of global infrastructure and growth. The various key ingredients that compose concrete contribute differently to its overall environmental footprint. Moreover, rapid urban and industrial growth has strained ecological systems and depleted resources, necessitating environmentally friendly substitutes for traditional concrete ingredients. Waste foundry sand has emerged as a potential replacement for natural sand in concrete mixtures, offering a sustainable option. Since evaluating waste foundry sand effects on concrete through laboratory methods is resource-intensive, therefore, this research employs six distinct ensemble boosting algorithms including CatBoost, XGBoost, HistGBRT, NGBoost, LightGBM, Adaboost and MLR to predict key properties of concrete made with waste foundry sand: split tensile strength, compressive strength, and elastic modulus. This investigation utilized SHapley additive exPlanations (SHAP) method to precisely illustrate feature interdependencies, quantify their complex relationships, and establish a hierarchy of importance. The models' performance was then assessed using multiple robust metrics, including mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (R), Nash-Sutcliffe efficiency coefficient (NSE), Kling-Gupta efficiency (KGE), and Willmott's Index (WI). The findings reveal that the XGBoost model excels in estimating the compressive strength, achieving an RMSE of 2.845 MPa and an R-value of 0.958. CatBoost follows as the second-best performer, with an RMSE of 3.083 MPa and an R-value of 0.952. For the elastic modulus estimation, XGBoost again outperforms other models, yielding an RMSE of 0.992 GPa and an R-value of 0.990, while NGBoost secures the second position with an RMSE of 1.256 GPa and an R-value of 0.985. In predicting the split tensile strength, XGBoost once more demonstrates superior accuracy, recording an RMSE of 0.296 MPa and an R-value of 0.925, closely follows by LightGBM with an RMSE of 0.302 MPa and an R-value of 0.926. This research demonstrates the effectiveness of XGBoost algorithms in predicting key properties of concrete with waste foundry sand when relevant factors are considered.

In this study, a multi-stage planning framework was constructed by using SWMM simulation modeling and NSGA-II and applied to optimize the layout of integrated grey–green infrastructure (IGGI) under land use change and climate change scenarios. The land use change scenarios were determined based on the master plan of the study area, with imperviousness of 50.7% and 62.0% for stage 1 and stage 2, respectively. Rainfall trends for stage 1 and stage 2 were determined using Earth-E3 from the CMIP6 model. The rainfall in stage 2 increased by 14.9% from stage 1. Based on these two change scenarios, the spatial configuration of IGGI layouts with different degrees of centralization of the layout (DCL) under the two phases was optimized, with the lowest life cycle cost (LCC) as the optimization objective. The results showed that the layout with DCL = 0 had better performance in terms of LCC. The LCC of the layout with DCL = 0 was only 66.9% of that of the layout with DCL = 90.9%. In terms of Tech-R, stage 2 had better performance than stage 1. Furthermore, the average technological resilience (Tech-R) index of stage 2 was 0.8–3.4% higher than that of stage 1. Based on the LCC and Tech-R indices of all of the layouts, TOPSIS was used to compare the performance of the layouts under the two stages, and it was determined that the layout with DCL = 0 had the best economic and performance benefits. The results of this study will be useful in exploring the spatial configuration of urban drainage systems under land use change and climate change for sustainable stormwater management.

This study investigates the feasibility of relevance vector machine tuned with dwarf mongoose optimization algorithm in modeling monthly streamflow. The proposed method is compared with relevance vector machines tuned by particle swarm optimization, whale optimization, marine predators algorithms, and single relevance vector machine methods. Various lagged values of hydroclimatic data (e.g., precipitation, temperature, and streamflow) are used as inputs to the models. The relevance vector machine tuned with dwarf mongoose optimization algorithm improved the efficiency of single method in monthly streamflow prediction. It is found that the integrating metaheuristic algorithms into single relevance vector machine improves the prediction efficiency, and among the input combinations, the lagged streamflow data are found to be the most effective variable on current streamflow whereas precipitation has the least effect.

Accurately predicting soil temperature (Ts) serves as the foundation of geothermal applications, modern irrigation strategies in arid agricultural landscapes, and understanding ecosystem changes. Also, this parameter is crucial for estimating crop water requirements, thereby enabling efficient management of scarce water resources in these moisture-limited environments. Therefore, the development of a sophisticated intelligent algorithm based on boosting ensemble including XGBoost, CatBoost, LightGBM, and AdaBoost models which incorporates nine different scenarios from meteorological variables as input parameters, offers valuable insights into subsurface thermal dynamics at various depths for accurately predicting thermal gradients within the soil profile, is imperative for soil science investigations. To unravel the underlying mechanisms influencing the models' predictions of Ts, the SHapley Additive exPlanations (SHAP) methodology was employed. This algorithm quantifies the contributory significance of each input variable and facilitates the explication of input-output dependencies. In this study, the model's performance was evaluated at two meteorological monitoring sites, denoted as Penjwen and Bazian, situated within the geopolitical boundaries of the Kurdistan region in Iraq using a suite of statistical indicators, including the correlation coefficient (R), the root mean square error (RMSE), the Nash-Sutcliffe efficiency (NSE), and the mean absolute error (MAE). These metrics provided a comprehensive assessment of the model's predictive accuracy and reliability. Moreover, this localized analysis provided insights into the model's efficacy under specific regional climatic conditions. At station Penjwen, the results based on the RMSE values indicated that the LightGBM and CatBoost methods performed better than other models in Ts estimation at depths of 5 cm and 10 cm, with RMSE values of 2.502 °C and 2.164 °C, respectively. Also, at station Bazian, CatBoost and LightGBM approaches showed the best results at depths of 5 cm and 10 cm, with RMSE values of 2.069 °C and 1.786 °C, respectively. The study's findings suggest that meteorological variables can serve as effective inputs for predicting Ts using the proposed algorithms.

The indiscriminate evolution of urban configurations aggravates flood vulnerabilities, threatening sustainable urban expansion. Present methodologies fall short in supplying urban planners with flood mitigative strategies centered on urban configuration facets. Leveraging the power of the XGBoost algorithm, this study posits an advanced optimization schema, adroitly balancing the dual objectives of mitigating urban flooding and enhancing economic growth, with minimal disruption to established urban layouts. Shenzhen serves as the investigative ground, where the model displays exceptional accuracy, resilience, and interpretability in predicting Pluvial Flooding Susceptibility (PFS) and Economic Contribution (EC). Model interpretation divulges the profound influence of three-dimensional urban configuration elements, primarily the Building Congestion Degree, on PFS and EC. Pareto solution exploration for multi-objective optimization unveils the ideal urban configuration interval. To minimize PFS while maximizing EC, the research suggests pertinent measures: augmenting vegetation density, regulating the impervious coverage ratio within 50–70%, limiting two- and three-dimensional building density thresholds, and moderately escalating urban drainage network density. Additionally, it encourages a comprehensive appreciation of function-oriented land usage and intrinsic site topographical characteristics to reconcile varied urban development goals during planning. By fusing data-derived insights with multi-objective optimization, this research anticipates influencing urban planning models, thus enhancing decision-making related to urban configuration and fostering flood-resilient, sustainable, and economically prosperous urban habitats.

Predicting streamflow is essential for managing water resources, especially in basins and watersheds where snowmelt plays a major role in river discharge. This study evaluates the advanced deep learning models for accurate monthly and peak streamflow forecasting in the Gilgit River Basin. The models utilized were LSTM, BiLSTM, GRU, CNN, and their hybrid combinations (CNN-LSTM, CNN-BiLSTM, CNN-GRU, and CNN-BiGRU). Our research measured the model’s accuracy through root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and the coefficient of determination (R2). The findings indicated that the hybrid models, especially CNN-BiGRU and CNN-BiLSTM, achieved much better performance than traditional models like LSTM and GRU. For instance, CNN-BiGRU achieved the lowest RMSE (71.6 in training and 95.7 in testing) and the highest R2 (0.962 in training and 0.929 in testing). A novel aspect of this research was the integration of MODIS-derived snow-covered area (SCA) data, which enhanced model accuracy substantially. When SCA data were included, the CNN-BiLSTM model’s RMSE improved from 83.6 to 71.6 during training and from 108.6 to 95.7 during testing. In peak streamflow prediction, CNN-BiGRU outperformed other models with the lowest absolute error (108.4), followed by CNN-BiLSTM (144.1). This study’s results reinforce the notion that combining CNN’s spatial feature extraction capabilities with the temporal dependencies captured by LSTM or GRU significantly enhances model accuracy. The demonstrated improvements in prediction accuracy, especially for extreme events, highlight the potential for these models to support more informed decision-making in flood risk management and water allocation.

This study investigates the efficacy of hybrid artificial neural network (ANN) methods, incorporating metaheuristic algorithms such as particle swarm optimization (PSO), genetic algorithm (GA), gray wolf optimizer (GWO), Aquila optimizer (AO), Runge–Kutta (RUN), and the novel ANN-based Runge–Kutta with Aquila optimizer (LSTM-RUNAO). The key novelty of this research lies in the developing and applying the LSTM-RUNAO model, which combines Runge–Kutta and Aquila optimizer to enhance streamflow prediction accuracy. The models’ performance is compared against the conventional ANN method, analyzing monthly streamflow prediction across three data split scenarios (50–50

This research offers a fast and accurate method for measuring the biogas production rate throughout biogas production. An agricultural biogas plant's measurement of eight process variables served as the source of experimental data used to create the models. Biomass type, reactor/feeding, volatile solids, pH, organic load rate, hydraulic retention time, temperature, and reactor volume were utilized in this context. Artificial neural networks (ANN) were developed to evaluate the biogas production rate. The variable selection was carried out using the cuckoo optimization algorithm (COA), multi-verse optimization algorithm (MVO), leagues championship algorithm (LCA), evaporation-rate water cycle algorithm (ERWCA), stochastic fractal search (SFS), and teaching-learning-based optimization (TLBO). In this study, the model's size decreased, the important process variables were highlighted, and the ANN models' potential was enhanced for prediction. The proposed COA, MVO, LCA, ERWCA, SFS, and TLBO and ensembles are the outcome of using the abovementioned approaches to synthesize the multi-layer perceptron (MLP). To evaluate the effectiveness of the used models, we have developed a scoring system in addition to employing mean absolute error, mean square error, and coefficient of determination as accuracy criteria. Implementing the COA, MVO, LCA, ERWCA, SFS, and TLBO algorithms enhances the accuracy of the MLP. It is found that some of the used hybrid techniques could provide better prediction outputs than traditional MLP rankings. Additional investigation indicated that the ERWCA is better than the three other algorithms. The biogas production rate was estimated with the greatest precision with R2 = 0.9314 and 0.9302, RMSE of 0.1969 and 0.24925, and MAE of 0.1307 and 0.19591.

Nature-based solutions (NbSs) are considered to form an innovative stormwater management approach that has living resolutions grounded in natural processes and structures. NbSs offer many other environmental benefits over traditional grey infrastructure, including reduced air pollution and climate change mitigation. This review predominantly centers on the hydrological aspect of NbSs and furnishes a condensed summary of the collective understanding about NbSs as an alternatives for stormwater management. In this study, which employed the CIMO (Context, Intervention, Mechanism, Outcome) framework, a corpus of 187 NbS-related publications (2000–2023) extracted from the Web of Science database were used, and we expounded upon the origins, objectives, and significance of NbSs in urban runoff and climate change, and the operational mechanisms of NbSs (including green roofs, permeable pavements, bioretention systems, and constructed wetlands), which are widely used in urban stormwater management, were also discussed. Additionally, the efficacy of NbSs in improving stormwater quality and quantity is discussed in depth in this study. In particular, the critical role of NbSs in reducing nutrients such as TSS, TN, TP, and COD and heavy metal pollutants such as Fe, Cu, Pb, and Zn is emphasized. Finally, the main barriers encountered in the promotion and application of NbSs in different countries and regions, including financial, technological and physical, regulatory, and public awareness, are listed, and future directions for improving and strategizing NbS implementation are proposed. This review gathered knowledge from diverse sources to provide an overview of NbSs, enhancing the comprehension of their mechanisms and applications. It underscores specific areas requiring future research attention.

Constructing underground reservoirs has emerged as a crucial strategy to address the shortage of fresh water in Rizhao, Shandong Province, China. However, the water quality, microbial community composition, and biogeochemical cycling of nutrients in underground reservoirs compared to raw water remain unknown. To unveil the characteristics of microbial community structures and their nitrogen cycling metabolic potential in coastal underground reservoirs, we utilized a functional gene array (GeoChip 5.0) in conjunction with high-throughput sequencing of 16S rRNA and 18S rRNA genes. Our findings indicate that the water quality in the underground reservoir exhibits a certain degree of eutrophication compared to raw water, with higher concentrations of TN, TP, NO3−N, NO2−-N, and Chl a, but lower concentrations of DO and NH4+-N. The alpha diversity of bacterial and microeukaryotic communities was significantly lower in the underground reservoir. The bacterial community presented a stronger correlation with environmental factors than the microeukaryotic community. Regarding the relative abundance of bacterial communities, Gammaproteobacteria dominated the bacterial community in raw water, while Gammaproteobacteria and Alphaproteobacteria dominated the bacterial community in underground reservoir water. Additionally, the relative abundance of Nitrospirae was noticeably higher in the underground reservoir water. Moreover, we found significantly higher sequence abundance of the archaea Thaumarchaeota in the underground reservoir. Furthermore, our analysis revealed that, except for the amoA functional gene, which significantly increased the metabolic potential of nitrification, the metabolic potential of other microbial nitrogen functional genes was significantly reduced. This reduction may contribute to the lower concentration of NH4+-N in the underground reservoir. This study provides a comprehensive understanding of the microbial community characteristics and their nitrogen cycling metabolic potential in underground reservoirs. It serves as a valuable reference for water source selection, the formulation of water quality assurance measures, and the construction and management of underground reservoirs for subsequent impounding.

Soil is a critical natural resource, and accurate estimation of soil erosion is vital for the optimal management and development of soil resources. Soil erosion assessment is necessary for long-term conservation plans, but the process can be expensive and time-consuming over large areas. It is imperative to examine the impact of water-induced soil erosion on cultivated lands, as it can cause significant damage. This study evaluates the effectiveness of four data-driven approaches (biogeography-based optimization, earthworm optimization algorithm, symbiotic organisms search, and whale optimization algorithm) combined with artificial neural network models in estimating soil erosion. The examined criteria include 14 geographic and environmental criteria, and the data used in a ratio of 70 to 30 for training and testing operations. And its results were measured by AUC values. The evaluation of AUC accuracy indices revealed compelling results. Specifically, in the case of SOS-MLP, the highest AUC values were observed, reaching 0.9973 for test data and 0.9296 for train data. Conversely, for WOA-MLP, the AUC values obtained were slightly lower but still notable, registering at 0.9809 for test data and 0.959 for train data. These values were also calculated for BBO-MLP (0.999 and 0.9327) and EWA-MLP (0.9304 and 0.9296) in the training and testing phases, respectively. Results showed that all four methods could successfully evaluate erosion susceptibility according to AUC values greater than 0.92, especially the BBO-MLP with the highest AUC values. Therefore, the findings of this study have shown that the combined optimization algorithms and Machine Learning used in this research have a suitable ability to optimize the artificial neural network and are very useful for identifying areas sensitive to erosion.

An accurate solar radiation (SR) prediction with a practical training approach is vital in estimating solar energy. A hybrid machine learning (ML) model is proposed for estimating the monthly SR. The proposed model includes two ML approaches: the response surface method (RSM) and support vector regression (SVR). The RSM is used to optimize the input variables and handle the data points for the prediction of SR. The first ML approach presents two input variables to estimate data handling. In the second ML process, the SVR model provides a nonlinear regression for handling data supplied by RSM. A new model was employed to predict the SR data taken from two stations in Turkey, as the temperature and extraterrestrial radiation were used as the model inputs. The RSM, artificial neural networks (ANNs), SVR, multivariate adaptive regression spline (MARS), M5 model tree (M5Tree) and convolutional neural networks (CNN) methods as existing ML approaches were employed to compare the predictions proposed hybrid ML approaches using several criteria. Data were split into training and testing sets, and two scenarios were established to compare models’ efficiencies according to different sets. The outcomes showed that the proposed model provides better accuracy for estimating SR using limited input data than other alternatives. The accuracy of the ANNs, SVR, MARS, M5Tree, RSM and CNN models was improved using a hybrid ML model. The proposed RSM-SVR method enhanced the efficiency of the ANN, SVR, MARS, M5Tree, and RSM methods by RMSE margins ranging from 0.1% to 5.6%, 2.8% to 7.3%, 1.0% to 8.3%, 0.1% to 28%, and 2.0% to 5.9%, respectively.

This study investigates the viability of a strong algorithm (PSOGSA) merging particle swarm optimization (PSO) and gravity search algorithm (GSA) in tuning adaptive neuro-fuzzy system (ANFIS) parameters for modeling dimensionless experimental discharge of combined weir–orifices. The results are compared with the standard ANFIS and two hybrid models ANFIS tuned with PSO and GSA. The models are assessed by applying several dimensionless input parameters, consisting h / D (the ratio of upstream water depth to channel diameter), W / D (the ratio of orifice opening height to channel diameter), H / D (the ratio of plate height to channel diameter) and using comparison indices such as root-mean-square error and mean absolute error. The outcomes reveal that the new ANFIS-PSOGSA method provides superior accuracy in modeling dimensionless experimental discharge over the ANFIS-PSO, ANFIS-GSA and standard ANFIS method. Among the input parameters, the h/D was found to be the most effective input on modeling dimensionless experimental discharge while involving the H / D parameter deteriorated the models’ performances. The relative root-mean-square error differences between ANFIS-PSOGSA and ANFIS are found as 50% and 68.29% for pipe A and B, respectively. By implementing the ANFIS-PSOGSA, the accuracy of ANFIS-PSO and ANFIS-GSA is also improved in modeling dimensionless experimental discharge by 45.71% and 29.63% in pipe A and by 63.89% and 45.83% in pipe B with respect to root-mean-square error.

In the field of global geology, the intricate relationship between Earth’s mineral resources and their pivotal role in fostering sustainable human development is a fundamental area of scholarly investigation. This research thoroughly explores the complex dynamics between technological progress, financial innovations, and regulatory structures in the context of sustainable management of mineral resources. Covering the period from 2000 to 2022 and including key international stakeholders in the mineral industry, the study employs a Panel Autoregressive Distributed Lag (ARDL) approach to assess the effects of Industry 4.0 technologies (IND), Financial Technology (FinTech), Regulatory Quality (RQ), Energy Efficiency (EF), and per capita Gross Domestic Product (GDP) on Mineral Resource Capital (MRC). The results reveal a substantial, positive long-term influence of Industry 4.0 on Mineral Resource Capital, underscoring the essential role of technology. Conversely, FinTech exhibits a nuanced association with Mineral Resource Capital, indicating the necessity for its careful incorporation. The analysis highlights the significance of regulatory quality in the sustainable management of resources, while the negative impact of energy efficiency points towards the need for more sustainable industrial practices. Through variance decomposition and impulse response analysis, the study delineates the responsiveness of Mineral Resource Capital to various economic fluctuations, with Granger causality tests offering additional insight into these complex interactions. This investigation contributes significantly to the existing scholarly literature and offers crucial perspectives for policy-makers and industry participants, advocating for a cohesive strategy that effectively harnesses technological and financial innovations to enhance sustainability and economic resilience in the mineral sector.

This study addresses the route scheduling problem for the heterogeneous robotic delivery system (HRDS) that perform delivery tasks in an urban environment. The HRDS comprises two distinct types of vehicles: an unmanned ground vehicle (UGV), which is constrained by road networks, and an unmanned aerial vehicle (UAV), which is capable of traversing terrain but has limitations in terms of energy and payload. The problem is formulated as an optimal route scheduling problem in a road network, where the goal is to find the route with minimum delivery cost and maximum customer satisfaction (CS) enabling the UAV to deliver packages to customers. We propose a new method of route scheduling based on an improved artificial bee colony algorithm (ABC) and the non-dominated sorting genetic algorithm II (NSGA-II) that provides the optimal delivery route. The effectiveness and superiority of the method we proposed are demonstrated by comparison in simulations. Moreover, the physical experiments further validate the practicality of the model and method.