Effective Resource Management Research Articles

Методичні підходи до визначення вартості послуг з утримання пунктів пропуску через державний кордон для автомобільного сполучення

Introduction. The article examines the methodology for determining the cost of services for maintaining border crossing points for road transport. The research highlights the importance of establishing effective resource needs to ensure the uninterrupted operation of these points, which is critical for national security and Ukraine’s economic development. Problem Statement. Given the current development of the infrastructure sector, determining the cost of comprehensive maintenance and technical servicing of buildings, structures, and adjacent areas within a road border crossing point is of particular importance. Ensuring the smooth and efficient operation of these points requires a systematic approach to evaluating provided services and developing a methodology that takes into account various aspects of maintaining and servicing infrastructure objects. Analyzing the cost of such services becomes a crucial stage in planning and effective resource management, as it affects service quality, availability, and competitiveness. Therefore, there is an urgent need to develop a scientifically sound methodology for determining service costs, considering the specificities of these facilities' operation.

Assessment and Modeling of Water Quality in the Niger River: A Comprehensive Study Using Water Quality Indices and Intelligent Techniques

The Niger River is essential to the livelihoods of those in Bamako and its neighboring areas, providing vital resources for drinking water, agriculture, livestock, industry, and fishing. Given its significance across these sectors, there is a pressing need to establish an effective water resource management strategy that incorporates a thorough qualitative assessment of the river's water quality. This research seeks to characterize the water quality of the Niger River by employing Water Quality Indices (WQI) and intelligent modeling techniques. To fulfil this objective, various physicochemical parameters, including pH, electrical conductivity (EC), nitrate (NO3-), nitrite (NO2-), and iron (Fe), were collected from 40 sampling points along the river during three distinct periods: December 2017, March 2018, and July 2018. The study utilized a weighted arithmetic approach to compute the WQIs, while the predictive models were developed using two of the most famous and effective modeling techniques namely Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN). In order to evaluate the predictive performance of the models, the dataset was partitioned into three distinct segments, allocating 60% for training purposes, 20% for validation, and the remaining 20% for testing, with the segments organized in a sequence from upstream to downstream. The performance of both models was evaluated using metrics such as the correlation coefficient (r²), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The computed Water Quality Indices (WQIs) vary from 0.44 to 1887.40, indicating a diverse range of water quality across the samples analyzed. The classification of these samples reveals that 62.5% are considered excellent, while 15% are categorized as good, another 15% as poor, 2.5% as very poor, and 5% as unsuitable for consumption. Furthermore, the results derived from ANN with five inputs, one hidden layer (13 neurons) and one output (WQI) demonstrates superior efficiency in assessing water quality.

Local knowledge of Mecula Haroano Laa as social cohesion of agricultural communities in Buton utara district

Mecula Haroano Laa is a local wisdom that includes beliefs, norms, and practices passed down from generation to generation in the context of agricultural resource preservation and community cultural identity formation. The author is interested in investigating the practices of the Mecula Haroano Laa tradition, which is unique to North Buton Regency and has unique specifications and characteristics. This research uses a qualitative approach. The data collection techniques used in this study are in-depth interviews and participatory observations. The results of this study demonstrate that Mecula Haroano Laa in North Buton society is more than just an agricultural custom; it is also an attempt to strengthen social solidarity among community members. This practice reflects the spirit of solidarity, gotong royong together, and respect for the environment. The North Buton community is actively involved in implementing Mecula Haroano Laa as a form of participation in developing sustainable agriculture. This research contributes to understanding the importance of local wisdom in building social cohesion in communities. Research implications include sustainable planning and efforts to empower communities in developing farms in North Buton Regency. Natural resource management policies may incorporate. Mecula Haroano Laa’s effective and sustainable resource management techniques to promote wise use, environmental conservation, economic resilience, and dependency reduction.

Integrated geophysical and GIS approaches for groundwater potential assessment: a case study of Aladja, Delta State, Nigeria

ABSTRACT This study explores the groundwater potential in Aladja, Udu, Local Government Area of Delta State, Nigeria, using an integrated approach combining electrical resistivity techniques with Remote Sensing (RS) and Geographic Information System (GIS) methods. The primary aim is to delineate aquifers and comprehensively assess groundwater resources. Methods include Vertical Electrical Sounding (VES) to gather resistivity data, RS and GIS for analyzing geological and hydrological parameters such as lineament density, drainage density, slope, soil type, and land use. The resistivity values ranged from 147 to 460.50 Ωm, with aquifer thicknesses varying between 6.50 and 36.30 m. The RS and GIS analysis indicated high groundwater potential in regions characterized by significant lineament density and moderate slopes. The study highlights the complementarity of VES and RS/GIS in groundwater exploration, revealing substantial groundwater resources in the northwestern regions of the study area. This approach underscores the importance of integrating these methods for a more accurate assessment of groundwater potential. The novelty of this study lies in validating RS and GIS findings with VES data, ensuring reliable groundwater potential mapping and effective resource management. This integrated methodology offers a robust framework for sustainable groundwater resource assessment and management in similar geological settings.

Leveraging Technological Research Methodologies to Improve Employee Performance in Organizations

The effective management of human resources in organizations is imperative for attaining optimum performance and eventual profitability. The increasingly competitive marketplace has compelled organizations to work towards attaining operational excellence, which heavily relies on the performance of its human resources. The adoption of cutting-edge research techniques has emerged as a pivotal strategy as organizations endeavour to achieve operational excellence. However, human resource managers have had to grapple with various challenges associated with the management of employee data generated and managed through digital platforms and tools, including issues such as data safety and security. This paper explores the intersection between tech-enhanced, advanced research methodologies and employee performance that is geared toward sustainable economic growth at the organizational level. Notably, the paper examines big data analytics as a tech-enhanced research tool leveraged by organizations in a bid to understand their human resource and the best way they can be managed to ensure high performance, which translates into high productivity and competitiveness in their respective sector or industries. In particular, this paper conducts a comprehensive review of recent extant literature regarding the adoption of big data analytics, which has revolutionized how organizations collect and analyze vast amounts of data, thereby enhancing their decision-making process. The reviewed literature focused on human resource aspects such as recruitment and selection; employee development; employee performance management and compensation structures. The review revealed that big data has emerged as a critical tool for managing large volumes of data that are acquired via numerous digital tools as organizations grapple with the integration of technology into their human resource practices. The technologies have helped human resource managers with these structured and unstructured data, they gain insight that in turn, guides their decision-making processes and improves the overall operational efficiency. The review outlines the transformative potential of integrating big data tools with human resource management and recommends that organizations should invest in these tools to enhance their recruitment and selection, employee development, performance measurement and upgrade their compensation structure.

Integration of geospatial techniques and analytical hierarchy process (AHP) ind demarcating groundwater potential zones in Lakhimpur district, Assam, India

Overexploitation and climate change have threatened the availability and sustenance of groundwater resources. A proper understanding of the regional distribution of groundwater is crucial to ensure long-term water security. The present study aims to identify the groundwater potential zones in the Lakhimpur district of Assam using the Analytical Hierarchy Process (AHP) in combination with geospatial technologies. The occurrence of groundwater in the region was determined by several factors including geomorphology, lithology, slope, distance from the river, drainage density, lineament density, rainfall, curvature, soil, land use, land cover, Normalized difference vegetation index (NDVI), and topographic wetness index (TWI). These factors organized as thematic layers were utilized to generate a groundwater potential zones (GWPZ) map in the GIS environment. The AHP, an effective decision-making technique, was adopted to assign weights to each thematic layer corresponding to their relative importance in influencing groundwater availability. The GWPZ map prepared using the weighted overlay techniques was categorized into three classes: good, moderate, and poor. The result revealed that the good potential zone comprises 1909.41 km2 (65.12%), moderate 1018.25 km2 (34.72%) and the poor zone comprises 4.22 km2 (0.14%) of the total geographical area. The obtained results of 73.33% (Overall accuracy), 0.708 (ROC-AUC), and 0.50 mbgl (groundwater level fluctuation) between pre-monsoon and post-monsoon prove that the model has performed satisfactorily in identifying groundwater potential zones. The findings provide a framework for the effective exploration and management of groundwater resources, ensuring their future availability in the region.

ПРОБЛЕМИ УПРАВЛІННЯ ПЕРСОНАЛОМ ТОРГОВИХ ПІДПРИЄМСТВ В КРАЇНАХ ЄВРОПЕЙСЬКОГО СОЮЗУ

Personnel management at any enterprise is a complex and multifaceted process. Managers must have deep knowledge and experience in the field of motivation, coordination, staff training, etc., The greatest asset of any organization is its human resources, which ensure the achievement of the company's goals and objectives. It is unfortunate that most companies have neglected the development and management of their main asset, which is human resources. Human resource management is a function in an organization that focuses on hiring, managing, and shaping development directions for people who work in an organization. It is also a strategic and comprehensive approach to managing people, culture and workplace environment. Employee engagement and motivation are critical to the success of any business. Engaged employees are more productive, more committed, and more likely to stay with their employer. In today's fast-paced business environment, it is more important than ever for managers to keep their employees engaged and motivated. The importance of managing human resources lies in the fact that the most important resource of an organization is people, and that the productivity of a significant world lies among them. In order for the management of human resources to have little significant impact on the efficiency of the organization, it is necessary to expand and effectively implement the required set of policies and processes for human resources. The strategic management of human resources is the key, which is significant, as spy workers reach the goals of the organization for additional policies and practices regarding human resources, ie integration into the strategy of human resources. The growing popularity of the promotion of the mechanism of human resource management has encouraged us to do some clear exercises to improve the efficiency of the organization. There have been very few efforts to maintain a connection between the practice of effective human resource management and efficient organization, as it has been carried out in the Ukrainian economy. Today, after the pandemic, the management of the effectiveness of the glasses without precedent changes.

Application of a reaction-based water quality model to the total dissolved solids concentration of the Pasig River.

With the goal to support effective water resource management, water quality models have gained popularity as tools for evaluating the distributions of pollutants and sediments. This work focuses on the application of the numerical solution of an advection-dispersion-reaction (ADR) water quality model for rivers and streams to a major Philippine waterway, the Pasig River. The water quality constituent is described by a system of reaction and advection-dispersion-reaction equations. The model and method are based on a previously used strategy where Guass-Jordan decomposition is applied to the matrix system and the resulting conservative form of the model is solved numerically using the fully implicit scheme and finite element method. The methodology is demonstrated by a case study in Pasig River involving the concentrations of total dissolved solids (TDS) obtained from the Department of Environment and Natural Resources (DENR) through the Pasig River Unified Monitoring Stations (PRUMS) report. Sensitivity analysis and parameter estimation are also applied to the model to assess which parameters influence the model output the most.

An explainable hybrid framework for estimating daily reference evapotranspiration: Combining extreme gradient boosting with Nelder-Mead method

Accurate estimation of reference evapotranspiration (ETo) is essential for effective water resources management, irrigation system design, and various hydrological and agricultural applications. This study employed extreme gradient boosting (XGBoost) model, signal decomposition techniques, and XGBoost coupled with Nelder–Mead (NM) method to enhance ETo prediction across two meteorological stations in Iran. This study proposed a novel framework which lies in its comprehensive integration of advanced techniques to create a model that is both interpretable and highly accurate for ETo estimation. For this aim, sixty meteorological variables were categorized into solar and cloud-based, temperature-based, wind and humidity-based, and pressure-based groups to analyze their effect on ETo estimation. Feature selection methods, including the Gradient Boosting Machine, Kendall’s Tau, and Relief Algorithm, were employed to identify the most influential predictors. Variables with normalized weights equal to or greater than 0.9 were selected for model input, resulting in the top 10% of variables being utilized. The XGBoost models were then developed using these selected inputs (level 1), a wavelet-based hybrid was developed according to the most effective features (level 2), and the XGBoost model was coupled by NM algorithm (level 3) for ETo estimation. Results of estimated ETo by levels 1 to 3 were compared with four common empirical approaches. The results indicated that Kendall’s Tau-based feature selection provided the most accurate predictions in Shiraz, achieving an RMSE of 0.721 (mm/day) for solar and cloud-based variables during the test phase. Additionally, the application of wavelet analysis further refined the model inputs, which enhanced ETo estimation in most variable groups. Integrating the NM algorithm with XGBoost demonstrated significant improvements in ETo estimation, where it could improve RMSE to 0.091 and 0.155 (mm/day) for testing section in Fasa and Shiraz, respectively.

Effect of Entrepreneurship Skills Training on Agricultural Production

This study examines the impact of entrepreneurship skills training on agricultural productivity among small-holder coffee farmers in Kenya. Entrepreneurship in coffee farming involves innovation, effective resource management, risk-taking, and market adaptation—all essential for the entrepreneurial process. Given the critical role of coffee farming in Kenya's economy and the challenges faced by small-holder farmers, this research explores how entrepreneurial skills can enhance productivity and sustainability. A mixed-method approach was employed, with data collected from 12,000 farmers using questionnaires. A sample size of 387 was determined using Yamane's formula, and stratified random sampling was applied across 17 factories. Data analysis was conducted using SPSS software. The results indicate that training in business planning, technology, financial management, and leadership significantly improves coffee farming performance. The study concludes that ineffective entrepreneurship training is closely linked to poor performance in coffee farming. It recommends that the government, through the ministries of agriculture and co-operatives, prioritize entrepreneurship training for small-holder coffee farmers to enhance productivity. These findings can guide future interventions aimed at reviving the coffee industry and ensuring its sustainability in Kenya.

Decoding Urban Intelligence: Clustering and Feature Importance in Smart Cities

The rapid urbanization trend underscores the need for effective management of city resources and services, making the concept of smart cities increasingly important. This study leverages the IMD Smart City Index (SCI) dataset to analyze and rank smart cities worldwide. Our research has a dual objective: first, we aim to apply a set of unsupervised learning models to cluster cities based on their smartness indices. Second, we aim to employ supervised learning models such as random forest, support vector machines (SVMs), and others to determine the importance of various features that contribute to a city’s smartness. Our findings reveal that while smart living was the most critical factor, with an importance of 0.259014. Smart mobility and smart environment also played significant roles, with the importance of 0.170147 and 0.163159, respectively, in determining a city’s smartness. While the clustering provides insights into the similarities and groupings among cities, the feature importance analysis elucidates the critical factors that drive these classifications. The integration of these two approaches aims to demonstrate that understanding the similarities between smart cities is of limited utility without a clear comprehension of the importance of the underlying features. This holistic approach provides a comprehensive understanding of what makes a city ’smart’ and offers a robust framework for policymakers to enhance urban living standards.

APPLICATIONS OF NON-TRADITIONAL EARNED VALUE MANAGEMENT MODELS IN PROJECT ANALYTICS

Effective management of financial resources in projects is crucial for project success. Often, difficulties with financial resources, such as budget overruns, lead to unfavorable consequences that directly impact the successful completion of the project, the quality of the outcome, and stakeholder satisfaction. Therefore, identifying and developing tools for the effective management of financial resources, is of paramount importance. The purpose of this study is to apply non-traditional earned value management (EVM) models based on machine learning to predict project costs. To achieve the research objectives, previous literature on the topic was analyzed, a dataset of past projects was prepared, and a machine learning model was applied. The study found that non-traditional models, such as the regression algorithm AdaBoost, produced results close to the actual costs. The research indicates that the developed model could become an indispensable tool for project management and business decision-making, as it demonstrates the ability to adapt to various conditions and make accurate forecasts.

Implementation of BLT DD: Insights from Village Cash Assistance Program

Background: Direct Cash Assistance from village funds (BLT DD) is vital for supporting rural communities, particularly during economic challenges. Specific background: Since 2020, Desa Gelam has allocated funds for BLT DD, making it a relevant case for examining program implementation. Knowledge gap: Limited research exists on the detailed execution and impact of BLT DD at the village level. Aims: This study analyzes the implementation of BLT DD in Desa Gelam, focusing on the roles of village officials and the importance of standard operating procedures (SOPs) for transparency. Results: Findings reveal that clear SOPs enhance the effective distribution of aid, with significant contributions from the village head and welfare officer, aligning with Edward III’s theory of communication, resources, disposition, and bureaucratic structure. Novelty: This research offers insights into how structured procedures improve social aid program execution, emphasizing village leadership. Implications: The study highlights the necessity of clear guidelines and strong leadership to ensure successful social assistance programs like BLT DD, serving as a model for enhancing aid distribution efficiency in other villages. Highlights : Role of SOPs: Clear standard operating procedures enhance transparency and efficiency in aid distribution. Leadership Impact: Village head and welfare officer are crucial for the successful implementation of BLT DD. Theoretical Alignment: Findings support Edward III’s theory on effective communication and resource management in social programs. Keywords: Direct Cash Assistance, BLT DD, Desa Gelam, standard operating procedures, village leadership

Pengaruh Perceived Overqualification dan Work Stress Dengan Moderasi Transformational Leadership Terhadap Innovative Work Behavior dan Turnover Intention

This research aims to analyze and explore the influence of two key factors, namely Perceived Overqualification and Work Stress, on employee Innovative Work Behavior and Turnover Intention in the context of logistics companies. In addition, this research considers the moderating role of Transformational Leadership in the relationship between Perceived Overqualification and Work Stress with Innovative Work Behavior and Turnover Intention. This research involved a survey of employees at a logistics company using a questionnaire, then the data collected was analyzed using quantitative methods with the population in this study involving 180 employees at a multinational logistics company that provides warehousing and distribution services in the Bekasi Regency area. The research results show that Perceived Overqualification and Work Stress have an influence on Turnover Intention. Apart from that, Transformational Leadership has been proven to moderate the relationship between Perceived Overqualification and Innovative Work Behavior. These results indicate that human resource management in logistics companies must consider Perceived Overqualification and Work Stress as important factors in promoting innovative work behavior that can reduce employee turnover intentions. This research has practical implications for logistics companies in developing more effective and efficient human resource management policies. Apart from that, this research also provides theoretical contributions in understanding the relationship between Perceived Overqualification, Work Stress, Transformational Leadership, Innovative Work Behavior and Turnover Intention in the context of logistics companies.

Challenges of water environment governance in China: based on a qualitative comparative analysis approach

ABSTRACT As a developing country focused on water pollution control, understanding the mechanisms behind China's water environment governance challenges is crucial. This study uses fuzzy set qualitative comparative analysis (fs/QCA) to develop a localized political-economic-social-technological (PEST) framework tailored to local governance realities, analyzing cases from the second round of Central Environmental Protection Inspection (CEPI). Two critical paths of local water governance challenges (CWEG) are identified: (1) In the 'government capacity constraints” path, stable yet limited official autonomy, combined with weak social supervision, obscures the institutional environment's empowering role and restricts local governance capacity, hindering environmental management effectiveness. (2) In the 'formalism in governance' path, a strong institutional environment, significant fiscal decentralization, robust digital infrastructure, green technology innovation, and low official turnover create a favorable governance environment. However, under the logic of avoiding accountability, formalism may emerge, posing challenges to effective water resource management.

Climatic and hydrological variability in the Oued Lakhdar watershed (Central High Atlas, Morocco)

Abstract The results of this study highlighted significant changes in hydroclimatic conditions over the period studied. Annual rainfall shows significant variability, with years such as 1996 characterised by exceptional humidity, while others, such as 2017, stand out for their particularly marked drought. This variation can be quantified using the Rainfall Anomaly Index (RAI), providing crucial information for water resource management. Temperatures also show seasonal fluctuations, oscillating between hot summers and cold winters. In particular, 2017 was exceptionally warm, while 2011 was unusually cold. In terms of river flows, there has been considerable variability, with some years showing high flows (e.g. 2011) and others with very low flows (e.g. 1990). However, the general trend towards an increase in flow from the 2010 onwards is notable (Sgat station). The RCV index, which classifies years according to surplus or deficit, highlights the predominance of deficit years. In addition, the Monthly Flow Coefficient (MFC) allows us to subdivide the hydrological regime into periods of high and low water. These results highlight the importance of monitoring hydro-climatic conditions for effective management of water resources and the implementation of measures to adapt to climate change.

A practical approach to promoting sustainable development in the construction industry through the use of the LOTUS-Vietnamese green-building assessment tool

In the context of global climate change mitigation, building design optimization is crucial for effective and efficient resource management throughout a project’s life cycle. This study addresses the challenge of assessing green building levels using a Vietnamese perspective. A survey of over 220 participants, including investors, consultants, contractors, and other stakeholders, was conducted to identify factors influencing green building assessments. Data analysis, employing SPSS software, explored 57 factors encompassing life-cycle economic optimization, energy and water conservation, material selection, project management, and strategic market benefits. The findings highlight effective energy management as a key determinant shaping sustainable building approaches and assessments. These results offer valuable insights for life cycle cost assessments and environmental impact evaluations during project implementation. Furthermore, the study emphasizes the importance of project developers prioritizing factors that influence the achievement of green building goals based on LOTUS green building rating standards in Vietnam. The predicted results demonstrate that governments should enforce legislation and establish clear green construction regulations to promote environmentally friendly growth, and that construction companies should use environmentally friendly materials and practices in their projects. This research contributes to the knowledge base for scholars, designers, and sustainability experts by systematically identifying factors that influence green building assessments under LOTUS standards.

Patterns of spatiotemporal variations in the hydrochemistry and controlling factors of bedrock aquifers in the northern region of the Linhuan mining area

Systematically studying the hydrochemical evolution of bedrock groundwater in mining areas during mining process is crucial for effective groundwater resource management and coal mine production. The spatiotemporal characteristics and hydrochemical evolution patterns of the Permian fractured sandstone aquifer (PA) and the Carboniferous Taiyuan Formation limestone aquifer (CTA), both of which are directly associated with coal mining in the northern Linhuan mining area, China, were investigated using multivariate statistical analyses, hydrochemical graphical methods, ion ratio analysis, and a conceptual model. 72 groundwater samples, collected before and after mining, were classified into four groups by hierarchical cluster analysis (HCA). Principal component analysis (PCA) and ion ratio analysis indicated that water-rock interactions involve mineral dissolution (carbonates, gypsum, dolomite, silicates), cation exchange, and common ion effects. Hydrochemical evolution is influenced by bedrock paleotopography, aquifer hydraulic conductivity, and mining drainage. Paletopographic differences significantly influence water-rock interactions and spatial variability in hydrochemistry, with ion concentrations in groundwater increasing as paleotopographic elevation decreases. The pattern of hydraulic conductivity reflects the control exerted by variations in aquifer characteristics on mineral dissolution, leading to minor changes in hydrochemical characteristics. Mining activities disrupt the aquifer's reducing environment, resulting in a significant increase in groundwater SO42− concentration. These findings provide insights and a solid theoretical foundation for studying the hydrochemical variations patterns of groundwater and these control mechanisms in the hidden coal fields of North China.

Advanced streamflow forecasting for Central European Rivers: The Cutting-Edge Kolmogorov-Arnold networks compared to Transformers

Accurate streamflow forecasting is crucial for effective water resource management, flood mitigation, and maintaining ecological balance, especially in Central Europe’s major rivers. This study investigates the performance of two advanced deep learning algorithms—Kolmogorov-Arnold Network (KAN) and Transformers—using long-term hydrological data from four key rivers: the Rhine, Danube, Elbe, and Oder. These rivers, characterized by varying flow regimes and significant human and climatic impacts, serve as vital arteries for both commerce and ecosystems.The dataset comprises historical daily streamflow data, alongside derived input parameters such as moving averages and flow rate changes, used to predict short-term (1 to 7 days) river discharges. The KAN model, leveraging learnable spline-based activation functions, was developed to enhance accuracy and interpretability in capturing complex hydrological patterns. In contrast, the Transformer model uses advanced attention mechanisms, which excel in handling sequential data with long-range dependencies.Results show that the KAN model significantly outperforms the Transformer in short-term forecasts (up to 3 days). For 1-day forecasts, the KAN achieved R2 values of 0.975 for the Rhine, 0.956 for the Danube, 0.992 for the Elbe, and 0.969 for the Oder, with MAPE values ranging from 3.20 % to 8.09 %. At the 3-day horizon, the KAN’s R2 values remained high, demonstrating its robustness. However, as the forecasting horizon extends to 7 days, the performance of both models converges.These findings underscore the methodological novelty of KAN, which provides enhanced short-term predictive capabilities compared to existing methods, offering valuable insights for improving water resource management strategies in complex hydrological settings.

Ensemble learning of catchment-wise optimized LSTMs enhances regional rainfall-runoff modelling − case Study: Basque Country, Spain

Accurate rainfall-runoff modeling is crucial for effective water resources management and planning, especially in flash catchments prone to rapid floods. This study investigates the performance of ensemble learning methods applied to regionally optimized deep learning models, specifically long short-term memory (LSTM) networks, for enhanced hydrological prediction. Three ensemble approaches were developed based on optimized regional hyperparameter settings: catchment-wise, top-10 regional, and K-means clustering selected configurations. These networks were trained, and the median of their simulations on the test set was considered the final prediction for each ensemble. The final predictions were then evaluated against observed data. Our findings show that ensemble learning methods consistently outperform conventional single-configuration approach of selecting the best regional setting in all locations, especially in catchments with prediction complexity or anthropogenic footprints. The catchment-wise ensemble demonstrated the highest prediction accuracy and robustness, highlighting the importance of tailoring network configurations to the unique characteristics of individual catchments. The findings highlight the potential of ensemble learning to significantly improve hydrological forecasts and inform better decision-making in water resources management. Specifically, this research demonstrates how ensemble learning of catchment-wise configurations can overcome limitations in regional hydrological predictions by deep learning models, addressing the “uniqueness of the place” paradigm.