Resource Management Decisions Research Articles

Anthropogenic Eutrophication Drives Major Food Web Changes in Mwanza Gulf, Lake Victoria

AbstractDiscerning ecosystem change and food web dynamics underlying anthropogenic eutrophication and the introduction of non-native species is necessary for ensuring the long-term sustainability of fisheries and lake biodiversity. Previous studies of eutrophication in Lake Victoria, eastern Africa, have focused on the loss of endemic fish biodiversity over the past several decades, but changes in the plankton communities over this same time remain unclear. To fill this gap, we examined sediment cores from a eutrophic embayment, Mwanza Gulf, to determine the timing and magnitude of changes in the phytoplankton and zooplankton assemblages over the past century. Biogeochemical proxies indicate nutrient enrichment began around ~ 1920 CE and led to rapid increases in primary production, and our analysis of photosynthetic pigments revealed three zones: pre-eutrophication (prior to 1920 CE), onset of eutrophication with increases in all pigments (1920–1990 CE), and sustained eutrophication with cyanobacterial dominance (1990 CE–present). Cladoceran remains indicate an abrupt decline in biomass in ~ 1960 CE, in response to the cumulative effects of eutrophication and lake-level rise, preceding the collapse of haplochromine cichlids in the 1980s. Alona and Chydorus, typically benthic littoral taxa, have remained at relatively low abundances since the 1960s, whereas the abundance of Bosmina, typically a planktonic taxon, increased in the 1990s concurrently with the biomass recovery of haplochromine cichlid fishes. Overall, our results demonstrate substantial changes over the past century in the biomass structure and taxonomic composition of Mwanza Gulf phytoplankton and zooplankton communities, providing a historical food web perspective that can help understand the recent changes and inform future resource management decisions in the Lake Victoria ecosystem.

Spatiotemporal Variations in Snow Cover on the Tibetan Plateau from 2003 to 2020

The variations in snow cover on the Tibetan Plateau play a pivotal role in comprehending climate change patterns and governing hydrological processes within the region. This study leverages daily snow cover data and the NASA Digital Elevation Model (DEM) from 2003 to 2020 to analyze spatiotemporal snow cover days and assess their responsiveness to climatic shifts by integrating meteorological data. The results reveal significant spatial heterogeneity in snow cover across the Plateau, with a slight decreasing trend in annual average snow cover duration. Snow cover is predominantly observed during the spring and winter seasons, constituting approximately 32% of the total snow cover days annually. The onset and cessation of snow cover occur within a range of 120–220 days. Additionally, an increasing trend in snow cover duration below 5000 m altitude was observed, in addition to a decreasing trend above 5000 m altitude. Sub-basin analysis delineates the Tarim River Basin as exhibiting the lengthiest average annual snow cover duration of 83 days, while the Yellow River Basin records the shortest duration of 31 days. The decreasing trend in snow cover duration closely aligns with climate warming trends, characterized by a warming rate of 0.17 ± 0.54 °C per decade, coupled with a concurrent increase in precipitation at a rate of 3.09 ± 3.81 mm per year. Temperature exerts a more pronounced influence on annual snow cover duration variation compared to precipitation, as evidenced by a strong negative correlation (CC = −0.67). This study significantly augments the comprehension of hydrological cycle dynamics on the Tibetan Plateau, furnishing essential insights for informed decision-making in water resource management and ecological conservation efforts.

Integration of Airborne Laser Scanning data into forest ecosystem management in Canada: Current status and future directions

Airborne Laser Scanning (ALS) has been the subject of decades of applied research and development in forest management. ALS data are spatially explicit, capable of accurately characterizing vegetation structure and underlying terrain, and can be used to produce value-added products for terrestrial carbon assessments, hydrology, and biodiversity among others. Scientific support for ALS is robust, however its adoption within environmental decision-making frameworks remains inconsistent. Cost continues to be a principal barrier limiting adoption, especially in remote, forested regions, however added challenges such as the need for technical expertise, unfamiliarity of data capabilities and limitations, data management requirements, and processing logistics also contribute. This review examines the current status of the integration of ALS data into forest ecosystem management in a Canadian context. We advocate for continued inter-agency acquisitions leading to integration of ALS into existing natural resource management decision pathways. We gauge the level of uptake thus far, discuss the barriers to operational implementation at provincial scales, and highlight how we believe ALS can support multiple objectives of forest and environmental management in Canada. We speak to potential benefits for supporting inter-agency terrain generation, ecosystem mapping, biodiversity assessments, silvicultural planning, carbon and forest health evaluations, and riparian characterizations. We conclude by providing key considerations for developing capacity using ALS and discuss the technologies future in the context of Canadian forest and environmental management objectives.

The Impact of Artificial Intelligence Replacing Humans in Making Human Resource Management Decisions on Fairness: A Case of Resume Screening

A growing number of organizations have used artificial intelligence (AI) to make decisions to replace human resource (HR) workers; yet, the fairness perceptions of the people affected by the decision are still unclear. Given that an organization’s sustainability is significantly influenced by individuals’ perceptions of fairness, this study takes a resume-screening scenario as an example to explore the impact of AI replacing humans on applicants’ perceptions of fairness. This study adopts the method of the online scenario experiment and uses SPSS to analyze the experimental data: 189 and 214 people, respectively, participated in two online scenarios, with two independent variables of decision makers (AI and humans), two dependent variables of procedural and distributive fairness, and two moderating variables of outcome favorability and the expertise of AI. The results show that the applicants tend to view AI screening resumes as less fair than humans. Furthermore, moderating effects exist between the outcome favorability and the expertise of AI. This study reveals the impact of AI substituting for humans in decision-making on fairness. The proposed model can help organizations use AI to screen resumes more effectively. And future research can explore the collaboration between humans and AI to make human resource management decisions.

Life cycle assessment to evaluate the integral water cycle in industrial supply: A real case study

Wastewater recycling technologies are developed in areas where the necessity of water resources cannot be satisfied by natural sources. Nevertheless, nowadays trends and European Union Plans show an increasing interest on using these technologies to reduce environmental impacts. This manuscript aims to address the question of the real environmental results of using these technologies and the differences between each specific case using the Life Cycle Assessment (LCA) methodology. A real case study is analyzed to answer this question: the integral water cycle of a northern of Spain, comparing a traditional water supply system (system I), and an alternative wastewater regeneration plant (system II). System II presents a higher impact for all categories (between 1.2 and 37 times higher), except for land use, where it is reduced by 53 %. These results show a larger impact produced by the alternative system due to higher energy and chemical product consumption. Energy consumption is the main factor causing the highest impact in most of the impact categories for both studied systems, including the one associated to the water resource consumption. It accounts for at least 50 % of the total impact for each system in 7 of the 16 evaluated impact categories. In terms of climate change, energy consumption is not particularly significant in system I, but it is for system II, where it represents around 50 % of that impact. In the categories where the impact is not determined by energy consumption, chemical product consumption and waste and discharge treatment are the most relevant factors. In this sense, this paper highlights the importance of analysing each case specifically and underscores the usefulness of using LCA methodology as a tool to improve decision-making in resource management, with water resources emerging as a crucial focal point.

Deep Learning-Assisted Online Task Offloading for Latency Minimization in Heterogeneous Mobile Edge

With the proliferation of smart devices in recent years, many applications requiring high computing capability and low latency have emerged. Edge computing is one of the promising paradigms to support such applications. Due to the high volatility of edge environments, e.g., frequent movements of mobile devices, varying task sizes, and time-variant channel conditions, we have to make the offloading and resource management decisions on the fly. This paper formulates and studies the problem of online task offloading and resource management in heterogeneous mobile edge environments. The goal of the problem is to minimize the overall system latency. We prove that the problem is NP-hard. Moreover, traditional algorithms needing long decision-making times are insufficient to support applications with high volatility. This paper proposes a deep learning-assisted online algorithm that can make fast decisions. In particular, we design an offline solver for the proposed problem and use a deep neural network to emulate the solver. We conduct extensive simulations to evaluate the proposed approach. Results show that the proposed approach is around <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$50,000\times$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$500\times$</tex-math></inline-formula> faster than the commercial Gurobi solver for the optimal solution and the proposed offline approximation solver, respectively. Moreover, the overall latency under the proposed approach is near-optimal.

A spectral mixture analysis based framework for estimating and charactering water use efficiency in heterogeneous drylands

Climate change and irrational land utilization have significantly disrupted the function and structure of the dryland ecosystems, leading to tremendously potential effects on the carbon and water cycles of the global terrestrial ecosystems. Ecosystem-scale water use efficiency (WUE) is a crucial metric that defines the ratio of gross primary productivity (GPP) to evapotranspiration (ET), revealing the coupling relationship between such two ecological processes and playing a significant role in water resource management decision-making. However, quantifying WUE in drylands remains a challenge because of their heterogeneous land surface challenging to characterize model parameters using traditional remote sensing techniques. Here, we developed a process-based framework to estimate GPP and ET, as well as WUE based on physical endmember fractions from spectral mixture analysis (SMA) model. And such framework is used to quantify the spatial and temporal evolution of WUE by optimizing the light use efficiency and assisting in the construction of a GV-Cws model for the separation of transpiration and evaporation processes during computation by utilizing the physically fractional vegetation and soil obtained from SMA in the Hexi Corridor of China, a typical dryland ecosystem. The results demonstrate that our framework can accurately estimate GPP and ET in the Hexi Corridor, as indicated by favorable performance in R2 (0.669, 0.705), RMSE (0.383,0.885) and NSE (0.534, 0.690) respectively. Such results surpass the accuracy of both two Moderate Resolution Imaging Spectroradiometer (MODIS) products and Penman-Monteith-Leuning Evapotranspiration V2 (PML_V2)’s ET product. These advantages highlight that our SMA-based framework can provide highly accurate and distinct characterization of surface carbon and water interaction processes. Meanwhile, the WUE of the Hexi Corridor ecosystem exhibited a positive trend from 2001 to 2017 (0.016 gC·m−2·mm−1·a-1), characterized by simultaneous increases in GPP and ET.

Exploring farmer choices in Southern Togo: Utilizing a strategy game to understand decision-making in agricultural practices

CONTEXTAgriculture plays a vital role in African countries, driving economic growth, employment, and non-agricultural sector improvements. However, persistent food insecurity and poverty among smallholder families raise concerns. Land fragmentation compounds these issues, limiting agricultural productivity and pushing farmers towards off-farm activities, despite Africa's potential agricultural markets. OBJECTIVEThe study's focus is Donomadé, a remote village in southern Togo. This study pursues a dual objective. Firstly, it investigates how household characteristics, including family size and resource availability, influence farmers' resource management decisions. Secondly, the research employs serious games to comprehend farmers' livelihood strategy choices within constrained real-world-like conditions. METHODSThe iterative and participatory Companion Modelling (ComMod) approach was utilized for the co-design and co-development of a serious game. This involved close collaboration with a local research team from the University of Lomé, a local NGO, and Donomadé village farmers. Various farming systems were characterized, and collective insights into Donomadé's agricultural dynamics were gathered through surveys, interviews, and workshops. RESULTS AND CONCLUSIONSRegardless of their in-game roles, farmers prioritize fulfilling basic family needs, such as food security and education instead of investing in agriculture. The study underscores the significance of household composition and resource availability when designing technology interventions or support mechanisms for farmers. Aligning interventions with farmers' overarching goals and challenges can enhance technology adoption. Furthermore, addressing food security remains crucial for sustainable development, as it not only improves human welfare and alleviates hunger but also forms the foundation for comprehensive solutions in the face of climate change, resource constraints, and population growth. SIGNIFICANCEThis research highlights that heavy investment in agriculture is not the priority for many farmers in southern Togo, despite it being their main source of sustenance and income. Addressing immediate family needs, especially food security, education, and housing, takes precedence over long-term agricultural investments. Given the persistent challenge of food security amidst global uncertainties, addressing these priorities is paramount to stimulate more significant interest in agricultural investment.

Application of artificial wetland technology in Miyun Reservoir water management

In recent years, with the increasing attention to environmental pollution, water quality management has become an important part of protecting water resources and maintaining ecological balance. And as an important means of water pollution remediation, artificial wetlands have gradually received widespread attention. Miyun Reservoir, as an important drinking water source in Beijing, is particularly important for water quality management. As an ecological restoration technology, artificial wetland has significant water quality improvement effect by simulating and improving the function of natural wetland. Therefore, artificial wetland has important scientific research and practical significance in the water management of Miyun Reservoir. In this paper, we will discuss the application of artificial wetland in water management of Miyun Reservoir, and summarise the results and conclusions of the study, highlight the impact and significance of the study, and look forward to the future research direction. By exploring the application of artificial wetlands in reservoir water management, it is expected to provide scientific basis and technical support for water resource management departments and decision makers, and contribute to the improvement of water quality and ecological restoration of Miyun Reservoir.

The Interplay of School Governance, Resource Management and Participatory Decision-Making for School Performance Optimization

The Interplay of School Governance, Resource Management and Participatory Decision-Making for School Performance Optimization

Comprehensive Analysis of Land Use and Cover Dynamics in Djibouti Using Machine Learning Technique: A Multi-Temporal Assessment from 1990 to 2023

Understanding land use and land cover (LULC) dynamics in semi-arid regions is vital for unraveling complex environmental processes and resource management. This study delves into the intricate interplay of land patterns and resource dynamics, offering indispensable insights into the environmental repercussions of these changes. The study aims to quantify land use categories in Djibouti's semi-desert region using remote sensing. It analyzes temporal changes and evaluates Random Forest (RF) algorithms for land use classification. Through meticulous quantification and comprehensive temporal analysis, the research contributes significantly to remote sensing and environmental science by enhancing understanding of land use dynamics and informing sustainable land management practices. Leveraging machine learning supervised classification on the Google Earth Engine (GEE) platform using Landsat data spanning four time periods (1990, 2002, 2012, and 2023), alongside spectral indices and Digital Elevation Model (DEM) data, our study achieves unprecedented insights. Our findings reveal a significant landscape transformation, delineating seven major land cover classes: mangroves, bushes, farmland, built-up areas, water bodies, barren land, and salt plains. With overall accuracy ranging from 89% to 95%, our assessments demonstrate significant changes in land use types over the studied period. Notably, mangroves, bushes, farmland, and salt areas witnessed declines, while built-up areas, water bodies, and barren lands expanded. This research underscores the pivotal role of remote sensing in monitoring long-term land use changes and their ecological impacts. By harnessing technological advancements, our study empowers stakeholders to make informed decisions for sustainable resource management and environmental conservation in semi-arid landscapes.

Sentinel-2 Perspective: Land Use and Land Cover Dynamics in East and West Karbi Anglong

This study delves into the dynamics of land use and land cover (LULC) in East and West Karbi Anglong using Sentinel-2 satellite imagery. In response to escalating environmental changes, remote sensing technologies, particularly Sentinel-2, offer crucial insights for sustainable land management. Through an analysis of LULC patterns, the study aims to inform evidence-based decisions for environmental conservation and resource management in the region. The present study highlights the utility of remote sensing in LULC assessments and acknowledges the necessity for customized approaches in regions with distinctive features such as Karbi Anglong. Methodologically, temporally stratified Sentinel-2 datasets were utilized, with meticulous pre-processing and accuracy assessments ensuring reliability. Results indicate notable shifts in LULC classes, including urban expansion and vegetation decline. Despite limitations, such as potential inaccuracies in data processing, the study emphasizes the importance of monitoring environmental changes and advocating for sustainable land management strategies.

Multimodel comparison and assessment of short to medium range precipitation and temperature forecasts over India: Implications towards forecasting of meteorological indices in India

AbstractA comprehensive assessment of the forecast skill of various meteorological indices over the Indian region from different numerical weather prediction (NWP) models is lacking in the literature. In this study the performance of four NWP models, namely Global Ensemble Forecast System (GEFSv12), European Center for Medium Range Forecasting (ECMWF), Climate Forecast System (CFSv2) and Indian Institute of Tropical Meteorology (IITM) towards forecasting of precipitation, temperature and associated meteorological indices, is evaluated at short to medium timescales across the Indian region. Further, the effect of ocean atmospheric (OA) oscillations on the precipitation/temperature forecast skill from the different NWP models is also assessed. Results show that the NWP models are better in predicting the meteorological indices than the quantitative forecasts. The ECMWF model was found to be the best for PCP forecasting with CFSv2 performing poorly. For temperature the GEFSv12 model performance was the lowest, compared to rest of the models. The models show poor skill in forecasting monsoon season precipitation compared to non‐monsoon and the temperature forecasts from the NWP models are particularly poor for the northern basins. Skilful temperature forecasts are observed in the Northwestern, Indo‐Gangetic and Central basins for the CFSv2 and ECMWF models. The forecast skill of precipitation indices are higher in the northwestern, central and Indo‐Gangetic basins compared to the rest. The skill of precipitation indices, namely rainy days, extreme rainy days and consecutive wet days, is higher during the monsoon seasons while the prediction skill of consecutive dry days is higher during the non‐monsoon season. OA analysis revealed that the ENSO phases have dominant effect on the forecast skill of the precipitation only. The temperature and meteorological indices forecasts are not affected significantly by the OA phases. The outcomes of this study have implications towards irrigation scheduling and water resources management decision making in India.

Predicting river water quality: An imposing engagement between machine learning and the QUAL2Kw models (case study: Aji-Chai, river, Iran)

Rivers play an essential role in supplying high-quality water to diverse sectors. Understanding water quality indicators and systematic monitoring is crucial for water resources management and macro-level decision-making. In this context, the forthcoming article delves into the simulation of three crucial parameters, namely EC, SAR, and TDS, through a reach of 106 km length along the Aji-Chai River, Iran, encompassing stations from Markid, Khajeh, Akhola, and Serin Dizj. This simulation employs three advanced machine learning models: SVM, GEP, and MLP, in conjunction with the QUAL2Kw mathematical simulator. The study meticulously evaluates the performance of these models using four key indices: RMSE, MAE, R2, and DDR. The calculated results unequivocally establish the superiority of the SVM in simulating three essential water quality parameters across all stations. This is supported by consistently high R2 and DDR values, along with low RMSE and MAE values. While the mathematical model used in this study showed reasonable accuracy in simulating the parameters under investigation, it consistently performed less effectively than the SVM model. In summary, the SVM model with specific parameters (C = 68.5, ε = 4.55, and γ = 205) emerges as the optimal choice for accurately simulating river water quality parameters based on the conducted study.

Quantitative forecasting of bed sediment load in river engineering: an investigation into machine learning methodologies for complex phenomena

Abstract The intricate calculation of bed sediment load (BSL), which is influenced by hydraulic, hydrological, and sedimentary factors, is vital for informed decision-making in water resource management. Machine learning models, which are gaining popularity due to their accessibility and ability to reveal complex relationships, play a significant role in tackling these challenges. The efficacy of gene expression programming (GEP) models, support vector machines (SVMs), multi-layer perceptron (MLP), and multivariate adaptive regression splines (MARS) has been assessed through measured data of number 540 obtained from six rivers, namely Oak Creek, Nahal Yatir, Sagehen Creek, Elbow River, Jacoby River, and Goodwin Creek from 1954 to 1992. The assessment of model performance has been conducted utilizing root mean square error (RMSE), R2, Nash–Sutcliffe coefficient (NSE), and developed discrepancy ratio (DDR) as indices. Following data normalization within the range of 0–1, the data models underwent training and testing processes with a partition ratio of 80% for training and 20% for testing. Four dimensionless parameters, denoted as Fr = U/√gy, U/U*, Se, and ω = τU/γs√gyDs3, were employed as inputs in the models. The outcomes indicate that they exhibit superior performance compared to other methods, as evidenced by the following metrics in predicting BSL during the test stage: RMSE = 1.4088, NSE = 0.73054, R2 = 0.8729, and maximum QDDR(max) = 1.9564.

Long-Term Trends and Variability of Hydroclimate Variables and Their Linkages with Climate Indices in the Songhua River

The long-term trends and variability of hydroclimate variables are critical for water resource management, as well as adaptation to climate change. Three popular methods were used in this study to explore the trends and variability of hydroclimate variables during last 122 years in the Songhua River (SHR), one of most important river systems in China. Results show the followings: (1) There was an obvious pattern of decadal oscillations, with three positive and three negative precipitation and streamflow anomalies. The lengths of these phases vary from 11 to 36 years. (2) Annual temperature demonstrated a statistically significant increasing trend in the last 122 years, and the trend magnitude was 0.30 °C/10 years in the last 50–60 years, being larger than that of the global surface temperature. It has increased much faster since 1970. (3) Monthly precipitation in the winter season in recent years was almost the same as that in earlier periods, but a significantly increasing monthly streamflow was observed due to snowmelt under a warming climate. (4) A statistically significant correlation between hydroclimate variables and climate indices can be determined. These results could be used to make better water resource management decisions in the SHR, especially under future climate change scenarios.

Investigating the Role of Geography Education in Natural Resource Management

Geography education plays a pivotal role in natural resource management by fostering an understanding of optimal conservation practices. Through comprehensive examination of regional environments and geography, we can gauge the impact of human activities on natural resources and propose sustainable solutions. By increasing environmental awareness, geography education elucidates the repercussions of activities such as deforestation, pollution, biodiversity loss, and climate change. This heightened awareness empowers us to refine policies and management practices, ensuring the judicious utilization and preservation of natural resources. This article underscores the paramount importance of geography education in natural resource preservation. Employing an applied and descriptive approach, the research methodology involves library studies. The findings highlight that geography education establishes vital links between individuals and their environment, facilitating informed resource management decisions. By analyzing regional geography and its influence on natural resources, optimal preservation strategies emerge. For instance, studying precipitation patterns enables efficient water management and drought mitigation. Ultimately, geography education equips us with the knowledge and tools to identify effective resource preservation methods, fostering sustainable utilization practices through enhanced environmental comprehension and spatial awareness.

Rating curve development and uncertainty analysis in mountainous watersheds for informed hydrology and resource management

Accurate measurement of continuous stream discharge poses both excitement and challenges for hydrologists and water resource planners, particularly in mountainous watersheds. This study centers on the development of rating curves utilizing the power law at three headwaters of the lesser Himalayas—Aglar, Paligaad, and Balganga—through the installation of water level recorders for stage measurement and salt dilution for discharge measurement from 2014 to 2016. The stream stage–discharge relationship, crucially known as the rating curve, is susceptible to numerous factors in mountainous watersheds that are often challenging to comprehend or quantify. Despite significant errors introduced during the rating curve development, such as stemming from observations, modeling, and parameterization, they are frequently overlooked. In this study, acknowledging the inherent uncertainty, we employ the maximum-likelihood method to assess uncertainty in the developed rating curve. Our findings reveal substantial inconsistency in the stage–discharge relationship, particularly during high flows. A novel contribution of this study is introducing a weighing factor concept that correlates uncertainty with the morphological parameters of the watershed. The higher value of the weighting factor in Paligaad (0.37) as compared to Balganga (0.35) and less in the case of Aglar (0.27) will have more uncertainty. The authors contend that precise rating curves and comprehensive uncertainty analyses can mitigate construction costs, foster robust decision-making, and enhance the perceived credibility of decisions in hydrology and water resource management.

Estimation of Average Annual Precipitation Considering Spatio-Temporal Variability Through the Use of Multiple Linear Regression in the State of Paraíba

Objective: The objective of this work was to calibrate a multiple linear regression equation to relate average annual precipitations to spatial localization of 145 stations placed on three mesoregions of Paraíba State, Brazil, Borborema, Agreste, and Mata Zone. Theoretical framework: The variability of precipitations in space and time is directly related to the hydrological cycle, land use and occupation, and local economy. In this sense, regional models for rainfall may provide relevant information, supporting the decision-making process associated with water resources planning and management. Method: For the study case, stations with rainfall series with more than 10 years of daily data were considered. The posts that their few flaws in their series had the data filled in by the Inverse Distance Weighted (IDW) deterministic method, which assings weitghted weights to the sample data, Where the influence of one station over another decreases with the distance to the station to be estimated. Results and conclusion: The results were moderate, with a determination coefficient of 0.62 for the studied mesoregions. The average percentage errors of the estimate showed that most of the analyzed surface is classified with errors between 8.3% and 22,8%. Implications of the research: The multiple regression statistical tool was used for precipitation data with high variability, proving effective modeling. This type of study helps with decision-making in water resource management and climate monitoring studies. Originality/value: Using the multiple regression statistical tool, it was possible to obtain a linear equation to estimate the average annual precipitation in three mesoregions of the Paraíba State.

Human Resource Management and Organisation Decision Optimization based on Data Mining

Human Resource Management and Organisation Decision Optimization based on Data Mining