scholarly journals Groundwater Level Resources Management Modelling: A Review

2021 ◽  
Author(s):  
Banjo Ayoade Aderemi ◽  
Thomas Otieno Olwal ◽  
Julius Musyoka Ndambuki ◽  
Sophiar S. Rwanga
Keyword(s):  
Resource Management ◽  
Groundwater Level ◽  
Distributed Storage ◽  
Groundwater Resources ◽  
Data Gathering ◽  
Data Driven ◽  
The Internet ◽  
Resources Management ◽  
Management Models ◽  
Efficient Data

Globally, groundwater is the largest distributed storage of freshwater that plays an important role in an ecosystem’s sustainability in addition to aiding human adaptation to both climatic change and variability. However, this resource is not unlimited and its sustainability is highly dependent on its prudent use. Thus, efficient management of groundwater resources to prevent overexploitation, scarcity and drought has become a major challenge for researchers as well as water managers. To solve these challenges, many solutions such as simulation and optimisation models have been proffered through the use of historical data. Therefore, this has made efficient data gathering essential to maintain data-driven groundwater level resource management models from the observation site. The global evolution of the Internet of Things (IoTs), has increased the nature of data gathering for the management of groundwater resources. Recently, a number of research challenges such as the lack of computational efficiency and scalability due to uncertainties from input parameters to the groundwater level resource model have been revealed in the management of groundwater level resources. In addition, efficient data-driven groundwater level resource management relies hugely on information relating to changes in groundwater resource levels as well as its availability. At the moment, the groundwater managers lack an efficient and scalable groundwater management system to gather the required data. The literature revealed that the existing methods of collecting data lack efficiency to meet computational model requirements and meet management objectives. Although the IoTs enabled automated data processing systems are in existence by transmitting the generated data from IoT enabled devices into the cloud through the Internet. However, traditional IoT Internet is not scalable and efficient enough to process the generated vast IoT data. Thus, it is necessary to have an efficient and scalable IoT system to extract valuable information in real-time for groundwater level resource management. Unlike previous surveys which solely focussed on particular groundwater issues related to simulation and optimisation management models, nonetheless, this paper seeks to highlight the current groundwater level resources management models as well as the IoT contributions.

scholarly journals Groundwater Resources Management Modelling: A Review

2021 ◽  
Author(s):  
Banjo Ayoade Aderemi ◽  
Thomas Otieno Olwal ◽  
Julius Musyoka Ndambuki ◽  
Sophiar S Rwanga
Keyword(s):  
Resource Management ◽  
Groundwater Management ◽  
Groundwater Resources ◽  
Data Gathering ◽  
Groundwater Resource ◽  
Data Driven ◽  
The Internet ◽  
Resources Management ◽  
Efficient Data ◽  
Groundwater Resources Management

Globally, groundwater is the largest distributed storage of freshwater that plays an important role in an ecosystem’s sustainability in addition to aiding human adaptation to both climatic change and variability. However, groundwater resources are dynamic and often changes as a result of land usage, abstraction as well as variation in climate. Thus, efficient management of groundwater resources to prevent overexploitation, scarcity, and minimising the effects of drought has become a major challenge for researchers as well as water managers. Furthermore, a number of research challenges such as the lack of computational efficiency and scalability due to uncertainties from input parameters to the groundwater resource model have been revealed in the management of groundwater resources. To solve these challenges, many conventional solutions such as numerical techniques have been proffered for groundwater modelling. Also, the use of data-driven techniques such as machine learning is gaining more attraction to solve these aforementioned challenges. Thus, this has made efficient data gathering essential to maintain da-ta-driven groundwater resources management models from the observation site. The global evolution of the Internet of Things (IoTs), has increased the nature of data gathering for the management of groundwater resources. In addition, efficient data-driven groundwater resource management relies hugely on information relating to changes in groundwater resources as well as their availability. Although the IoTs enabled automated data processing systems are in existence by transmitting the generated data from IoT enabled devices into the cloud through the Internet. However, traditional IoT Internet is not scalable and efficient enough to process the generated vast IoT data At the moment, some pieces of the literature revealed the groundwater managers lack an efficient, scalable and real-time groundwater management system to gather the required data. Also, the literature revealed that the existing methods of collecting data lack efficiency to meet computational model requirements and meet management objectives. Thus, it is necessary to have an efficient and scalable IoT system to extract valuable information in real-time for groundwater resource management. Unlike previous surveys which solely focussed on particular groundwater issues related to simulation and optimisation management methods, nonetheless, this paper seeks to highlight the current groundwater management models as well as the IoT contributions

scholarly journals A Review of Groundwater Management Models with a Focus on IoT-Based Systems

2021 ◽  
Author(s):  
Banjo Aderemi ◽  
Thomas Otieno Olwal ◽  
Julius Musyoka Ndambuki ◽  
Sophia Sudi Rwanga
Keyword(s):  
Groundwater Management ◽  
Distributed Storage ◽  
Groundwater Resources ◽  
Data Gathering ◽  
Land Usage ◽  
Management Models ◽  
Management Objectives ◽  
Efficient Data ◽  
The Moment ◽  
The Internet Of Things

Globally, groundwater is the largest distributed storage of freshwater and plays an important role in an ecosystem’s sustainability in addition to aiding human adaptation to both climatic change and variability. However, groundwater resources are dynamic and often change as a result of land usage, abstraction, as well as variation in climate. To solve these challenges, many conventional solutions, such as certain numerical techniques, have been proffered for groundwater modelling. The global evolution of the Internet of Things (IoT) has enhanced the culture of data gathering for the management of groundwater resources. In addition, efficient data-driven groundwater resource management relies hugely on information relating to changes in groundwater resources as well as their availability. At the moment, some studies in the literature reveal that groundwater managers lack an efficient and real-time groundwater management system that is needed to gather the required data. Additionally, the literature reveals that the existing methods of collecting data lack the required efficiency to meet computational model requirements and meet management objectives. Unlike previous surveys, which solely focussed on particular groundwater issues related to simulation and optimisation management methods, this paper seeks to highlight the current groundwater management models as well as the IoT contributions.

scholarly journals A Review of Groundwater Management Models with a Focus on IoT-Based Systems

2021 ◽  
Vol 14 (1) ◽  
pp. 148
Author(s):  
Banjo Ayoade Aderemi ◽  
Thomas Otieno Olwal ◽  
Julius Musyoka Ndambuki ◽  
Sophia Sudi Rwanga
Keyword(s):  
Groundwater Management ◽  
Distributed Storage ◽  
Groundwater Resources ◽  
Data Gathering ◽  
Land Usage ◽  
Management Models ◽  
Management Objectives ◽  
Efficient Data ◽  
The Moment ◽  
The Internet Of Things

Globally, groundwater is the largest distributed storage of freshwater and plays an important role in an ecosystem’s sustainability in addition to aiding human adaptation to both climatic change and variability. However, groundwater resources are dynamic and often change as a result of land usage, abstraction, as well as variation in climate. To solve these challenges, many conventional solutions, such as certain numerical techniques, have been proffered for groundwater modelling. The global evolution of the Internet of Things (IoT) has enhanced the culture of data gathering for the management of groundwater resources. In addition, efficient data-driven groundwater resource management relies hugely on information relating to changes in groundwater resources as well as their availability. At the moment, some studies in the literature reveal that groundwater managers lack an efficient and real-time groundwater management system which is needed to gather the required data. Additionally, the literature reveals that the existing methods of collecting data lack the required efficiency to meet computational model requirements and meet management objectives. Unlike previous surveys, which solely focussed on particular groundwater issues related to simulation and optimisation management methods, this paper seeks to highlight the current groundwater management models as well as the IoT contributions.

Research on HRM under Internet Environment

2014 ◽  
Vol 687-691 ◽  
pp. 4836-4839
Author(s):  
Liu Jin
Keyword(s):  
Resource Management ◽  
Human Resources ◽  
Human Resource Management ◽  
Human Resource ◽  
Human Resources Management ◽  
Supply And Demand ◽  
The Internet ◽  
Management Reform ◽  
Resources Management ◽  
The Impact

Human resource management is the mainly field of Internet business applications. The impact of the Internet on corporate human resource management can be summarized as changing macroeconomic environment, improve openness, democracy and scientific human resources management. The Internet has changed market environment of human resources, resulting in a shortage of talent required to change people's knowledge structure, the structural supply and demand of talent more prominent. Internet lead to faster flow of talent, but also conducive to business properly treat and handle the flow of talent problem. Internet help enterprises realize the sharing of human resources, increase the likelihood of sharing scarce talent while gain a better talent to share results. Internet can promote human resources management reform to keep up with the management of information technology and networking pace, but also to promote changes in corporate human resources management concept.

scholarly journals Monitoring Groundwater Storage Depletion Using Gravity Recovery and Climate Experiment (GRACE) Data in the Semi-Arid Catchments

2018 ◽  
Author(s):  
Nizar Abou Zaki ◽  
Ali Torabi Haghighi ◽  
Pekka M. Rossi ◽  
Mohammad J. Tourian ◽  
Bjørn Klove
Keyword(s):  
Groundwater Level ◽  
Water Mass ◽  
Groundwater Resources ◽  
Water Volume ◽  
Groundwater Depletion ◽  
Drought Period ◽  
Resources Management ◽  
Grace Data ◽  
Gravity Recovery

Abstract. The Bakhtegan catchment, an important agricultural region in south-western Iran, has suffered groundwater depletion in recent years. As groundwater is considered the main source of fresh water in the catchment, especially for agriculture, monitoring groundwater responses to irrigation is important. Gravity Recovery and Climate Experiment (GRACE) satellite data can help determine water mass changes in catchments and assess water volume changes, but have been under-used to date in water resources management. In this study, we compared GRACE-derived water mass data against groundwater volume variations measured in situ. We also assessed the efficiency of GRACE-derived data in catchments smaller than the 200,000 km2 recommended area when using GRACE. For the study period (January 2002 through December 2011), the GRACE data showed a 7.6 mm annual decline in groundwater level, with a total volume loss of 2.6 km3 during the period. The in situ monthly measurements of groundwater level showed an average depletion of 10 m in catchment aquifers during the study period. This depletion rate was supported by the recorded decrease in precipitation volume, especially in the post-drought period after 2007. These results demonstrate that GRACE can be useful in groundwater resources management of catchments facing groundwater depletion and increasing water demand.

Mahakam Field Characterization Using Production Type-Curve With Business Intelligence Application

2020 ◽  
Author(s):  
P. Noverri
Keyword(s):  
Business Intelligence ◽  
Data Gathering ◽  
Production Data ◽  
Type Curve ◽  
Type Curves ◽  
Production Type ◽  
Efficient Data ◽  
Production Behavior ◽  
Factor Type ◽  
Mahakam Delta

Delta Mahakam is a giant hydrocarbon block which is comprised two oil fields and five gas fields. The giant block has been considered mature after production for more than 40 years. More than 2,000 wells have been drilled to optimize hydrocarbon recovery. From those wells, a huge amount of production data is available and documented in a well-structured manner. Gaining insight from this data is highly beneficial to understand fields behavior and their characteristics. The fields production characterization is analyzed with Production Type-Curve method. In this case, type curves were generated from production data ratio such as CGR, WGR and GOR to field recovery factor. Type curve is considered as a simple approach to find patterns and capture a helicopter view from a huge volume of production data. Utilization of business intelligence enables efficient data gathering from different data sources, data preparation and data visualization through dashboards. Each dashboard provides a different perspective which consists of field view, zone view, sector view and POD view. Dashboards allow users to perform comprehensive analysis in describing production behavior. Production type-curve analysis through dashboards show that fields in the Mahakam Delta can be grouped based on their production behavior and effectively provide global field understanding Discovery of production key information from proposed methods can be used as reference for prospective and existing fields development in the Mahakam Delta. This paper demonstrates an example of production type-curve as a simple yet efficient method in characterizing field production behaviors which is realized by a Business Intelligent application

EVALUATING THE EFFECTIVENESS OF USING FINANCIAL RESOURCES OF MEDICAL ORGANIZATIONS

2020 ◽  
Vol 2 (7) ◽  
pp. 32-38
Author(s):  
S. S. BUDARIN ◽  
◽  
Yu. V. EL’BEK ◽  
V. O. VATOLIN ◽  
◽  
...  
Keyword(s):  
Resource Management ◽  
Medical Care ◽  
Healthcare Reform ◽  
Health Sector ◽  
Financial Resources ◽  
Medical Organization ◽  
Resources Management ◽  
Material Resources ◽  
Public Resources

In the context of the Moscow healthcare reform that has been carried out in recent years, the issues of evaluating the effectiveness of financing the healthcare system and the performance of medical organizations in providing medical care to the population are particularly relevant. Given the limited public resources allocated to the health sector, the quality of management of available financial, human and material resources is becoming more important. The article considers the application of the method of assessing the quality of resource management, introduced in Moscow since 2016, and its results in terms of evaluating the effectiveness of financial resources. It is revealed that the effectiveness of financial resources management is influenced by certain indicators that characterize the organization of management of the main activities of a medical organization.

Sign in / Sign up

Export Citation Format

Share Document