Towards managing catchment water utilisation for the Lake St Lucia ecosystem

1995 ◽  
Vol 32 (5-6) ◽  
pp. 95-101
Author(s):  
D. R. Weston ◽  
G. Quibell ◽  
W. V. Pitman

Lake St Lucia is one of Africa's largest coastal lakes, and is one of South Africa's most important wetland ecosystems. However, like many ecosystems, it is under threat from increased demands for water in its catchment area. Reduced runoff primarily impacts on the salinity regime of the lake. At low lake levels there is a net inflow of seawater to the lake. Evaporation concentrates the salts, and lake salinities rise to several times that of seawater. In water periods, lake levels rise and there is a net outflow toward the sea. Under these conditions salinities are low. The lake therefore naturally experiences a range of salinities associated with wet and dry cycles. Increased water use in the catchment will, however, impact on this natural salinity regime. This paper introduces a suite of models which will be used to facilitate management of the water resources of the catchment. The preliminary work presented demonstrates the ability of the models to provide meaningful input into a catchment management decision-support system.

2020 ◽  
Vol 89 ◽  
pp. 20-29
Author(s):  
Sh. K. Kadiev ◽  
◽  
R. Sh. Khabibulin ◽  
P. P. Godlevskiy ◽  
V. L. Semikov ◽  
...  

Introduction. An overview of research in the field of classification as a method of machine learning is given. Articles containing mathematical models and algorithms for classification were selected. The use of classification in intelligent management decision support systems in various subject areas is also relevant. Goal and objectives. The purpose of the study is to analyze papers on the classification as a machine learning method. To achieve the objective, it is necessary to solve the following tasks: 1) to identify the most used classification methods in machine learning; 2) to highlight the advantages and disadvantages of each of the selected methods; 3) to analyze the possibility of using classification methods in intelligent systems to support management decisions to solve issues of forecasting, prevention and elimination of emergencies. Methods. To obtain the results, general scientific and special methods of scientific knowledge were used - analysis, synthesis, generalization, as well as the classification method. Results and discussion thereof. According to the results of the analysis, studies with a mathematical formulation and the availability of software developments were identified. The issues of classification in the implementation of machine learning in the development of intelligent decision support systems are considered. Conclusion. The analysis revealed that enough algorithms were used to perform the classification while sorting the acquired knowledge within the subject area. The implementation of an accurate classification is one of the fundamental problems in the development of management decision support systems, including for fire and emergency prevention and response. Timely and effective decision by officials of operational shifts for the disaster management is also relevant. Key words: decision support, analysis, classification, machine learning, algorithm, mathematical models.


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