Miaojing Meng

Bio: Miaojing Meng is an academic researcher. The author has contributed to research in topics: Watershed & Watershed management. The author has co-authored 1 publications.

A Multi-Objective Decision Making System (MDMS) for a Small Agricultural Watershed Based on Meta-Heuristic Optimization Coupling Simulation

Abstract: [Background] The key to integrated watershed management is to take simultaneous account of environmental, economic, and social development goals; hence, a multi-objective decision making approach is required. However, our understanding and application of multi-objective decision making in watershed management remains limited. [Objective] The objective of this study was to develop a multi-objective decision making system (MDMS) that could simultaneously handle multiple problems and objectives in a small watershed based on the relationships among land, water and economy. [Methods] The MDMS was coupled with the watershed hydrological model and economic benefit evaluation model to comprehensively simulate the watershed operational process, and established a multi-objective function to minimize sediment, nitrogen, and phosphorus outputs, while maximizing the economic benefits for integrated watershed management. The MDMS also utilized an improved meta-heuristic algorithm to optimize the agricultural land use structure of the small watershed to obtain the best integrated management plan at the small watershed scale. [Results] We found that the MDMS achieved seamless connections between automatic updating, analysis, and the optimization of land use structures in the iterative process, and successfully obtained an optimal scheme from a large number of agricultural land use structure alternatives, with particularly high time efficiencies. [Conclusions] Overall, the MDMS effectively controlled the negative impacts of crop planting on the environment, and simultaneously considered the economic benefits, which might assist managers in arriving at efficient scientific decisions toward the integrated management of small agricultural watersheds.

A Developed Method of Water Pollution Control Based on Environmental Capacity and Environmental Flow in Luanhe River Basin

Abstract: To solve increasingly serious water pollution problems, it is necessary to systematically manage water resources, water environment, and water ecology as elements of a watershed. Comprehensive watershed water pollution control should regard the basin as a whole, respect the natural laws of the river and lake system, and focus on the protection and restoration of its natural ecological environment so that the comprehensive ecological service functions of rivers and lakes can be fully realized. Based on the concepts of environmental capacity (EC) and environmental flow (EF), this study established watershed water pollution control scheme prediction and evaluation methods to explore the changes in the water environment and water ecology in the basin under different water pollution control schemes. The MIKE11 model was used to construct a hydrologic and water quality model of the study area, the one-dimensional water quality model was used to calculate the water environmental capacity, and the Tennant method was used to evaluate the environmental flow. In this study, the method was applied to the Luanhe River Basin of Chengde, Hebei Province, China. It simulated the concentration changes of four pollutants—namely, NH3-N, COD, TN, and TP—under eight different water pollution control schemes, and the responses of EC and EF were compared and analyzed. Some conclusions are as follows: (1) Reducing point source pollution has the most obvious effect on water pollution prevention, especially on NH3-N and COD, while reducing nonpoint source pollution is weaker and the effect of increasing upstream water is the weakest. (2) The increase in up-stream water inflow and reducing point source pollution can greatly increase the EC of NH3-N and COD. The EC of TN can be greatly increased by reducing point source pollution, and the EC of TP can be greatly increased by reducing nonpoint source pollution. (3) The increase in upstream water inflow can improve the EF level to a certain extent. This method can also be applied to other similar river basins, providing valuable suggestions for rationally formulating water environmental management strategies and for promoting the sustainable development of the ecological environment and social economy in the river basin.

Large-scale Multi-objective Optimization for Water Quality in Chesapeake Bay Watershed

Abstract: The careful selection of Best Management Practices (BMPs) to reduce loading, such as nitrogen, phosphorus, and sediments, can substantially improve the water quality of water-sheds. This paper introduces the first implementation of a hybrid and customized evolutionary multi-objective (EMO) algorithm to improve the Chesapeake Bay Watershed's (CBW) water quality. To make the algorithm scalable, we inject a few solutions obtained using an integer programming algorithm (IPOPT) in the initial population of EMO. Also, a repair operator is applied to satisfy every equality constraint. Combining these approaches can find a set of non-dominated trade-off solutions from 1,012 variables (Tucker county in West Virginia) to a staggering 153,818 variable problem (the whole state of West Virginia). Furthermore, a pre-liminary analysis of obtained trade-off solutions finds interesting properties of BMP allocations, providing an optimistic picture of applying the proposed customized optimization algorithm in addressing other bigger states leading to the whole Chesapeake Bay watershed.

Large-scale Multi-objective Optimization for Water Quality in Chesapeake Bay Watershed

Abstract: The careful selection of Best Management Practices (BMPs) to reduce loading, such as nitrogen, phosphorus, and sediments, can substantially improve the water quality of water-sheds. This paper introduces the first implementation of a hybrid and customized evolutionary multi-objective (EMO) algorithm to improve the Chesapeake Bay Watershed's (CBW) water quality. To make the algorithm scalable, we inject a few solutions obtained using an integer programming algorithm (IPOPT) in the initial population of EMO. Also, a repair operator is applied to satisfy every equality constraint. Combining these approaches can find a set of non-dominated trade-off solutions from 1,012 variables (Tucker county in West Virginia) to a staggering 153,818 variable problem (the whole state of West Virginia). Furthermore, a pre-liminary analysis of obtained trade-off solutions finds interesting properties of BMP allocations, providing an optimistic picture of applying the proposed customized optimization algorithm in addressing other bigger states leading to the whole Chesapeake Bay watershed.

Optimization of the Decision-Making System for Advertising Strategies of Small Enterprises - Focusing on Company A

Abstract: In the post-COVID-19 era, the founding rates of micro enterprises and startups will increase due to the low youth employment rates and increased retirement of baby boomers. Therefore, the portion of small enterprises among all enterprises is expected to grow. The rapid change in consumption patterns due to the COVID-19 pandemic has accelerated the entry of small enterprises into the online advertising market. However, advertising costs spent in running the businesses are taking up a large portion of their sales budgets due to intense competition and various advertising platforms. This study examines the decision-making system to optimize advertising expenditures considering the difference in advertising costs depending on various media types and keywords based on limited advertising budgets for stable management of small enterprises. To this end, this study modeled the advertising system of small enterprise A (Company A) with system dynamics and used the Java-based simulation software AnyLogic. Through simulation modeling, we conducted optimization analysis of two scenarios, maximum buyers and minimum advertising costs, in the post-COVID-19 era. Based on the results, this study forecast the conditions for optimization of decision-making in each advertising platform.