Multiagent System and Rainfall-Runoff Model in Hydrological Problems: A Systematic Literature Review
2. Materials and Methods
2.3. SLR Methodology
- SQ 1.
- How were the MAS agents defined and what was the objective(s) of this model?
- SQ 2.
- What RRM used, and what was the objective(s) of this model?
- SQ 3.
- Were other techniques necessary to generate the study? If so, which ones and how were they used and related to the MAS and RRM tools?
- SQ 4.
- How did the work integrate the use of MAS and RRM techniques?
- SQ 5.
- Are the main challenges encountered related to the MAS and/or RRM modeling processes?
- QQ 1.
- Do the results satisfy the objectives of the study?
- QQ 2.
- Does the study present an analysis of the results obtained?
- QQ 3.
- Is the adopted methodology presented in a clear and detailed way?
- QQ 4.
- Does the study analyze the advantages and disadvantages of the methods used?
- QQ 5.
- Does the study present an analysis on the integration of MAS and RRM modeling?
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A.1. Answers to the Specific and Quality Questions of the Primary Studies
|SQ 1||The agents of the MAS are the families of farmers, whose interaction occurs through competition for land and wood. There is no interaction between agents, only between individuals and the environment. The model has a more direct interaction with the forest dynamics model, where families are established in a part of the region, and each individual can keep the cells free from trees and allocate a type of landuse, such as rice or corn. Agents consume timber and replant trees elsewhere in the region.|
|SQ 2||For the coupled model, the distributed RRM based on terrain of brasington et al.  was adopted. From the regular discretization of the DEM in cells of 20 m, a three-dimensional cell model was used. Precipitation is considered to be uniformly distributed throughout the region, and its annual time series is reproduced in a standard way, year after year in the model. Each cell receives information that serves as input for the hydrological model. It returns parameters such as hydraulic conductivity, canopy capacity of the vegetation, and the response of the calculations on the soil moisture flows, which is generated from a finite volume solver per a four-layer structure.|
|SQ 3||The use of a model based on individuals was used to represent the forest dynamics of the trees. Trees grow according to a dynamic competition model called SORTIE, which uses allometric relationships of the trees. It provides the availability of wood for farmers, which modifies the MAS. However, it has no direct interaction with the hydrological model.|
|SQ 4||From the results of the farmer and forestry systems, a land use map is generated and implemented in the RRM. This model operates independently and returns the hydrological components to the MAS. Land use define the amount of soil moisture in a spatially distributed manner, as well as flows and floods. Information on changes in canopy capacity and hydraulic conductivity is necessary to generate the coupling between MAS and RRM.|
|SQ 5||As mentioned earlier, the dynamics, the spatial and temporal scales, the processing time, and the validation between the sub-models and the coupled model are major challenges encountered. Separately, MAS and RRM do not present all of these challenges, as it is possible to generate modeling, parameter calibration, and validation without relying on data obtained by other systems. The integration between MAS and RRM, on the other hand, depends on analysis and planning to minimize the challenges.|
|QQ 1||The main objective was to develop small-scale dynamic models, to build a coupled model that represents the large-scale behavior of the forest, analyzing the main spatial interactions, and how spatial distributions interfere with the dynamism of the system. The results showed that although there are several challenges and possible improvements for the model, it describes the behavior of the system when relating the actions of the agents, the hydrological data, and the forest dynamics. However, it is understood that with faster processing and more interactions between models, the results would be better and more detailed.|
|QQ 2||In work, the results are analyzed through experiments, as the components of the models are changed. The analysis performed refers to the changes generated in the coupled model, without bringing a quantitative or qualitative analysis of the data obtained. This is explained in the conclusions of the work, where the authors emphasize that it is not possible to generate a sensitivity analysis of the model due to computational time, the large number of parameters that must be explored, and the non-linearity of the coupled system.|
|QQ 3||It can be seen that the work presents the methodology in relation to the developed models, and interactions, including an appendix that explains part of each submodel. However, previous publications are mentioned, in which the models are detailed, as well as applications and analyzes focused on the research in question. In addition, it is mentioned that topographical, rainfall and soil characteristics used were obtained through previous work.|
|QQ 4||Throughout the explanation of the models and their interactions, some advantages and disadvantages are commented on. However, the authors reflect on some possible and important changes that, even bringing challenges, can be made to generate more detailed and better results about the study, being one of the sections focused on this theme.|
|QQ 5||The integration of MAS and RRM is briefly analyzed in the results section and in the work appendix. As the insertion of agricultural agents in the model, some important changes occur in relation to soil moisture, due to the use of wood from the trees and the change in the landuse. In addition, with the increase in population, the greater the variation in the equilibrium of simulated flow components. Furthermore, it was observed that both runoff and evapotranspiration were highly sensitive to the increase in agriculture generated by farmers.|
|SQ 1||The MAS is a homogeneous hydrological simulation system, which serves to link the individual regional models in a single model of the basin. 18 sub-regions of the basin were defined as the system agents, modeled by the eWater Source river system. The behavior of each agent is determined as a nascent river model and is modified only through the calculated parameter values. Each agent is given a name or ID and must be represented by four status in different colors so that the simulation is easily understood. The status are: configured; running; done; and failed. The interactions of the agents are implemented from three processes related to the flow data between the regions, and to the types of interactions that can be of two forms: when the exits of a region A affect the entrance of another region B; and when the output of two regions A and B affect, respectively, the inputs of regions B and A.|
|SQ 2||Sacramento RRM of Burnash et al.  was used for the development of the work. The objective was to integrate RRM into the SMDBRM structure, to generate unmeasured flow from each sub-basin.|
|SQ 3||The work is based on the hydrological modeling eWater Source, which was used to develop the daily conceptual hydrological model SMDBRM with seven calibration parameters. The SMDBRM model provides the framework for the MAS, where the agents are the sub-regions of the basin. In addition, an automatic stepwise calibration procedure is used, where each sub-region is calibrated in a sequential order using the water balance equation. One of the calibrated values is generated from the Sacramento RRM. The optimized values of the calibration parameters are obtained before the simulations and updated in each region through an agent behavior controller. In addition, parallel programming techniques were used at three levels to decrease computational time, as follows: unconnected agents are run in parallel; agent connections are performed in parallel; and different simulations can be run in parallel.|
|SQ 4||The agents represent the regions and interact from the hydrological connectivity between the river systems. Each agent has a node-link structure project, based on the SMDBRM model. In the integrated modeling platform, the calibration script is embedded, including the calibration generated in RRM. Each agent has a behavior controller that updates the values already calibrated, before the simulation.|
|SQ 5||The authors do not comment on challenges when coupling MAS and RRM. The difficulties exposed throughout the work are related to what the model proposes to solve. Thus, there are no mentions of challenges related to MAS or RRM, only on specific issues of model calibration and validation.|
|QQ 1||The objective of the work was to develop a homogeneous river modeling platform based on agents to link the individual models of the basin regions. Despite the statistical analysis showing that some results are not satisfactory, the authors mention that it was possible to achieve the objectives, as the time and performance of the model were good in almost all regions. In addition, they claim that the tool presents consistent, explainable, and comparable results.|
|QQ 2||The execution time and performance of the model are analyzed in a section of the work. The calibration time was not mentioned, but after obtaining the values of the calibrated parameters, the model simulation had fast processing. The performance of the model was analyzed statistically, considering the steps of calibration and validation, proving to be highly satisfactory in most regions. In addition, the authors present a discussion on the modeling platform. However, possible improvements to the model or future work are not mentioned.|
|QQ 3||The work presents sections of explanation about the approached methodology and the interactions between the techniques. Howerver, it is based on existing models, the authors direct the methodology to the development of the MAS and the self-calibration process of the model. The structure of the agents, their interactions, and the way they are executed are explained in detail. Hydrological models are mentioned, including RRM, but are not explicitly presented.|
|QQ 4||The authors only mention the advantages of using MAS. Among them are the ease in executing the parallel computing processes; the possibility to model complex environmental systems; and simulate and analyze the nonlinear behavior of social and natural systems. In addition, they comment that the choice of RRM Sacramento was made through an analysis among six models, but did not address the advantages and disadvantages of the tools.|
|QQ 5||Integrations between MAS and RRM were not discussed. Integration was mentioned when explaining the relationship between agents and calibrated data from regional models.|
|SQ 1||The main agents defined were the raindrops, the soil, the altitude, and the amount of water. The main objective of the MAS was to simulate the processes of the rainfall-runoff system and soil erosion in the basin. For this, the open- source, multi-agent programmable modeling environment NetLogo was used. The environment was divided into cells, and in each one, a soil class was considered based on the DEM, which is resampled in NetLogo by the approach of the nearest neighbor. The amount of water used is programmed for each cell. Raindrops are generated according to the rainfall data. Depending on the corresponding altitude of each cell, the droplets accumulate in the region and become agents of the amount of water. The soil changes based on the effect of raindrops. The DEM is modified according to the variation of altitude and soil.|
|SQ 2||The final RRM is the tool based on OARES agents, which aims to simulate the rainfall-runoff interaction and soil erosion. The model provides data such as: total rainfall-runoff along with the regression adjustment; landuses total change; and soil erosion in image and color scales.|
|SQ 3||The Soil Conservation Service model was chosen to model and simulate the direct flow of the basin. Images from Landsat 8 tool were used to generate the landuse maps. To obtain information on the slope, the DEM ASTER was used. To be able to analyze the climate forecasting system, meteorological data from 36 years of the basin were obtained. These data and tools were coupled to generate the OARES platform. To validate the model, comparisons between OARES and conventional Hydrologic Modeling System (HEC-HMS) and SWAT models were performed.|
|SQ 4||The integration between MAS and RRM was generated from the development of the OARES tool. The simulation occurs from the integration of the Python, R, and NetLogo libraries. The environment is divided into cells, which receive input data prepared from meteorological, hydrological and DEM data. The agents move around the environment using adopted flow criteria, load a soil unit according to an algorithm, modify their amount of water depending on the conditions of the environment and elevation.|
|SQ 5||In order to generate data on flow and soil erosion, tools and equations were needed to generate the responses for the system. However, the results showed differences in the estimates of the variables. In addition, to use the DEM it was necessary to resample it on the NetLogo platform, which decreases the accuracy of the model.|
|QQ 1||The objective of the work was to explore the modeling of a MAS to analyze the rainfall-runoff processes and soil erosion in the Asan watershed. By analyzing the results, the OARES tool proved to be practical and lightweight. In addition to running in open source libraries, it can be run with large numbers of data.|
|QQ 2||The study performs a quantitative analysis of the output of the OARES model. More precisely, the maps of landuse, the rainfall-runoff relationship and estimated runoff, and soil erosion are analyzed. Moreover, in the work, the validity of the model is analyzed through comparison with conventional models. Additional simulations at OARES were performed to quantitatively measure the decay variations in the obtained precipitation data and the estimated runoff, and an analysis of the results was presented in an appendix.|
|QQ 3||The approached methodology is presented from three stages. The first details the preparation of the necessary data to serve as a parameter for the model. In the second part, the simulation of the model is exposed through the equations and methods used in OARES, where an appendix is mentioned to explain the interactions between the agents. However, it is mentioned that OARES integrates the Python, R, and NetLogo libraries, but the processes on each platform and how they were integrated are not explained. Finally, an analysis of the model is performed.|
|QQ 4||The authors emphasize the advantages of using the MAS and RRM tools but do not mention the disadvantages. They mention that the use of MAS in the hydrological context is, sometimes, compared with other computational methodologies. However, they claim that agent-based systems make it possible to use physically realistic parameters, without the need to consider a large amount of data. In addition, they can have a flexible programming module, and obtain computational time savings. Regarding RRM, the authors mention the advantages of using semi-distributed models, which generate satisfactory modeling of the hydrographic region and are less complex than approaches with distributed structure.|
|QQ 5||The developed MAS generated hydrological rainfall-runoff and soil erosion modeling over the basin. Thus, the analysis on the integration of MAS and RRM was executed based on the results of the agent-based model.|
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|Planning||Specification of the research question|
Development of the review protocol
|Execution||Primary studies identification|
Primary studies selection
Primary studies evaluation
|Research question||What were the processes and challenges when engaging MAS and RRM to solve hydrological problems?|
|Main objective||Through the SLR, determine and analyze papers published between 2001–2021, which used MAS and RRM in the context of hydrology.|
|Keywords||- Multiagent System;|
- Rainfall-Runoff Model;
- Hydrology or Hydrological.
|Inclusion criteria||The paper presented the keywords or their variations.|
|Exclusion criteria||- The paper is not in the searched databases;|
- The paper is not in English;
- The paper was not published between 2001 and 2021;
- The paper was not based on MAS or RRM to solve a problem in hydrology.
|Bibliographic Database Name||URL|
|ACM Digital Library||https://dl.acm.org/, accessed on 2 October 2021|
|ASCE Library||https://ascelibrary.org/, accessed on 2 October 2021|
|IEEE Xplore||https://ieeexplore.ieee.org/, accessed on 2 October 2021|
|ScienceDirect||https://sciencedirect.com/, accessed on 2 October 2021|
|Scopus (Elsevier)||https://scopus.com/, accessed on 2 October 2021|
|SpringerLink||ttps://link.springer.com/, accessed on 2 October 2021|
|(“multiagent system”||or||“multiagent systems”||or|
|“multiagents system”||or||“multiagents systems”||or|
|“multi-agent system”||or||“multi-agent systems”||or|
|“multi-agents system”||or||“multi-agents systems”||or|
|“multiagent simulation”||or||“multiagents simulation”||or|
|“multiagent simulations”||or||“multiagents simulations”||or|
|“multi-agent simulation”||or||“multi-agent simulations”||or|
|“multi-agents simulation”||or||“multi-agents simulations”||or|
|“multiagent based simulation”||or||“multi-agent based simulation”||or|
|“multiagents based simulation”||or||“multi-agents based simulation”||or|
|“multiagent based simulations”||or||“multi-agent based simulations”||or|
|“multiagents based simulations”||or||“multi-agents based simulations”||or|
|“multiagent-based simulation”||or||“multi-agent-based simulation”||or|
|“multiagents-based simulation”||or||“multi-agents-based simulation”||or|
|“multiagent-based simulations”||or||“multi-agent-based simulations”||or|
|“multiagents-based simulations”||or||“multi-agents-based simulations”)|
|(“rainfall runoff model”||or||“rainfall runoff models”||or|
|“rainfall runoff modeling”||or||“rainfall runoff modelings”||or|
|“rainfall runoff modelling”||or||“rainfall runoff modellings”||or|
|“rainfall-runoff model”||or||“rainfall-runoff models”||or|
|“rainfall-runoff modeling”||or||“rainfall-runoff modelings”||or|
|“rainfall-runoff modelling”||or||“rainfall-runoff modellings”)|
|Selection strategy||- Find works;|
- Remove duplicate works;
- Read and analyze the title and abstract of the works;
- Read and analyze the introduction of the works;
- Read the full papers.
|Data extraction||Objective results extraction.|
|Quality evaluation||Assist in data analysis and synthesis.|
|Data summary||- Answer the specific questions;|
- Answer the research question;
- Answer the quality assessment questions;
- Compare the works.
|Mike Bithell and James Brasington||Coupling agent-based models of subsistence farming with individual-based forest models and dynamic models of water distribution||Scopus|
|Ang Yang, Dushmanta Dutta, Jai Vaze, Shaun Kim and Geoff Podger||An integrated modelling framework for building a daily river system model for the Murray–Darling Basin, Australia||Scopus|
|Sayantan Majumdar, Shashwat Shukla and Abhisek Maiti||Open agent based runoff and erosion simulation (oares): a generic cross platform tool for spatio-temporal watershed monitoring using climate forecast system reanalysis weather data||Scopus|
|Strong Points||Research Points|
|Coupling of several different systems||Interaction between models|
|Analysis from different perspectives||Stability|
|Direct contribution between models||Coupling direction|
|Better visualization of processes||Processing|
|Reduced complexity due to the use of MAS||Validation|
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Leitzke, B.; Adamatti, D. Multiagent System and Rainfall-Runoff Model in Hydrological Problems: A Systematic Literature Review. Water 2021, 13, 3643. https://doi.org/10.3390/w13243643
Leitzke B, Adamatti D. Multiagent System and Rainfall-Runoff Model in Hydrological Problems: A Systematic Literature Review. Water. 2021; 13(24):3643. https://doi.org/10.3390/w13243643Chicago/Turabian Style
Leitzke, Bruna, and Diana Adamatti. 2021. "Multiagent System and Rainfall-Runoff Model in Hydrological Problems: A Systematic Literature Review" Water 13, no. 24: 3643. https://doi.org/10.3390/w13243643