Integrated Decision Support System for Flood Disaster Management with Sustainable Implementation

Emergency management of a flood catastrophe should involve not only an immediate reaction to the flooding but also the pre-flood disaster phase, the flood disaster phase, and the post-flood disaster phase. This is in addition to the immediate response to the flooding. This research investigates whether or not a decision support system (DSS) for flood emergency management is necessary and whether or not it is feasible. Previously, we presented a model for the development of modular disaster management decision support systems. The prior work that we did on the integration of DSS model decomposition-derived modular subroutines is expanded upon in this research. The act of merging many models into a single comprehensive logical model is referred to as “Observed and Forecasted.” After that, it offers a systematic framework for handling crises throughout the whole of the process, and it concludes with an in-depth analysis of the function architecture of each application subsystem. In this work, we create a disaster management metamodel with the goal of eventually building a disaster management (DM) language. It will function as a representational layer of DM data that, over time, will create a system for DM decision support that mixes and matches various DM actions in line with the evolution of the disaster as it develops. A decision support system that will contain an innovative metamodel as a core component will be developed in order to accomplish the aims of integrating, enabling, and accelerating access to DM information. These objectives will be reached via the creation of a decision support system.


Introduction
Flooding is both one of the most prevalent and one of the most hazardous natural disasters.It has swiftly gone to the top of the list of natural disasters that India experiences in terms of frequency, intensity, and the amount of damage that it does.It is generally agreed that flooding poses a significant threat not just to people's lives and property but also to the growth of the national economy, social order, and the political system.The growth of both the population and the property values in areas that are prone to flooding is one of the factors that is leading to an increase in losses caused by flooding.Since the early 1980s, the amount of damage caused by floods has been continuously growing, going from tens of billions on average to over 200 billion in 1998.This trend began in the early 1980s.The most recent statistics from the relevant departments show that there were 30 instances of floods brought on by basin rainstorms in the last five years.As a result of these floods, there was an average death toll of 1,583 per event, direct economic losses of 102.7 billion annually, and an average number of 159 million flood disaster victims.In addition, these floods resulted in direct economic losses of 102.7 billion annually [1].
In light of these facts, the rescue and relief activities for flood catastrophes, in addition to the battle against flooding in nations where floods are a common occurrence, get a significant amount of attention.In recent decades, our country has also carried out substantial river control, constructed a broad variety of flood disaster works, and established a system of flood-prevention projects, all of which have enhanced flood-prevention capability and reduced flood damage.In addition, processes for flood prevention projects have been developed.Many views about the relevance of the work to construct flood protection systems crossed people's minds as they battled against floods.However, the flood protection initiative has certain downsides, such as passivity and disinterest.It is favourable for problem analysis with speed, ease, and perceptual intuition to use the DSS for flood disaster management because it gives multilayer information that is very accurate for decision supporter analysis and it delivers this information [2].
In order to effectively reduce the amount of damage caused by the flood, it is necessary to implement both a scientific method for making decisions and an essential component that is dictated by the system engineering of flood management.It is essential to put this system into action in order to safeguard people's lives and property from the devastating effects of flooding, which can be prevented by promoting the sustainable use of our country's water resources and reducing the risk of such disasters occurring.One of the most crucial topics is model management, which is generally acknowledged as a crucial element of decision support systems.Model integration, which entails merging already-existing DSS models and components, is one of the main responsibilities of model management.It includes activities like linking models, creating composite models from pre-existing models, gathering models, reuse of models, and analysis of models.The current research focuses on reducing the consequences of severe events like more frequent and heavy rainfall, despite the fact that climate change increases a number of urban vulnerabilities.These occurrences cause urban drainage to increase and natural water bodies to overflow [3].
Flooding has several negative repercussions on civilization, including serious infrastructure damage, job losses, and limited access to basics such as clean water, electricity, and transportation.Flooding occurs as a result of excessive urban runoff contaminating natural water sources, and it has disastrous consequences for ecosystems.When it comes to climate adaptation, flash flooding, which happens when rainfall exceeds the capacity of drainage systems in metropolitan areas, is a major issue for much of Europe and many other countries across the globe.The modular approach to designing decision support systems is especially emphasised in order to facilitate efficient decision-making in crisis management while only keeping flexibility.In previous work, we proposed modularity as a viable solution to difficulties associated with the development of disaster management decision support systems.This system's design has most likely showed the greatest promise since it integrates a number of technical and theoretical features, including model decomposition approaches that emphasize modularity and model reusability [4].
A variety of DSS systems have been created for various types of catastrophes; they are based on specialized models and decision support criteria.A single model cannot meet all of the decision support needs that occur in catastrophe management.Adopting "combined" models offers many benefits and boosts efficacy in dynamic post-apocalyptic scenarios.The integrated model will be made feasible by the usage of modular subroutines designed expressly for catastrophe management.Catastrophic risk is characterized as the outcome of vulnerability, exposure, and possible threats.This danger may be reduced by focusing on one of the components or modifying the urban environment via an integrated strategy [5].

The subsystem of pre-flood disaster preparation
The pre-flood disaster subsystem's structural design is shown in Figure 3, which may be found here.
x This is accomplished by examining the flood's natural features, such as its range, depth, and duration, as well as the socioeconomic situation in the flood zones.In addition, the pre-flood damage assessment also takes into account the current state of the environment.x The formulation and ongoing administration of the emergency plan: The contingency plan is a crucial component of the emerging DSS.In order to be ready for any flood that may occur in the future, we need to devise in advance a strategy for emergency rescue and disaster relief and utilise this strategy as a guide for how to deal with flood-related problems.
x The three components of the emergency plan are the overall plan, the tailored plan, and the on-thespot plan.In the event of a flood disaster, having a strategy in place for training disaster management personnel is critical.Flood emergency exercises are required on a regular basis to acquaint disaster rescue workers with the flood emergency plan and the functioning of the disaster relief information system.The earliest possible date for these duties should be selected.x The administration of flood control reserves, which includes calculating the total amount of flood control reserves in the case of a flood and providing the capacity to query and dispatch data for all sorts of reserves in order to respond to emergencies.

Figure 1:
The structural design of the pre-flood disaster preparatory subsystem

Review Of Literature
The pre-flood disaster phase, flood disaster phase, and post-flood disaster phase should all be included in the emergency management of a flood tragedy.The feasibility and need of a decision support system (DSS) for flood emergency management are investigated in this study.It then presents a systematic framework for emergency management of the whole process, and it concludes by providing specifics on the function design of each application subsystem [6].Software model engineers often make use of a language that is designed for a variety of purposes, such as the Unified Modelling Language (UML), while developing their domain application models.However, in the event that they discover that their models do not precisely meet the modeling criteria as they would like, a more specialized domain modeling language provides a better alternative option [7].Within the scope of this research, we create a disaster management (DM) metamodel in order to construct a disaster management (DM) language.It will function as a representational layer of DM information that, in the long run, will lead to a DM decision support system that mixes and matches a variety of DM actions in line with the ongoing disaster.An innovative metamodel will be included as a fundamental component into a decision support system that is going to be developed in order to achieve the goals of unifying, facilitating, and accelerating access to DM information [8].
Model integration has received a lot of attention from academics as one of the most important aspects of Decision Support System (DSS) model management.Independent DSS models now meet certain decisionmaking requirements in disaster management systems, but integrating these models provides a variety of advantages.As a consequence, selecting and integrating appropriate models is critical.The prior work that we did on the integration of DSS model decomposition-derived modular subroutines is expanded upon in this publication.The act of merging many models into a single comprehensive logical model is referred to as "model fusion [9].
During the course of the last decade, research has been conducted on the importance of decision support in emergency management, and a number of different approaches have been suggested.As a direct outcome of the study, many frameworks that provide decision-making assistance in the realm of emergency management have been developed.The user of a decision support system (DSS) may have a variety of needs and requirements for decision aid, which drives the creation of a new model.Consequently, since there are many different needs for decision assistance, a DSS model is being created.Because a single application may have a range of requirements, users may want a selection of models to fulfill their requirements and criteria for decision help.We designed a modular architecture that, depending on demands (such as environmental and catastrophe dependence), creates a variety of subroutine organization kinds [10].
Despite a supporting legal framework and a growing body of positive information from several research, there are presently few modern spatial planning techniques that include climate change.This is true even after the usefulness of the procedures has been proved.This is especially true for more localized planning approaches that influence how individual lots, buildings, and other pieces of property are utilized in metropolitan settings.Although climate scenarios are considered when developing large-scale spatial plans, this method is much too broad to be applied when developing smaller-scale plans.As a consequence, comprehensive land-unit development plans often disregard weather considerations or address issues at the planned site [11].Given that land unit planning decisions may have a significant influence on urban space throughout the watershed, it is obvious that this strategy is insufficient to increase urban resilience to pluvial floods.Both the amount and quality of storm water flow may be used to examine how development decisions have impacted the watershed.Because of the relationship between flow volume and pollutant discharge, flooding has the effect of hastening the rate at which contaminants infiltrate surrounding water bodies.watery bodies that move.Flood resilience planning would be viable if planning moved from fragmented, site-based planning to integrated, catchment-based planning.
Various strategies have been employed in the past to attain this goal, including object-oriented, relational, graph-based, knowledge base, and structured modelling, to name a few.Numerous solutions are employed in the literature to promote climate resilience-focused urban governance, demonstrating that organizational structures and processes may be effective in a wide range of configurations.The key to resilience is to integrate several forms of development, such as social, technical, economic, and political progress.Planning support systems (PSS) may enhance spatial data analysis and increase the resilience of urban design to stormwater by better characterizing risk zones.A prototype was used to improve our method even more.We were able to demonstrate the effectiveness of our plan by fabricating a bogus emergency to use in our presentation.It was studied if the proposed technique to creating an integrated model might be utilized to reduce the complexity and inefficiency of maintaining many DSS models for disaster management.The purpose of this research was to determine whether or not this strategy could offer an integrated model.Sustainability refers to a development strategy's ability to be sustained indefinitely for the benefit of both present and future generations.The technique for establishing the value, relevance, or standing of a piece of work, a course of action, or a general or specialized line of development activity is another component of the sustainability evaluation.This phrase refers to a broad variety of expert evaluations of the economic, environmental, and social implications of growth and sustainability.Progress toward (or away from) previously selected objectives or criteria based on approved sustainable development principles may be used to describe the elements that determine how sustainable a development plan is.These criteria take into account both positive and negative progress toward the goals or standards.Researchers and urban planners have been interested in the approaches used to assess the long-term sustainability of development projects in order to better understand how they might measure the degree to which development initiatives contribute to sustainable development.An integrated decision-making process may be utilized at multiple policymaking levels, including the micro-level integration used for individual projects, the macro-level integration used for policies and programs, and the macro-level integration used at the national level.A variety of techniques, including an accounting-based approach (GPI), a narrative review, and indicatorbased evaluations, have been developed in recent years with the goal of measuring sustainability on a macro or national scale.

Overall Structural Design of Disaster Management of Flood Disaster
The emergency management DSS of a flood catastrophe can be broken down into three tiers based on user requirements for flood control and network decision-making as well as knowledge, a management system, and the current workflow acquired through years of flood control and disaster relief operations in various locations.The requirements of users in terms of network and flood-control decision-making make this a feasible aim.This is done in order to accommodate the requirements of the users with regard to the decisionmaking process for the network and flood control.With the assistance of the data and models that are supplied by the data layer, the work of successfully completing the task of disposing of business and evaluating judgements for all different kinds of flood control is effectively finished.In the end, the manmachine interface of the express layer is what allows it to communicate with the decision analyser and the policymaker.Figure 1 depicts the general structural design of the disaster support system (DSS) applicable to flood disasters for both the strain module and the rebuilding module.This design may be seen in Figure 1.Four subsystems make up the DSS for flood disaster: the system for data collection, the system for planning and management, the system for forecasting the river regime, and the system for calculating the damage brought on by Li Jian's floods.Each of these subsystems is described in more detail below.The DSS is made up of all of these individual parts working together.In terms of flood management, Chi Tianhe held the belief that the DSS need to have measures that take into consideration both the degree to which each level is relatively independent from the others and the interactions that take place between the levels.The system's application layer is comprised of four powerful subsystems: one for flood disaster prevention, one for flood emergency response, one for post-flood disaster reconstruction, and one for flood emergency response.All of these components work together to ensure that the system's application layer runs smoothly.Flood emergency scenarios are handled in the same order as other emergency situations, following the basic pattern of the approach.This technique is made up of four interrelated subsystems.To avoid a flood catastrophe, an interface between the subsystems of rebuilding after a flood disaster and flood disaster prevention will be constructed simultaneously on the new foundation of the reconstruction scenario.This will be done to prevent a catastrophic flood.This will be done in order to avert an unthinkable flood.

Refinement of DM Metamodel: Bushfire Disaster Case Study
In order to explain and validate the semantics of our metamodel ideas, we incorporate the concepts that were discussed in the scenario of the recent awful wildfire that occurred in Marysville (Victoria, Australia), which is given in Table 1.This scenario describes the recent horrible wildfire that occurred in Marysville.It serves as an example of how the concepts that were addressed in our DM metamodel might be developed further in a specific catastrophic domain.The significant advancement that is shown in Figure 2 demonstrates how our metamodel is unique in comparison to any specific catastrophic metamodel.It may be used to the production of a hypothetical model of any one of a number of different disasters.one of the models that may be created by the use of the DM metamodel as a resource.According to this wildfire model, the high temperature (47 degrees Celsius), low humidity (less than 6%), strong wind from the northwest (an average speed of 100 kilometers per hour), and an unusually low fuel moisture content in the area's bushland were among the many factors that contributed to the disaster that occurred in Marysville.The wind came from the direction of 100 kilometers per hour, and the average speed was 100 kilometers per hour.All of these factors contributed to the blaze that ultimately caused the tragedy that occurred.While developing our DM metamodel, we took a number of different factors into consideration, one of which was the gravity factor.These combinations are all shown as examples to illustrate these notions.Alterations to the global climate, on the other hand, are an illustration of a component that contributes to a rise in the level of complexity in this scenario.As a direct consequence of this, a combination of rules pertaining to the Gravity Factor and the Complexity Factor will have an effect on the causes that cause wildfires.A concept known as the "Bushfire Factor" is one that is employed in conjunction with this method to bring about the disaster.

Research Methodology
There is evidence in the empirical data that demonstrates a dynamic relationship between two subroutines of the subschema-1 (see Table 2).(A) demonstrates that there is an inverse relationship between runoff and retention up to the month of December.The retention climbed by a large margin between the months of December and January, while the runoff rose by just a marginal amount.After that point, both the runoff and the retention begin to decrease.Up to December, (B) anticipates further events along these lines.After that point, the slope of the projected retention and the slope of the actual retention diverge by just a little amount.The months of February and March saw an increase in both the retention rate as well as the runoff rate.According to the results of the empirical research (which can be found in Table 3), even a little extension of the amount of time it rained greatly increased the penetration rate.The empirical data demonstrate that there is a dynamic interaction taking place between two subroutines of the subschema-2.Along the same lines as what was witnessed, the forecast indicates both a progressive increase and a drop.

Analysis and Interpretation
A two-factor analysis of variance with measurement repetition was carried out in order to ascertain whether or not there was a statistically significant difference between the groups of the first factor, "Sep-04, Oct-04, Nov-04, Dec-04, Jan-05, Feb-05, and Mar-05" (repeated measures), in relation to the dependent variable.
A statistically significant difference between the groups in the second component Forecasted Subschema-1, in which the dependent variable is the focus of attention.
In terms of the dependent variable, there is a significant interaction between the two components "Sep-04, Oct-04, Nov-04, Dec-04, Jan-05, Feb-05 and Mar-05" and Forecasted Subschema-1.The findings of the two-factor analysis of variance with repeated measurements indicated a Significant difference between the groups of the first factor, "Sep-04, Oct-04, Nov-04, Dec-04, Jan-05, Feb-05, and Mar-05" in connection to the dependent variable, p=aN.Forecasted Subschema-1 interacts with " Sep-04, Oct-04, Nov-04, Dec-04, Jan-05, Feb-05, and Mar-05 " in relation to the dependent variable, p=aN.The coefficient for the Oct-04 variable is negative, coming in with a value of -7.73.This suggests that a higher value for Oct-04 is associated with a greater possibility that "Mean Retention" is the dependent variable.According to the p-value, the significance of this influence may be inferred from the data.
According to the odds ratio, there is a times increase in likelihood for the dependent variable "Mean Retention" for every unit increase in the variable Oct-04.This means that the chance of "Mean Retention" will grow by a total of times.
The value b = 8.23 represents a positive coefficient for the variable Nov-04.This suggests that an increase in the value of Nov-04 is connected to an increase in the chance that the dependent variable, "Mean Retention," will take place.According to the p-value, the significance of this influence may be inferred from the data.According to the odds ratio, there is a times increase in probability for the dependent variable "Mean Retention" for every unit increase in the variable Nov-04.This means that the likelihood of the dependent variable "Mean Retention" will grow by a total of times.
The coefficient for the Dec-04 variable is negative, coming in at b = -8.22.This suggests that a higher value for December 2004 is associated with a greater possibility that "Mean Retention" is the dependent variable.According to the p-value, the significance of this influence may be inferred from the data.According to the odds ratio, there is a times increase in likelihood for the dependent variable "Mean Retention" for every unit increase in the variable Dec-04.This means that the chance of "Mean Retention" will grow by a total of times.
According to the p-value, the significance of this influence may be inferred from the data.According to the odds ratio, there is a times increase in likelihood for the dependent variable "Mean Retention" for every unit increase in the variable Mar-05.This means that the chance of "Mean Retention" will grow by a total of times.

Conclusion
The flood disaster decision support system's structure is made up of three layers: the expression layer, the application layer, and the data layer.These three layers are each referred to by their own names.The foundation of this approach is how the emergency situation is handled as a whole.In the case of catastrophic flooding, this may enable the auxiliary decision-making system to leverage network technology, information collecting technology, and flood Observed and Forecasted in Subschema.The integrated system would be in charge of a wide range of important tasks in order to improve the capabilities of flood catastrophe pre-warning monitoring, damage assessment, and emergency treatment.To aid in decisionmaking for emergency management.A particular catastrophic scenario is used as the basis for the model's creation.Using a made-up crisis scenario, we were able to demonstrate the structure and applicability of the proposed integrated model as well as the essential components of the framework.An intelligent method is utilized to choose the set of subroutines that will be used to build the integrated model.
With the aim of making the challenging decision-making process that takes place within the framework of crisis management more manageable, this technique permits the selection of modular subroutines to be used in the creation of a dynamic model.Additionally, to enable the dynamic connection that is present in IOP Publishing doi:10.1088/1755-1315/1285/1/01201511 the integrated model, we advised developing a domain foundation and constructing an integrated model utilizing the ER approach.Both of these recommendations may be found in the earlier section.The simulations were shown in the part before that.The simulations provide the decision-makers with a means to evaluate and forecast the circumstances around the present choice.Among these responsibilities was the management of hydrologic data in real time, the monitoring of potential flood disasters and the anticipation of such events, the dispatching of flood disaster response teams, and the evacuation of individuals.It is possible that the disaster preparedness and response system (DSS) for flooding might offer an essential and reliable basis for determining the possibility of flooding and formulating a strategy for dealing with it.This demonstrates that there is potentially scope for further research and use in the future.

Figure 2 :
Figure 2: The general structural architecture of the emergency management system for flood and catastrophic situations.

Figure 3 :
Figure 3: The Results of Statistical Analysis on Forecasted Subschema-1

Table 1 :
A refinement of DM metamodel concepts to a specific-domain disaster

Table 2 :
The Results of Statistical Analysis on Subschema-1

Table 3 :
The Results of Statistical Analysis on Subschema-2