Hydrologic modelling supported by GIS and ML over the past decade has greatly enhanced forecast capabilities. Now, digital terrain models are utilised as primary input datasets for water resource modelling. As stated above, mathematical hydrological models have been in use for over a hundred years. Examples include Darcy’s Law that describes water flow through the soil (1856) and St. Venant equations that model open-channel flow (1871). The Digital Terrain Models emerged in the 1950s for use in geophysical sciences. By the mid-1960s, computer programs simulating surface water flow and transport came into use. With advances in technology, models for surface water quality and groundwater flow were developed in 1970s and those for analysing transport problems in the 1980s. In the 1990s, GIS was adopted as an important tool in hydrological modelling (Grover, 1999).
During an international conference that took place in Boulder, Colorado, on September 1991 (Djokic and Maidment, 1993), the unavailability of spatial datasets to support the integration of GIS and hydrology was identified as a research gap (Djokic and Maidment, 1993, Mallawaarachchi et al., 1996). In the subsequent ten years, North American and European researchers made important progress in this area. Their focus was on creating improved spatial hydrological models that exploit the visualisation capabilities GIS (Goodchild, 2003). Integrating the two areas was a critical part of the research agenda. Although different GIS visualisation approaches were utilised, the strategy adopted was considered appropriate (Sui et al., 1999). The application of integrated GIS hydrology models is dependent on the GIS-based visualisation method required (Sui and Maggio, 1999). However, little progress has been made in this area, and in part, this thesis seeks to redress some of the gaps discussed during the 1991 Boulder meeting.
For example, compared to other natural phenomena, applied hydrological modelling employs validated practices and approaches. However, differences in the integration of these models with GIS are observed among Australian jurisdictions. For example, in New South Wales, South Australia and Victoria, hydrological modelling using GIS has been applied in local land use planning, flood monitoring and natural resource management (Pourali et al., 2014, Mallawaarachchi et al., 1996, Chiew et al., 2008). Pourali et al. (2014b) developed GIS-based hydrological models that predict overland stormwater flow impact. Additionally, the Topographic Wetness Index has been utilised for improved flooding prediction and management locally (Pourali, 2014). This study created new spatial information for improving GIS and hydrological visualisation to ensure that urban flood risks are well managed.
Existing numerical hydraulic models, particularly 1D backwater modelling, are commonly applied in river engineering because they are easy to use and require less input data and computational resources than 2D or 3D models. However, 2D models can give detailed and accurate representation of floods in plains and channels (Pappenberger et al., 2005). In general, input data and computational requirements for 2D and 3D modelling is higher than for 1D models. They integrate expert opinions with flood inventory data on spatial distribution of soil physical and hydrological characteristics implemented in qualitative approaches like the Analytical Hierarchy Process (AHP) to predict the flooding risk.
Qualitative information based on expert opinion is, however, sometimes prone to bias (Lawal et al., 2012; Rahmati et al., 2016b). Thus, although such expertise and recommendations may be valid for local studies, they may not be applicable to assessing the risk of large-scale flooding events. Commonly used quantitative assessment approaches include multivariate statistical analysis (MSA) and bivariate statistical analysis (BSA). While BSA estimates the flooding impact of every category of conditioning factors, the MSA method establishes the effect of individual factors on stormwater inundation (Tehrany et al., 2014a). Examples of BSA and MSA methods include weight of evidence (WoE) and frequency ratio (FR) and logistic regression, respectively (Devkota et al., 2013).
GIS-based Decision Support Systems (DSSs) have been developed to provide real-time forecast information for decision-making. Decision-makers use the data for spatial analysis as well as to bolster their analytical and modelling capabilities. DSSs also provide useful spatial and hydrological information to flood risk managers. They also support learning, efficient hydrological visualisation capabilities and reporting (Sugumaran and Degroote, 2010). The DSSs are especially valuable tools in regions characterised as having a high flood risk.
Spatial Decision Support Systems (SDSSs) based on hydrological and pressure-driven models can be used in flood management (Rata et al., 2014). SDSSs are useful in groundwater management. They can be used to assess flooding risk and impacts of infiltration on aquifer water quality, level and salinity. The SDSS comprises three components. The first one is a geodatabase that stores and maintains data used by the system, an automated customised tool that implements the methodology and a graphical user interface (GUI) through which the user interacts with the system (Shneiderman, 2010).
The social, political and economic dimensions of a society can promote or constrain the development of national geospatial capabilities. A lack of policy and capacity-building programs is a major impediment to the growth of digital spatial information and related enabling architectures. Some of these development challenges have been noted in previous studies (Campbell and Masser, 1995; Cornelius and Medyckyj-Scott, 1991; de Man and van den Toorn, 2002; Obermeyer and Pinto, 2008; Pinto and Azad, 1994; Reeve and Petch, 1999; Schultz et al., 1987; Somers, 1998; Tomlinson, 2007). The spatial data that is required to support the Victorian Floodplain Management Strategy would help guide floodplain management in this region. It is based on the 1998 Victoria Flood Management Strategy that is aligned with the Victorian Government’s flood management proposals presented to the Victorian Floods Review and the Parliamentary Inquiry into flood mitigation infrastructure. It also draws on the broader emergency management framework contained in the 2013 Emergency Management Act. Notably, it incorporates the 2013 Victorian Waterway Management Strategy and the Victorian Coastal Strategy into the current floodplain management practice.
Presently, the geospatial information is integrated into flood management strategies for local urban agenda rather than the national context. However, public policy related to the development of sustainable processes in natural resource use recognises the need for a integrating infrastructures into a comprehensive flood risk management to support strategic priorities of governments. Framing policies in ways that exploit the power of evolving geospatial information and technologies in the area of urban climate change and flooding is therefore critical. Thus, research questions and objectives can be developed in this area to investigate the integrative role spatial data in floodplain management policy.
This study aimed to generate a land use planning approach for flood-risk-prone areas that is simple to execute, utilises minimal input data, does not require expensive and expert hydrodynamic models and can be completed by non-expert users.
Research Aim, Objectives and Questions
The professional obligations recognised in flood-risk land use planning encompasses managing land-use change and mapping flash flood-prone areas in order to design effective strategies and policies for long-term land use. The main objective of this research project was to develop geospatial data infrastructure to promote flood-risk-based land use planning in local areas prone to floods. Therefore, I sought to apply new information to support flooding risk assessment and decision-making.
An efficient tool in mapping flood-prone areas is the Light Detection and Ranging (LiDAR)-based surface topography data. In this study, I sought to develop an LiDAR data-based approach that offers a better prediction of flood risks in new areas than traditional land assessments do.
Studies recognise gaps among spatial industry, flood mitigation programmes and policy practice (Douglas et al., 2008). The main objective of this thesis is to describe current practices and challenges, assess the potential of GIS as a useful tool in developing effective hydrologic models and determine how GIS has transformed the hydrological field.
This study seeks to elucidate the utility of GIS in hydrology, especially in floodplain locations and design accurate tools for forecasting urban flooding risk. Two GIS applications are investigated for potential application in this field: Arc Hydro (ArcGIS Hydro Model) and the SWAT (Soil and Water Assessment Tool) based watershed model. A second aim of this study is to investigate flood level fluctuations because of climate change and the risk posed to local urban areas. Therefore, the general objective of this research is to design GIS-based hydrological tools that could inform policy within the flood-risk management strategy of local authorities. The main research question guiding this research is; in what ways can geospatial data and enabling infrastructure support decisions made by the Victorian flood management stakeholders for a sustainable floodplain management and flooding risk reduction? Five intermediate research questions and objectives derived from this issue are presented in the table below.
Table 1: Intermediate objectives and guiding questions.
|Intermediate Objectives||Intermediate Research Questions|
| ||RQ1. How well is the GIS integrated with hydrological modelling and geospatial data systems? |
RQ2. Is there a gap in the policy development and practice in data-driven sustainable floodplain management?
| ||RQ3. In what ways can a GIS-based hydrological model strengthen the current GIS database?|
| ||RQ4. What improvement opportunities exist for the present drainage analysis approaches? |
RQ5. In what ways can spatial decision-support system determine flood-prone infrastructure?
|4. To identify and analyse the factors that contribute to flooding in flood-prone locations using ML methods.||RQ6. How can GIS tools, LiDER and ML help identify factors that increase the risk of flooding in flood-prone areas?|
Justification for Research
The study’s scope is the relationship between GIS and hydrology. The purpose of the research is to review the rationale for the GIS-hydrological model linkage (Grover, 1999). Its primary goal is to determine the most appropriate hydrological model for GIS systems.
Latest developments in GIS with potential applications in hydrological modelling will be examined in this study. One such technology is LiDAR, which is useful in source of topographical data. LiDAR-derived DEMs offer quality dataset with enhanced capability that can be applied in hydrology. Complete three-dimensional terrain outline can be generated from LiDAR. A summary of the justification for this study is provided below.
- GIS-based hydrology modelling would support better flood monitoring and alerts. It will also facilitate the development of risk information specific to local flood-prone areas for decision-makers to use in monitoring, planning, and managing flooding.
- The GIS domain lacks geospatial information exchange and visualisation, which are powerful tools for managing flood risks effectively. The mapping tools and methods proposed for visualising flooding impacts by other researchers (for example, Yang, 2016; Marcy et al., 2011; Lathrop et al., 2014) do provide for data sharing as this research does.
- A GIS structure is required in existing numerical models that are developed based on hydrodynamic-morphological models or coupled-framework. However, interoperability among these systems is limited. Data on potential flood impacts needs to guide land-use planning by policymakers. Thus, pre-disasater models that simulate the possible effects of floods on infrastructure are required.
- Available technologies can provide better outcomes for end-users. ArcHydro, ArcSWAT, ML methods, screening strategies that simulate flood hazards and land-use change can be used to assess flood impact for effective decision-making. Software developers should focus on creating flexible GIS extension to support improved modelling of future flooding.
Developed flood models aid in the prediction of flooding patterns during storms, particularly in urban settings, as simulated by a DEM tend to be constant over time. Hence, this study elucidates five models of flooding:
- The use of arc hydro tools to define legal points of discharge on both impervious and pervious surfaces, overland flow path, and the likelihood of flooding in minor storms
- In the application of a scale of SWAT creek-basin in the measurement of the magnitude of the influence of flood management practices on intricate and simple watersheds.
- The utilization of extreme monitoring of rainfall occurrences in the evaluation of the susceptibility of roads and buildings in risky areas.
- Mitigation of flood risk using the modelling strategy of land use scenario.
- The application of GIS, machine learning, and remote sensing as a connection flash-flood potential and land-use changes
In this case, the study submits that the integration machine learning with GIS linked to hydrological model in predicting urban foods provides an effective strategy of informing response interventions by government. Evidently, Victoria’s flood risk management is not effective because provides limited information and knowledge, complicating the work of policymakers and scientists in addressing flooding activities (Cliquet et al., 2008). Thus, the study recommends that enhanced provision of information aid the management of flood by promoting collaboration required in urban areas. In this view, the developed flood models help in boosting the effectiveness of present strategies and tools that management employ in making informed decisions.
In conclusion, policy involved in flood management has limitations regarding the method of decision-support and the adoption of digital spatial information. Additionally, a policy gap exists in flood management because catchment authorities control the impacts of land use alterations, which occur on rivers, streams, and riparian areas. Environmental management is a complicated process because it entails the use of decision-support. Practically, some of the major issues stem from the inability to share, integrate, and adopt effective spatial information approaches.