- 340 - observed data to evaluate the model’s accuracy. This comparison helps in identifying any discrepancies and refining the model for better accuracy. The final step involves analysing the simulation results to draw conclusions about LISFLOOD’s effectiveness in flood risk management and its potential applications in other regions.The route map of the simulation and visualisation process is as illustrated in Figure 2 below: Findings The application of the LISFLOOD software to simulate the 2020 floods in Buscot and Tewkesbury yielded significant insights into the model's accuracy and effectiveness. The simulations closely matched the observed flood extents, validating the model's capability in predicting flood dynamics. Specifically, the predicted water levels and inundation areas were in strong agreement with the actual data recorded during the flood events, demonstrating LISFLOOD's reliability in capturing the spatial distribution of flooding. This alignment suggests that LISFLOOD can be a valuable tool for flood risk assessment and management in these regions. Further analysis of the simulation results revealed critical flood-prone areas that require focused mitigation efforts. In both Buscot and Tewkesbury, the simulations identified several low-lying regions and areas near riverbanks that were particularly vulnerable to flooding. These findings are essential for local authorities and policymakers, as they highlight the need for targeted infrastructure improvements and flood defense mechanisms in these high-risk zones. Additionally, the model's ability to simulate different flood scenarios provides a basis for developing comprehensive flood preparedness and response plans.
- 341 - The study also highlighted areas for potential improvement in the LISFLOOD model. While the overall accuracy was high, some discrepancies between the simulated and observed data were noted, particularly in regions with complex topography and land use patterns. These discrepancies underscore the importance of high-resolution data and suggest that further refinement of the model's parameters could enhance its predictive capabilities. Future research should focus on integrating more granular data and exploring the use of real-time inputs to improve the model's responsiveness to changing flood conditions. This ongoing refinement will ensure that LISFLOOD remains a robust and reliable tool for managing flood risks in the UK and beyond. Conclusion The evaluation of LISFLOOD software in simulating the 2020 floods in Buscot and Tewkesbury demonstrates its robust capabilities in accurately predicting flood extents and identifying critical flood-prone areas. The model's strong alignment with observed data underscores its reliability and potential as a valuable tool for flood risk management and mitigation strategies. While some discrepancies were noted, further refinement with higher resolution data and realtime inputs can enhance its accuracy and responsiveness. Overall, LISFLOOD's application in these regions supports its utility in enhancing flood preparedness, informing targeted interventions, and ultimately reducing the adverse impacts of flooding on communities. References Environmental Agency (2023). Flood and coastal erosion risk management report: 1 April 2020 to 31 March 2021. (Accessed: 2nd May 2023). Flood Assist Insurance (2020). Flood Alert Info- River Thames and tributaries from Buscot Wick down to Kings Lock. Available at: https://floodassist.co.uk/floodwarnings/flood-area-info/gloucestershire/061waf23bsctkngs/river-thames-andtributariesfrom-buscot-wick-down-to-kings-lock (Accessed: 26th April 2023). Horritt, M. S. and Bates, P. D. (2002). ‘Evaluation of 1D and 2D numerical models for predicting river flood inundation’, Journal of Hydrology, 268(1), pp. 87-99. doi: https://doi.org/10.1016/S0022-1694(02)00121-X. Husain., A. (2017). ‘Flood Modelling by using HEC-RAS’. Available at: http://www.ijettjournal.org/2017/volume-50/number1/IJETT-V50P201.pdf. Joint Research Centre (2022). LISFLOOD Model. Available at: https://ecjrc.github.io/lisflood-model/. Lhomme, J., et al. (2008). Recent development and application of a rapid flood spreading method Professor Paul Bates, et al. (2005). Use manual and technical note, Code release 2.6.2(Available at: https://www.bristol.ac.uk/medialibrary/sites/geography/migrated/documents/lisflood-manual-v5.9.6.pdf (Accessed: 10th November 2022). Tristan Cork (2020). 'Gloucestershire Flood of Christmas 2020', Gloucestershire Live. Available at: https://www.gloucestershirelive.co.uk/news/gloucester- 85 news/gallery/gloucestershire-flood-christmas-2020-pictures4832819 (Accessed: 13th November 2022).
- 342 - ID-74: Prediction of Land Cover Changes of Indramayu Regency and Its Relation to The Rebana Priority Area Development Plan Using The Cellullar Automata Method Ayubella Anggraini Leksono, S.T. 1 , Nurrohman Wijaya 2 1,2 School of Architecture, Planning and Policy Development (SAPPD), Bandung Institute of Technology, 40132, Bandung City, Indonesia [email protected] 1 , [email protected] 2 Highlight: This study predicts the land cover changes in Indramayu Regency by 2031 using the Cellular Automata (CA) method and to assess its relation to the Rebana Industrial Estate (KPI) development plan. Land cover maps were generated from Landsat satellite imagery using a hybrid classification method (supervised and unsupervised) and validated through field surveys. The CA method utilized an Artificial Neural Network algorithm under business-asusual and KPI development scenarios for 2026 and 2031. Key driving factors included distance to roads, settlements, coastlines, and population density. The results showed significant increases in built-up areas and decreases in agricultural land under all scenarios. The built-up area increased by 517.97% (business as usual), 683.08% (2026 KPI), and 786.02% (2031 KPI). Agricultural land decreased by 21.74%, 25.21%, and 29.96%, respectively. The KPI development could convert 61.86% of agricultural land and affect 36.14% of water bodies, posing flood risks. Effective policies are necessary to manage land conversion and optimize land use. The data and methods of this study can be used for the spatial planning and land conversion control in Indramayu Regency. Keywords: Land Cover Prediction, Cellular Automata, Indramayu Regency, Land for Sustainable Food Agriculture, Industrial Estate Introduction Increasing national rice production is a crucial commitment of the Indonesian government to ensure domestic food security and contribute to international food stability. Indramayu Regency, as one of the regions with the highest rice production in West Java, is facing rapid agricultural land conversion. The average land conversion rate reaches 13.9 hectares per year, threatening the availability of critical agricultural land for food production. This study aims to identify land cover changes in Indramayu Regency from 2011 to 2021 and predict land cover in 2031 based on existing scenarios. Additionally, the study analyzes the suitability of the predicted land cover with the spatial planning (RTRW) of Indramayu Regency 2021-2041, specifically for sustainable food agricultural land (LP2B) and important area conservation (KPI). The analysis includes the use of Landsat imagery for the years 2011, 2016, and 2021, and the Cellular Automata (CA) model with the MOLUSCE plugin in QGIS. The results of this study are expected to provide valuable insights for land management policies and efforts to preserve agricultural land in Indramayu Regency. Literature Review i. Dynamics of Land Cover Change and Driving Factors Allan et al. (2022), in the journal article "Driving Forces behind Land Use and Land Cover Change: A Systematic and Bibliometric Review," reviewed 1,541 articles on the driving factors of land cover change in urban areas over the past 10 years. They used keyword analysis from papers taken from the Scopus database and identified key driving factors grouped into three levels: economic and financial, policy and regulation, and transportation
- 343 - availability. The study results indicate that land cover change is largely determined by the interaction between these factors. With the urban population continuously increasing, a deep understanding of these driving factors is crucial for effective urban planning. ii. Cellular Automata Method Cellular Automata (CA) is a modeling technique used to simulate dynamic spatial and temporal processes, such as urban growth and land use change. CA models divide space into discrete cells, each representing a land cover type, and predict changes based on transition rules influenced by the state of neighboring cells. The core elements of CA include cells, states, neighborhoods, transition rules, and time-steps, which collectively enable the simulation of complex spatial patterns from simple local interactions. CA models, such as CA-Markov and hybrid CA-Neural Network models, are particularly effective for integrating spatial interactions and temporal dynamics, making them valuable for urban planning and environmental studies. CA's adaptability and ability to handle heterogeneous spatial data make it a robust tool for predicting future land cover scenarios and analyzing urban sprawl. Methodology The method used is mixed methods. This research methodology combines quantitative and qualitative research methods in a single research sequence (Creswell & Plano Clark, 2007). The study employs both primary and secondary data collection methods. Primary data includes land cover classification data from field research, observations, ground truth checks, and satellite imagery from Landsat 7 (2011) and Landsat 8 (2016 and 2021) processed with Google Earth Engine. Secondary data comprises official statistics from the Indramayu Regency Central Bureau of Statistics, open geospatial data from platforms like tanah.air.go.id, policy-related data from the Indramayu Regency Research and Development Agency, and shapefile data from the Indramayu Regency Public Works Department. The research methodology involves three stages: identifying land cover change patterns using Landsat data, predicting land cover changes with driving factors and scenarios using a Cellular Automata with Artificial Neural Network algorithm, and assessing the suitability of 2031 land cover for LP2B and KPI allocations through intersection analysis. Figure 1: Research Framework
- 344 - Finding This study presents several key findings regarding land use change patterns in Indramayu Regency from 2011 to 2021, land cover predictions for 2031, and the impact of land cover on LP2B and KPI areas: i. Land Use Change (2011-2021): From 2011 to 2016, built-up land increased from 6,584 hectares to 9,267 hectares (3.15% to 4.44% of the total area), while agricultural land decreased from 165,631 hectares to 140,446 hectares (79.32% to 67.26% of the total area). Between 2016 and 2021, built-up land decreased to 6,605 hectares (3.16% of the total area), and agricultural land increased to 143,123 hectares (68.54% of the total area). ii. Land Cover Prediction (2026 & 2031): The "business as usual" scenario predicts a continuous increase in built-up land and a decrease in agricultural land from 2026 to 2031. Built-up land is projected to rise from 6,604.82 hectares in 2021 to 33,775.91 hectares in 2026 and 40,817.53 hectares in 2031 (3.16% to 16.17% and 19.55% of the total area, respectively). Agricultural land is expected to increase slightly from 143,123.02 hectares in 2021 to 144,147.69 hectares in 2026, then decrease to 112,001.74 hectares in 2031 (68.54% to 69.03% and 53.64% of the total area, respectively). iii. Impact on LP2B and KPI Area: In the LP2B area, agricultural land is projected to decrease by 16.78% (business as usual), 10.6% (KPI 2026 scenario), and 24.73% (KPI 2031 scenario). In the KPI area, the construction is expected to cause a conversion of 12,283 hectares (61.87% of the total KPI area) or 9.76% of the total LP2B area. Additionally, 7,176 hectares (36.14% of the total KPI area) are located on water bodies, posing flood risks and threatening water resource sustainability. Figure 2: The Land Cover Condition of Indramayu Regency in 2021-2026-2031 (Source: Personal Anlaysisi from Remote Sensing, 2023)
- 345 - Conclusion The conclusion of this research is that the use of Cellular Automata-based land cover prediction models provides good accuracy in anticipating land cover changes in Indramayu Regency. It was found that there is an increase in built-up areas and a decrease in agricultural land during the period 2011-2016. The pattern of land cover change shows high dynamics, with significant increases in built-up areas and fluctuations in agricultural land from 2021-2031. The LP2B area has a similar pattern of change to the overall Indramayu region, while the KPI area experiences increases in both built-up and agricultural land. Policies for controlling land use conversion are needed to maintain a balance between development and environmental sustainability. The Cellular Automata model can be used to forecast future land changes and assist in the formulation of more effective and efficient land use policies. 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Facultyof BuiltEnvironment andSurveying MALAYSIAN INSTITUTE OF PLANNERS CO-ORGANISERS: 2024 1 INTERNATIONALURBANPLANNINGCONFERENCE ST iNUPC Advancing Inclusi on and Inno vati on f o r Sustainab le De v el o pment Planning e-Proceeding e-ISBN 978-629-99070-3-9