Simulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata

Accurate simulations and predictions of urban expansion are critical to manage urbanization and explicitly address the spatiotemporal trends and distributions of urban expansion. Cellular Automata integrated Markov Chain (CA-MC) is one of the most frequently used models for this purpose. However, the urban suitability index (USI) map produced from the conventional CA-MC is either affected by human bias or cannot accurately reflect the possible nonlinear relations between driving factors and urban expansion. To overcome these limitations, a machine learning model (Artificial Neural Network, ANN) was integrated with CA-MC instead of the commonly used Analytical Hierarchy Process (AHP) and Logistic Regression (LR) CA-MC models. The ANN was optimized to create the USI map and then integrated with CA-MC to spatially allocate urban expansion cells. The validated results of kappa and fuzzy kappa simulation indicate that ANN-CA-MC outperformed other variously coupled CA-MC modelling approaches. Based on the ANN-CA-MC model, the urban area in South Auckland is predicted to expand to 1340.55 ha in 2026 at the expense of non-urban areas, mostly grassland and open-bare land. Most of the future expansion will take place within the planned new urban growth zone.</p

Urban expansion in Auckland, New Zealand: a GIS simulation via an intelligent self-adapting multiscale agent-based model

Abstract: When modelling urban expansion dynamics, cellular automata models focus mostly on the physical environments and cell neighbours, but ignore the ‘human’ aspect of the allocation of urban expansion cells. This limitation is overcome here using an intelligent self-adapting multiscale agent-based model. To simulate the urban expansion of Auckland, New Zealand, a total of 15 urban expansion drivers/constraints were considered over two periods (2000–2005, 2005–2010). The modelling takes into consideration both a macro-scale agent (government) and micro-scale agents (residents of three income levels), and their multi-level interactions. In order to achieve reliable simulation results, ABM was coupled with an artificial neural network to reveal the learning process and heterogeneity of the multi-sub-residential agents. The ANN-ABM accurately simulated the urban expansion of Auckland at both the global and local scales, with kappa simulation value at 0.48 and 0.55, respectively. The validated simulation result shows that the intelligent and self-adapting ANN-ABM approach is more accurate than an ABM with a general type of agent model (kappa simulation = 0.42) at the global scale, and more accurate than an ANN-based CA model (kappa simulation = 0.47) at the local scale. Simulation inaccuracy stems mostly from the outdated master land use plan.</p

DataSheet_1_A regional analysis of tide-surge interactions during extreme water levels in complex coastal systems of Aotearoa New Zealand.zip

Tide-surge interaction (TSI) is a critical factor in assessing flooding in shallow coastal systems, particularly in estuaries and harbours. Non-linear interactions between tides and surges can occur due to the water depth and bed friction. Global investigations have been conducted to examine TSI, but its occurrence and impact on water levels in Aotearoa New Zealand (NZ) have not been extensively studied. Water level observations from 36 tide gauges across the diverse coast of NZ were analysed to determine the occurrence and location of TSI. Statistical analysis and numerical modelling were conducted on data from both inside and outside estuaries, focusing on one estuary (Manukau Harbour) to determine the impact of TSI and estuarine morphology on the co-occurrence rate of extreme events. TSI was found to occur at most sites in NZ and primarily affects the timing of the largest surges relative to high tide. There were no regional patterns associated with the tide, non-tidal residual, or skew-surge regimes. The strongest TSI occurred in inner estuarine locations and was correlated with the intertidal area. The magnitude of the TSI varied depending on the method used, ranging from -16 cm to +27 cm. Co-occurrence rates of extreme water levels outside and inside the same estuary varied from 20% to 84%, with TSI modulating the rate by affecting tidal amplification. The results highlight the importance of investing in a more extensive tide gauge network to provide longer observations in highly populated estuarine coastlines. The incorporation of TSI in flooding hazard projections would benefit from more accurate and detailed observations, particularly in estuaries with high morphological complexity. TSI occurs in most sites along the coast of NZ and has a significant impact on water levels in inner estuarine locations. TSI modulates the co-occurrence rate of extreme water levels in estuaries of NZ by affecting tidal amplification. Therefore, further investment in the tide gauge network is needed to provide more accurate observations to incorporate TSI in flooding hazard projections.</p

On the projected changes in New Zealand's wave climate and its main drivers

Wave climatologies from historical and projected simulations of the ACCESS1.0, MIROC5 and CNRM-CM5 Global Circulation Models (GCM) were sourced from the Coordinated Ocean Wave Climate Project (COWCLIP) and downscaled using the SWAN wave model. Biases between GCM's historical simulations and a regional hindcast were assessed, and the two best-performing models (ACCESS1.0, MIROC5) had their projections analysed. A general increase in wave height and period was observed along the south/west, together with a decrease in Hs along the north/east coasts. The projected near-term (NEA21C) period shows mostly a Hs increase, whilst for the long-term (END21C) period, increased and decreased Hs are present. The areas of statistically significant changes are larger in the END21C than in the NEA21C period. The wave direction change is counter-clockwise along the west and clockwise along the east coasts. This study is a first assessment of historical and projected GCM-forced waves along New Zealand and the database we generated can be of great value for renewable energy research, risk assessment and the mitigation of future coastal hazards.</p

Appendix B. A figure showing velocity distributions in Mahurangi Harbour, New Zealand.

A figure showing velocity distributions in Mahurangi Harbour, New Zealand

Appendix A. Natural history, including ecological and environmental interactions of Atrina zelandica.

Natural history, including ecological and environmental interactions of Atrina zelandica

Simulating longshore shoreline change: Improving performance of one-line models

One-line models are a popular reduced-complexity approach to simulating shoreline change driven by gradients in longshore sediment transport. The rate of sediment transport is typically calculated using an empirical formula based on the direction of incident waves relative to the shoreline, such as the CERC (US Army Corps of Engineers, 1984) or Kamphuis (1991) equations. Examples utilising this approach include well- known standalone models of longshore change like GENESIS (Hanson, 1989), CEM (Ashton et al., 2001), and more recently ShorelineS (Roelvink et al., 2020), as well as hybrid models combining cross-shore and longshore processes such as CoSMoS-COAST (Vitousek et al., 2017), COCOONED (Antolínez et al., 2019), and ShorelineEvol (de Santiago et al., 2021).</p

Evaluating five shoreline change models against 40 years of field survey data at an embayed sandy beach

Robust and reliable models are needed to understand how coastlines will evolve over the coming decades, driven by both natural variability and climate change. This study evaluated how accurately five popular ‘reduced-complexity’ models replicate multi-decadal shoreline change at Narrabeen-Collaroy Beach, a sandy embayment in Sydney, Australia. Measured shoreline positions derived from approximately monthly field surveys were used for 20-year calibration and 20-year validation periods. The models performed similarly on average but with large variability between transects. The set-up of several models was modified to compensate for their sensitivity to imperfect input wave data, and further site-specific improvements were identified. Capturing interannual to decadal-scale variability in cross-shore and longshore dynamics at this site was challenging for all five models. Models appeared to aggregate key processes at this timescale into parameter values rather than representing them directly. This suggests time-varying parameters or changes to model structure may be necessary for decadal-scale simulations.</p