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Accurately simulating and predicting the urban expansion process, especially in expeditious urbanization areas, is an important aspect of managing limited land resources and adjusting flawed land use policies. This research was conducted on the basis of a high-temporal-resolution land use dataset to precisely model urban expansion in a rapidly developing zone by integrating the Artificial Neural Network (ANN), cellular automata (CA), and Markov Chain (MC). An urban suitability index (USI) map was created using ANN and fed to CA–MC to identify possible changed-to-urban cells. Two ANNs, multiple-layer perceptron (MLP) and long short-term memory network (LSTM), were implemented as simulation models for comparison. Due to its ability to capture more temporal information, LSTM outperformed MLP in modeling urban expansion dynamics over a short temporal interval. The simulated results were validated by (fuzzy) kappa simulation and the results revealed that the combination of ANN and CA–MC can precisely model the urban development locations due to its strength in revealing the nonlinear relationship between the expansion process and its drivers. The same model was applied to southern Auckland, and the compared results show that the most simulated variance is caused by the land use policies implemented by different types of governments. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Predicting recreational water quality is one of the most difficult tasks in water management with major implications for humans and society. Many data-driven models have been used to predict water quality indicators to allow a real time assessment of public health risk. This assessment is most commonly based on Faecal Indicator Bacteria (FIB), with the value of FIB compared with thresholds published in guidelines. However, FIB values usually tend to be unbalanced within water quality datasets, with small proportions of data exceeding guideline thresholds and far larger numbers that do not. This can be a limiting factor in the uptake of model predictions since, even if the overall accuracy is high, the sensitivity of the predictions can be low. To address this issue, this paper proposes an adaptive synthetic sampling algorithm (ADASYN) to generate synthetic above-threshold FIB instances and test the validity of the approach for the prediction of recreational water quality. The models in this paper are based on four machine learning techniques: k-mean nearest neighbour, boosting decision tree, support vector machine, and multi-layer perceptron artificial neural network and are applied to five different locations in Auckland, New Zealand. Aside from support vector machine, all models provide favourable predictions with relatively high sensitivity (around 75%) and overall accuracy (over 90%), indicating that both the compliant and exceedance conditions can be effectively predicted through the use of more sophisticated model training which involves artificial data. Considering the model accuracy and stability, boosting decision trees (BDT) and multi-layer perceptron artificial neural (MLP-ANN) network are the best two models and the multi-layer perceptron is the most efficient with the shortest computation time. Image 1 • Applied ADASYN to address the problem of unbalanced water quality datasets. • Ssessed the ability of machine learning models when using a balanced dataset. • Compared results at different locations using different machine learning models. [ABSTRACT FROM AUTHOR]

In characterising urban expansion, existing landscape indices lack consideration for directional changes. This limitation is overcome with the proposed directional perspective (urban expansion vector) in this study, which can identify both the urban expansion mode and local changes at the patch and sectorial levels, and reveal the varying importance of the same urbanisation factors at multi-scales. Landscape (expansion) indices (LEI) and directional characteristics of newly grown urban patches were derived / extracted from time-series (1975–2016) satellite images in the Auckland metropolitan area, based on which urban expansion was simulated using logistic regression cellular automata at both the global and sectorial levels. The results show that Auckland urban development was mostly compact with "organic" being the dominant mode of expansion throughout the last 40 years. Most urban development took place in the southeast (270° - 360°) and northwest (90° - 180°) sectors, within which urban expansion occurred mostly in places that had favourable transportation infrastructures and are close to the CBD. The influence of the nine factors of urbanisation studied varies with scale and sector. In general, accessibility (e.g., proximity to roads and the CBD) and amenity (e.g., distance to transportation and schools) are more important than physical variables (e.g., terrain) in affecting urban development. The localised sectorial simulation result is in a closer agreement with the observed urbanisation than the global, sectorless result. The directional perspective, in conjunction with the LEI and other landscape indices, can effectively characterise the spatiotemporal patterns of urban expansion. More importantly, it improves the simulation accuracy of urban expansion (e.g., simulation kappa rising from 0.024 to 0.161 in the northwest sector, and from 0.31 to 0.40 in the southeast sector) for a monocentric city whose urbanisation is strongly sectorial. • This paper characterised and quantified the spatiotemporal dynamics of directional expansion of urban Auckland; • The Logistic-CA model assessed the varying importance of nine factors in affecting directional expansion of urban areas at multi-scales; • This research assessed the effect of the directional perspective on the accuracy of simulating urban expansion at the global and sectorial scales; • This directional perspective provides a refined method with several logic steps for more accurate modelling of urban area growth. [ABSTRACT FROM AUTHOR]