Preliminary Model of Saturated Flow Using Cellular Automata A fullycoupled from surface to groundwater hydrological model is being developed based on the Extended Cellular Automata formalism (XCA), which proves to be very suitable for high performance computing. In this paper, a preliminary module related to threedimensional saturated flow in porous media is presented and implemented by using the OpenCAL parallel software library. This allows to exploit distributed systems with heterogeneous computational devices. The proposed model is evaluated in terms of both accuracy and precision of modeling results and computational performance, using single layered threedimensional test cases at different resolutions (from local to regional scale), simulating pumping from one or more wells, rivergroundwater interactions and varying soil hydraulic properties. Model accuracy is compared with analytic, when available, or numerical (MODFLOW 2005) solution, while the computational performance is evaluated using an Intel Xeon CPU socket. Overall, the XCAbased model proves to be accurate and, mainly, computationally very efficient thanks to the many options and tools available with the OpenCAL library.

A novel cellular automata model integrated with deep learning for dynamic spatiotemporal land use change simulation Abstract   Land use change (LUC) exhibits obvious spatiotemporal dependency. Previous cellular automata (CA)based methods usually treated the LUC dynamics as Markov processes and proposed a series of CAMarkov models, which however, were intrinsically unable to capture the longterm temporal dependency. Meanwhile, such models used only numerical proportion of neighboring land use (LU) types to represent neighborhood effects of LUC, which inevitably neglected the complicated spatial heterogeneity and thus caused inaccurate simulation results. To address these problems, this paper presents a novel CA model integrated with deep learning (DL) techniques to model spatiotemporal LUC dynamics. Our DLCA model firstly uses a convolutional neural network to capture latent spatial features for complete representation of neighborhood effects. A recurrent neural network then extracts historical information of LUC from timeseries land use maps. A random forest is appended as binary change predictor to avoid the imbalanced sample problem during model training.  Land use data collected from 2000 to 2014 of the Dongguan City, China were used to verify our proposed DLCA model. The input data from 2000 to 2009 were used for model training, the 2010 data for model validation, and the data collected from 2011 to 2014 were used for model evaluation. In addition, four traditional CA models of multilayer perceptron (MLP)CA, support vector machine (SVM)CA, logistic regression (LR)CA and random forest (RF)CA were also developed for accuracy comparisons. The simulation results demonstrate that the proposed DLCA model accurately captures longterm spatiotemporal dependency for more accurate LUC prediction results. The DLCA model raised prediction accuracy by 9.3%xe2x80x9311.67% in 2011xe2x80x932014 in contrast to traditional CA models.