Modeling the hydrological impacts of land use/land cover changes in the Andassa watershed, Blue Nile Basin, Ethiopia

Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects.

Vector-based cellular automata (VCA) models have been applied in land use change simulations at fine scales. However, the neighborhood effects of the driving factors are rarely considered in the ex...

Simulating urban land use change by integrating a convolutional neural network with vector-based cellular automata

A critical issue in urban cellular automata (CA) modeling concerns the identification of transition rules that generate realistic urban land use patterns. Recent studies have demonstrated that linear methods cannot sufficiently delineate the extraordinary complex boundaries between urban and non-urban areas and as most urban CA models simulate transitions across these boundaries, there is an urgent need for good methods to facilitate such delineations. This paper presents a machine learning CA model (termed MachCA) with nonlinear transition rules based on least squares support vector machines (LS-SVM) to simulate such urban growth. By projecting the input dataset into a high dimensional space using the LS-SVM method, an optimal hyper-plane is constructed to separate the complex boundaries between urban and nonurban land, thus enabling the retrieval of nonlinear CA transition rules. In the MachCA model, the transition rules are yes–no decisions on whether a cell changes its state or not, the rules being dynamically updated for each iteration of the model implementation. The application of the MachCA for simulating urban growth in the Shanghai Qingpu–Songjiang area in China reveals that the spatial configurations of rural–urban patterns can be modeled. A comparison of the MachCA model with a conventional CA model fitted by logarithmic regression (termed LogCA) shows that the MachCA model produces more hits and less misses and false alarms due to its capability for capturing the spatial complexity of urban dynamics. This results in improved simulation accuracies, although with only less than 1 % deviation between the overall errors produced by the MachCA and LogCA models. Nevertheless, the way MachCA model use in retrieving the transition rules provides a new method for simulating the dynamic process of urban growth.

Modeling urban growth with GIS based cellular automata and least squares SVM rules: a case study in Qingpu–Songjiang area of Shanghai, China

The frequency in occurrence and severity of floods has increased globally. However, many regions around the globe, especially in developing countries, lack the necessary field monitoring data to characterize flood hazard risk. This paper puts forward methodology for developing flood hazard maps that define flood hazard risk, using a remote sensing and GIS-based flood hazard index (FHI), for the Nyamwamba watershed in western Uganda. The FHI was compiled using analytical hierarchy process and considered slope, flow accumulation, drainage network density, distance from drainage channel, geology, land use/cover and rainfall intensity as the flood causative factors. These factors were derived from Landsat, SRTM and PERSIANN remote sensing data products, except for geology that requires field data. The resultant composite FHI yielded a flood hazard map pointing out that over 11 and 18% of the study area was very highly and highly susceptible to flooding, respectively, while the remaining area ranged from medium to very low risk. The resulting flood hazard map was further verified using inundation area of a historical flood event in the study area. The proposed methodology was effective in producing a flood hazard map at the watershed local scale, in a data-scarce region, useful in devising flood mitigation measures.

Characterizing flood hazard risk in data-scarce areas, using a remote sensing and GIS-based flood hazard index

In any watershed, many factors influence land use/cover change (LUCC). The nonlinear relationships between these factors and LUCC are very complicated and make it difficult to build a model that is capable of accurately simulating the range of physical processes. The aim of this study was: (1) to simplify the structure of a simulation model and improve its simulation speed, and (2) evaluate the impact of land use/cover change on surface runoff and evapotranspiration. Firstly, a coupled cellular automata (CA) and artificial neural network (ANN) watershed land use/cover change simulation model, calibrated and validated using 1980 and 2000 land use data, respectively, was developed. It was used to simulate land use/cover type in the upper reaches of the Hanjiang Basin for 2020. Results indicate that the area of paddy field, dry land, shrubbery and construction land by 2020 will have increased; however, woodland, grassland and water areas will have decreased. Secondly, hydrological processes in the upper reaches of the Hanjiang Basin were simulated using the SWAT model. Finally, variations in watershed surface runoff and evapotranspiration for the LUCC 1980 and 2000 scenarios and the simulated 2020 scenario were analyzed. Results show that there is an increasing trend in the annual average runoff flowing into the Danjiangkou Reservoir, and that land use change has more influence on runoff throughout the year than during the flood season. The annual average evapotranspiration, annual runoff variation coefficient and annual runoff distribution coefficient were predicted to increase. Results confirm that (1) the ANN–CA model is capable of simulating land use/cover change for multiple classes, (2) the SWAT model facilitates sustainable land management planning, and (3) coupling the two models provides a new method for assessing the potential redistribution of land use types in the future.

Simulation of land use/land cover change and its effects on the hydrological characteristics of the upper reaches of the Hanjiang Basin

Assessment of paddy expansion impact on regional climate using WRF model: a case study in Sanjiang Plain, Northeast China

Quantifying the effects of meteorological change between neighboring days on human thermal comfort in China

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The pattern of virtual water transfer in China: From the perspective of the virtual water hypothesis

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Papers

Simulation of land use/land cover change and its effects on the hydrological characteristics of the upper reaches of the Hanjiang Basin

Assessment of paddy expansion impact on regional climate using WRF model: a case study in Sanjiang Plain, Northeast China

Quantifying the effects of meteorological change between neighboring days on human thermal comfort in China

The pattern of virtual water transfer in China: From the perspective of the virtual water hypothesis