Monitoring and modeling land use/cover changes in Arasbaran protected Area using and integrated Markov chain and artificial neural network

2020 ◽  
Vol 6 (3) ◽  
pp. 1901-1911 ◽  
Author(s):  
Elham Shahi ◽  
Saeed Karimi ◽  
Hamid Reza Jafari
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Artificial Neural Network ◽  
Markov Chain ◽  
Protected Area ◽  
Artificial Neural

scholarly journals Multi-layer perceptron-Markov chain-based artificial neural network for modelling future land-specific carbon emission pattern and its influences on surface temperature

2021 ◽  
Vol 3 (3) ◽  
Author(s):  
Md.Abdul Fattah ◽  
Syed Riad Morshed ◽  
Syed Yad Morshed
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Artificial Neural Network ◽  
Markov Chain ◽  
Surface Temperature ◽  
Land Surface ◽  
Sustainable Urban Development ◽  
Strong Positive Correlation ◽  
Multi Layer Perceptron ◽  
Artificial Neural

AbstractReliable and accurate environmental state prediction can help in long-term sustainable planning and management. Enormous land-use/ land-cover (LULC) transformation has been increasing the carbon emissions (CEs) and land surface temperature (LST) around the world. The study aimed to (i) examine the influences of land specific CEs on LST dynamics and (ii) simulate future potential LULC, CEs and LST pattern of Khulna City Corporation. Landsat satellite images of the year 2000, 2010 and 2020 were used to derive LULC, LST and CEs pattern and change. The correlation between land-use indices (NDBI, NDVI, NDWI) and LST was examined to explore the impacts of LULC change on LST. Unplanned urbanization has increased 11.79 Km2(26.10%) buildup areas and 25,268 tons of CEs during 2000–2020. The calculated R2 value indicates the strong positive correlation between CEs and LST. To simulate the future LULC, CEs and LST pattern for the year 2030 and 2040, multi-layer perceptron-Markov chain (MLP-MC)-based artificial neural network model was utilized with the accuracy rate of 94.12%, 99% and 98.48% for LULC, LST and CEs model, respectively. The simulation shows that by 2040, buildup area will increase to 87.33%, net CEs will increase by 19.82 × 104tons, and carbon absorptions will decrease by 23. 55 × 104tons and 69.54% of the total study area's LST will be above 390C. Such predictions signify the necessity of implementing a sustainable urban development plan immediately for the sustainable, habitable and sound urban environment.

Application of Neural Network in Urban Land Use Suitability Evaluation

2011 ◽  
Vol 474-476 ◽  
pp. 681-686
Author(s):  
Xiao Rui Zhang ◽  
Gang Chen
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Urban Land ◽  
Urban Land Use ◽  
Suitability Evaluation ◽  
Land Use Suitability ◽  
Artificial Neural

Urban land use suitability evaluation is the basic work of urban land use planning and management. The evaluation method is a core in urban land use suitability evaluation. Traditional urban land use suitability evaluation methods are GIS-based methods which often can not get satisfactory results for the complex nonlinear urban land use system. Artificial neural network is a frontier theory of complex non-linearity scientific and artificial intelligence science. It is a new method to evaluate urban land use suitability. This paper took the land use suitability evaluation of Hefei city as an example, building a back propagation neural network with 8 neurous of input layer, 5 neurons of hide layer and 3 neurons of output layer. The analysis shows: the high suitability area is 682.27 km2in Hefei city, being about 8.73% of the total study area; the middle suitability area is 5965.76 km2, or about 76.33% of the total area and the low suitability area is 1167.35 km2, or about 14.94% of the total area. The results reflect the actual situation in Hefei city. The study shows that the back propagation neural network model can overcome the shortcomings of traditional evaluation methods. It means that artificial neural network is suitable for urban land use suitability evaluation. This reflects that artificial neural network has great academic value and application prospect in urban land use suitability evaluation. It also reflects that this study can provide a new idea and method for urban land use suitability evaluation.

scholarly journals Modeling the Land Use Change in an Arid Oasis Constrained by Water Resources and Environmental Policy Change Using Cellular Automata Models

2018 ◽  
Vol 10 (8) ◽  
pp. 2878 ◽  
Author(s):  
Xiaoli Hu ◽  
Xin Li ◽  
Ling Lu
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Artificial Neural Network ◽  
Sustainable Development ◽  
Environmental Policy ◽  
Water Resources ◽  
Cellular Automata ◽  
Policy Change ◽  
Arid Regions ◽  
Artificial Neural

Land use and land cover change (LUCC) is an important issue in global environmental change and sustainable development, yet spatial simulation of LUCC remains challenging due to the land use system complexity. The cellular automata (CA) model plays a crucial role in simulating LUCC processes due to its powerful spatial computing power; however, the majority of current LUCC CA models are binary-state models that cannot provide more general information about the overall spatial pattern of LUCC. Moreover, the current LUCC CA models rarely consider background artificial irrigation in arid regions. Here, a multiple logistic-regression-based Markov cellular automata (MLRMCA) model and a multiple artificial-neural-network-based Markov cellular automata (MANNMCA) model were developed and applied to simulate complex land use evolutionary processes in an arid region oasis (Zhangye Oasis), constrained by water resources and environmental policy change, during the period 2000–2011. Results indicated that the MANNMCA model was superior to the MLRMCA model in simulated accuracy. Furthermore, combining the artificial neural network with CA more effectively captured the complex relationships between LUCC and a set of spatial driving variables. Although the MLRMCA model also showed some advantages, the MANNMCA model was more appropriate for simulating complex land use dynamics. The two integrated models were reliable, and could reflect the spatial evolution of regional LUCC. These models also have potential implications for land use planning and sustainable development in arid regions.

scholarly journals An Artificial Neural Network Approach to Predict the Future Land Use Land Cover of Great Malang Region, Indonesia

2021 ◽  
Author(s):  
Fatwa Ramdani ◽  
Budi Setiawan ◽  
Alfi Rusydi ◽  
Muhammad Furqon
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Artificial Neural Network ◽  
Land Cover ◽  
Negative Impact ◽  
Population Increase ◽  
Land Use Land Cover ◽  
Neural Network Approach ◽  
The Future ◽  
Artificial Neural

Great Malang region is developing rapidly with the population increase and inhabitant`s activity, like migration and urbanization. Other activities like agricultural expansion as well as an uncontrolled residential development need to be monitored to avoid any negative impact in the future. The availability of free and open-source software, spatial high-resolution satellite imagery datasets, and powerful algorithms open the possibilities to map, monitor, and predict the future trend of land use land cover (LULC) changes. However, the accuracy and precision of this model is still in doubt, especially in the Great Malang region. Research is needed to provide a foundational basis and documentation on how the changes occur, where did the changes occur, and the accuracy of the predicted model. This study tries to answer those questions using the high spatial resolution of Sentinel-2 imageries. Combination of the fuzzy algorithm, artificial neural network, and cellular automata was utilized to process the datasets. We analysed four different scenarios of simulation and the result then compared. The different number of hidden layers and iteration was used and evaluated to understand the effect of different parameters in the prediction result. The best scenario was then used to predict future land use changes. This study has successfully produced the future LULC model of Great Malang region with high accuracy level (87%). The study also found that the land use transformation from agriculture to urban built-up area is relatively low, where changes of the built-up area over three periods of analysis are below than 5%. This is due to the physical condition of Great Malang region where mountainous areas are dominated.

scholarly journals Prediction of Land Use and Land Cover Changes for North Sumatra, Indonesia, Using an Artificial-Neural-Network-Based Cellular Automaton

2019 ◽  
Vol 11 (11) ◽  
pp. 3024 ◽  
Author(s):  
Muhammad Hadi Saputra ◽  
Han Soo Lee
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Artificial Neural Network ◽  
Land Cover ◽  
Cellular Automaton ◽  
Lulc Changes ◽  
The Road ◽  
Ca Model ◽  
Artificial Neural ◽  
North Sumatra

Land use and land cover (LULC) form a baseline thematic map for monitoring, resource management, and planning activities and facilitate the development of strategies to balance conservation, conflicting uses, and development pressures. In this study, changes in LULC in North Sumatra, Indonesia, are simulated and predicted using an artificial-neural-network-based cellular automaton (ANN-CA) model. Five criteria (altitude, slope, aspect, distance from the road, and soil type) are used as exploratory data in the learning process of the ANN-CA model to determine their impacts on LULC changes between 1990 and 2000; among the criteria, altitude and distance from the road have strong impacts. Comparison between the predicted and the real LULC maps for 2010 illustrates high agreement, with a Kappa index of 0.83 and a percentage of correctness of 87.28%. Then, the ANN-CA model is applied to predict LULC changes in 2050 and 2070. The LULC predictions for 2050 and 2070 demonstrate high increases in plantation area of more than 4%. Meanwhile, forest and crop area are projected to decrease by approximately 1.2% and 1.6%, respectively, by 2050. By 2070, forest and crop areas will decrease by 1.2% and 1.7%, respectively, indicating human influences on LULC changes from forest and cropland to plantations. This study illustrates that the simulation of LULC changes using the ANN-CA model can produce reliable predictions for future LULC.

Modeling effects of changing land use/cover on daily streamflow: An Artificial Neural Network and curve number based hybrid approach

2013 ◽  
Vol 485 ◽  
pp. 103-112 ◽  
Author(s):  
Sabahattin Isik ◽  
Latif Kalin ◽  
Jon E. Schoonover ◽  
Puneet Srivastava ◽  
B. Graeme Lockaby
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Artificial Neural Network ◽  
Hybrid Approach ◽  
Curve Number ◽  
Daily Streamflow ◽  
Artificial Neural
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