Prediction of spatio-temporal land use/land cover dynamics in rapidly developing Varanasi district of Uttar Pradesh, India, using geospatial approach: a comparison of hybrid models

Prediction of Land Use and Land Cover Changes in Mumbai City, India, Using Remote Sensing Data and a Multilayer Perceptron Neural Network-Based Markov Chain Model

“…Previously, several models have established to imitate and predict the future pattern of LULC. Commonly used models for predicting future LULC are empirical models [17], evolutionary models [18], SLEUTH [19][20][21], cellular automaton (CA)-Markov chain model (MCM) [22][23][24], agent-based models [25], artificial neural network/multiple perceptron neural network (ANN/MLPNN)-based model [26][27][28][29], etc. The detailed information of these models can be found in [26,[30][31][32].…”

“…The detailed information of these models can be found in [26,[30][31][32]. These models have demonstrated their ability to deliver a quantifiable technique to enable the policy-making process for ecological and urban management and suitability evaluation of land, which is essential to any growing region of the world [27,[33][34][35]. Alternatively, while predicting the future pattern of LULC, each of these models has distinct limitations that have been discussed in [36].…”

“…MLPNN-MCM is a hybrid type of model which was successfully used and tested by numerous researchers [47][48][49][50]. The capability of MLPNN to model numerous or even entire transitions at one time makes MLPNN-MCM better than other models to predict the future pattern of LULC [27]. It also automatically produces several parameter values that take a lesser amount of data for training and reduce the calibration time of the model [46].…”

“…It also automatically produces several parameter values that take a lesser amount of data for training and reduce the calibration time of the model [46]. MLPNN also combines the different parameters that affects the various LULC transitions [26,27]. In the MLPNN-MCM approach, MLPNN is based on a supervised backpropagation algorithm with effective competence for each LULC alteration and simulation.…”

Avaliação da dinâmica de áreas de conservação por meio de ferramentas de suporte à decisão na Bacia Hidrográfica da Represa do Lobo (Itirapina, SP)

“…Por exemplo, se na modelagem houver um potencial para o desmatamento devido à agricultura, as variáveis podem considerar o declive do terreno, proximidade de estradas ou proximidade às áreas anteriormente desmatadas (EASTMAN, 2012;MAS et al, 2014). Além disso, a associação entre cada variável e a distribuição das classes de cobertura do solo que passaram por transição dominantes, é verificada quanto ao seu poder explicativo por meio do coeficiente V de Cramer (HEIDARLOU et al, 2019;MAS et al, 2014;MISHRA et al, 2018). O coeficiente V de Cramer (CRAMÉR, 2016) é calculado pela Equação (1).…”