Cellular Automata-Based Artificial Neural Network Model for Assessing Past, Present, and Future Land Use/Land Cover Dynamics

Abstract

Land use and land cover change (LULCC) is among the most apparent natural landscape processes impacted by anthropogenic activities, particularly in fast-growing regions. In India, at present, due to the impacts of anthropogenic climate change, supplemented by the fast pace of developmental activities, the areas providing the highest agricultural yields are facing the threat of either extinction or change in land use. This study assesses the LULCC in the fastest-changing landscape region of the Indian state of Bihar, District Muzaffarpur. This district is known for its litchi cultivation, which, over the last few years, has been observed to be increasing in acreage at the behest of a decrease in natural vegetation. In this study, we aim to assess the past, present and future changes in LULC of the Muzaffarpur district using support vector classification and CA-ANN (cellular automata-artificial neural network) algorithms. For assessing the present and past LULC of the study area, we used Landsat Satellite data for 1990, 2000, 2010, and 2020. It was observed that between 1990 and 2020, the area under vegetation, wetlands, water body, and fallow land decreased by 44.28%, 34.82%, 25.56%, and 5.63%, respectively. At the same time, the area under built-up, litchi plantation, and cropland increased by 1451.30%, 181.91%, and 5.66%, respectively. Extensive ground truthing was carried out to assess the accuracy of the LULC for 2020, whereas historical google earth images were used for 1990, 2000, and 2010, through the use of overall accuracy and kappa coefficient indices. The kappa coefficients for the final LULC for the years 1990, 2000, 2010, and 2020 were 0.79, 0.75, 0.87, and 0.85, respectively. For forecasting the future LULC, first, the LULC of 1990 and 2010 were used to predict the landscape for 2020 using the CA-ANN model. After calibrating and validating the CA-ANN outputs, LULC for 2030 and 2050 were generated. The generated future LULC scenarios were validated using kappa index statistics by comparing the forecast outcomes with the original LULC data for 2020. It was observed that in both 2030 and 2050, built-up and vegetation would be the major transitioning LULC. In 2030 and 2050, built-up will increase by 13.15% and 108.69%, respectively, compared to its area in 2020; whereas vegetation is expected to decrease by 14.30% in 2030 and 32.84% in 2050 compared to its area in 2020. Overall, this study depicted a decline in the natural landscape and a sudden increase in the built-up and cash-crop area. If such trends continue, the future scenario of LULC will also demonstrate the same pattern. This study will help formulate better land use management policy in the study area, and the overall state of Bihar, which is considered to be the poorest state of India and the most vulnerable to natural calamities. It also demonstrates the ability of the CA-ANN model to forecast future events and comprehend spatiotemporal LULC dynamics.

Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models

“…The region may face a serious problem with irrigation and groundwater. Based on the obtained kappa values, the CA-Markov model proves suitable for accurately predicting the future spatial and temporal dynamics of LULC in the studied landscape [59]. Consequently, LULC change prediction models with 80% or higher accuracy are typically considered highly reliable predictive tools [60].…”

Flood inundation prediction model related to land subsidence with Lidar in North Coastal Jakarta

“…Additionally, the 2021 data is used as a reference. Artificial neural networks exhibit high efficiency compared to traditional models, leveraging hidden layers to create a prediction model for land use change and incorporating Cellular Automata for transitional potential prediction 10 . Figure 2 shows the flowchart of the method used in the study.…”

The Kerch Peninsula in Transition: A Comprehensive Analysis of Land Use and Land Cover Changes Over Thirty Years

“…For example, a study on the Pakhal Lake area in Telangana utilized the MOLUSCE plugin to detect LULCC and predict future changes in the landscape [13]. Similarly, cellular automata-based artificial neural network model incorporated the MOLUSCE plugin to assess changes in various LULC classes over time [14]. Furthermore, the MOLUSCE plugin has been instrumental in monitoring and simulating landscape changes over long periods.…”