Published

2021-10-27

Prediction of land degradation by Machine Learning Methods

Degradación de la tierra medida a través de métodos de aprendizaje automático: Caso de estudio de la cuenca Sharifabad, en Irán Central

DOI:

https://doi.org/10.15446/esrj.v25n3.75821

Keywords:

Groundwater level, Partial least square regression (PLSR), Artificial neural networks (ANN), Adaptive Neuro-Fuzzy Inference System(ANFIS), Land degradation (en)

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Authors

  • Vahid Habibi Department of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran
  • Hassan Ahmadi Faculty of Natural Resource, University of Tehran, Karaj, Iran
  • Mohammad Jaffari Faculty of Natural Resource, University of Tehran, Karaj, Iran
  • Abolfazl Moeini Department of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran

In this study, three models were used to monitor and predict the GWL and the land degradation index via the IMDPA method. In all models, 70% of the data was applied for training, while 30% of data were employed for testing and validation. Monthly rainfall, TWI index, the distance of the river, Geographic location was the inputs and the level of groundwater was the output of each method. we found that ANN has the highest efficiency, which agrees with other findings. We combined the results of ANN with Ordinary Kriging and produced a groundwater condition map. According to the potential desertification map and groundwater level index, the potential of desertification had become severe since 2002 and was at a rate of 60% of land area, which, due to incorrect land management in 2016, increased to almost 98% of the land surface in the study area. Using ANN, we predicted that around 99% of the area was severely degraded for 2017. We also used latitude and longitude as input variables which improved the model. In addition to the target variable, latitude and longitude play important roles in Ordinary Kriging and decreased the total error of two combined models.

Con el fin de monitorear y predecir los niveles de agua subterránea en la cuenca Sharifabad, provincia Central de Irán, se utilizaron los modelos Regresión de mínimos cuadrados parciales (PLSR, del inglés Partial Least Square Regression), Redes neuronales artificiales (ANN, Artificial Neural Networks), y Sistema de inferencia de neurodifusión adaptativo (ANFIS, Adaptive Neuro-Fuzzy Inference System). El 70 % de la información fue utilizada para probar los tres modelos, mientras que el 30 % se empleo en la evaluación y validación. La pluviosidad mensual, el índice topográfico de humedad (TWI index), la distancia al río y la ubicación geográfica fueron los datos ingresados, mientras que el índice de agua subterránea es el resultado de cada método. Se observó que el modelo ANN es el de mayor eficiencia, y que es acorde con otros hallazgos. Los resultados del modelo ANN se utilizaron en la preparación del mapa de distribución de aguas freáticas o subterráneas. De acuerdo con el mapa de desertificación potencial y el índice de agua subterránea, el potencial de la desertificación se ha vuelto severa desde 2002. Esto significa que desde el 60 % del suelo que, debido a un manejo incorrecto en 2016, se llegó hasta casi el 98 % de terreno en el área de estudio. Con el modelo ANN se predice que el 100 % del área se degradará severamente para 2025. Además de la variación del objetivo, la latitud y la longitud juegan papeles importantes en el kriging ordinario y en la reducción del error total de los dos modelos restantes.

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Habibi, V., Ahmadi, H., Jaffari, M. and Moeini, A. (2021). Prediction of land degradation by Machine Learning Methods. Earth Sciences Research Journal, 25(3), 353–362. https://doi.org/10.15446/esrj.v25n3.75821

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Habibi, V., Ahmadi, H., Jaffari, M. and Moeini, A. 2021. Prediction of land degradation by Machine Learning Methods. Earth Sciences Research Journal. 25, 3 (Oct. 2021), 353–362. DOI:https://doi.org/10.15446/esrj.v25n3.75821.

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Habibi, V.; Ahmadi, H.; Jaffari, M.; Moeini, A. Prediction of land degradation by Machine Learning Methods. Earth sci. res. j. 2021, 25, 353-362.

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HABIBI, V.; AHMADI, H.; JAFFARI, M.; MOEINI, A. Prediction of land degradation by Machine Learning Methods. Earth Sciences Research Journal, [S. l.], v. 25, n. 3, p. 353–362, 2021. DOI: 10.15446/esrj.v25n3.75821. Disponível em: https://revistas.unal.edu.co/index.php/esrj/article/view/75821. Acesso em: 16 apr. 2024.

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Habibi, Vahid, Hassan Ahmadi, Mohammad Jaffari, and Abolfazl Moeini. 2021. “Prediction of land degradation by Machine Learning Methods”. Earth Sciences Research Journal 25 (3):353-62. https://doi.org/10.15446/esrj.v25n3.75821.

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Habibi, V., Ahmadi, H., Jaffari, M. and Moeini, A. (2021) “Prediction of land degradation by Machine Learning Methods”, Earth Sciences Research Journal, 25(3), pp. 353–362. doi: 10.15446/esrj.v25n3.75821.

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V. Habibi, H. Ahmadi, M. Jaffari, and A. Moeini, “Prediction of land degradation by Machine Learning Methods”, Earth sci. res. j., vol. 25, no. 3, pp. 353–362, Oct. 2021.

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Habibi, V., H. Ahmadi, M. Jaffari, and A. Moeini. “Prediction of land degradation by Machine Learning Methods”. Earth Sciences Research Journal, vol. 25, no. 3, Oct. 2021, pp. 353-62, doi:10.15446/esrj.v25n3.75821.

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Habibi, Vahid, Hassan Ahmadi, Mohammad Jaffari, and Abolfazl Moeini. “Prediction of land degradation by Machine Learning Methods”. Earth Sciences Research Journal 25, no. 3 (October 27, 2021): 353–362. Accessed April 16, 2024. https://revistas.unal.edu.co/index.php/esrj/article/view/75821.

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Habibi V, Ahmadi H, Jaffari M, Moeini A. Prediction of land degradation by Machine Learning Methods. Earth sci. res. j. [Internet]. 2021 Oct. 27 [cited 2024 Apr. 16];25(3):353-62. Available from: https://revistas.unal.edu.co/index.php/esrj/article/view/75821

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