Monitoring and Prediction of the changes in water zone of wetlands using an intelligent neural-fuzzy system based on data from Google Eearth Engine system (Case study of Anzali Wetland, 2000-2019)

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Seyed Mousavi Seyed Morteza; Akhoondzadeh Hanzaei Mahdi

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Journal: ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY Year:2022 | Volume:9 | Issue:4 Page(s): 19-42

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Title

Monitoring and Prediction of the changes in water zone of wetlands using an intelligent neural-fuzzy system based on data from Google Eearth Engine system (Case study of Anzali Wetland, 2000-2019)

Author(s)

Seyed Mousavi Seyed Morteza | Akhoondzadeh Hanzaei Mahdi | Issue Writer Certificate

Keywords

Wetland

Google Eearth Engine

time series analysis

neural network

Fuzzy Inference System

Abstract

Wetlands are one of the most important ecological resources. Detecting their long-term changes plays a key role in the quality of the management of such areas. These unique ecosystems in the world with high ecological diversity are threatened by various natural factors such as: decrease in rainfall, increase in temperature, increase in evaporation, drought, and so on. This research focuses on developing a practical and effective framework for long-term monitoring of water area of the Wetland using parameters affecting the Wetland and Landsat time series images, all obtained from the Google Earth Engine (GEE) system. In this study, in order to determine the Changes in the water body, normalized difference water index (NDWI) has been used to have a better discrimination between water and other classes of the region. The changes in the water area of Anzali Wetland and the natural factors affecting it were studied in the period of 240 months between January 2000 and December 2019. Then, by using the method based on MLP machine learning and the parameters affecting the surface changes of the Wetland as the input of the network, the surface changes of the Wetland with average root mean square error (RMSE) of 0. 977 were modeled. Also, in order to predict the severe surface changes of the Wetland in the future, the surface changes of the Wetland and all parameters for a long period (last 20 years) were examined on a monthly basis using the Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) methods. Finally, according to the results obtained from the previous stages and detecting the factors that have a greater impact on the Wetland and due to uncertainty, nonlinearity of the behavior of variables, the Fuzzy Inference System (FIS) was designed to create a Wetland drought warning system. Therefore, the developed model can be easily implemented to be used continuously for the management and monitoring of Wetlands.

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APA: Copy

Seyed Mousavi, Seyed Morteza, & Akhoondzadeh Hanzaei, Mahdi. (2022). Monitoring and Prediction of the changes in water zone of wetlands using an intelligent neural-fuzzy system based on data from Google Eearth Engine system (Case study of Anzali Wetland, 2000-2019). ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY, 9(4 ), 19-42. SID. https://sid.ir/paper/1051650/en

Vancouver: Copy

Seyed Mousavi Seyed Morteza, Akhoondzadeh Hanzaei Mahdi. Monitoring and Prediction of the changes in water zone of wetlands using an intelligent neural-fuzzy system based on data from Google Eearth Engine system (Case study of Anzali Wetland, 2000-2019). ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY[Internet]. 2022;9(4 ):19-42. Available from: https://sid.ir/paper/1051650/en

IEEE: Copy

Seyed Morteza Seyed Mousavi, and Mahdi Akhoondzadeh Hanzaei, “Monitoring and Prediction of the changes in water zone of wetlands using an intelligent neural-fuzzy system based on data from Google Eearth Engine system (Case study of Anzali Wetland, 2000-2019),” ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY, vol. 9, no. 4 , pp. 19–42, 2022, [Online]. Available: https://sid.ir/paper/1051650/en

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