scholarly journals PREDIKSI PERUBAHAN TUTUPAN LAHAN DENGAN MODEL MARKOV CHAIN DAN ANN-MARKOV DI DAS KRUENG ACEH (Land cover change prediction using Markov Chain and ANN-Markov Model in Krueng Aceh Watershed)

2021 ◽  
Vol 5 (2) ◽  
pp. 185-206
Author(s):  
Yudi Armanda Syahputra ◽  
◽  
Muhammad Buce Saleh ◽  
Nining Puspaningsih ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Markov Chain ◽  
Land Cover ◽  
Markov Model ◽  
Land Cover Change ◽  
Change Prediction ◽  
Artificial Neural ◽  
Spatio Temporal

Prediksi perubahan tutupan lahan yang baik akan menjadi pertimbangan dalam menentukan strategi pembangunan di masa depan. Terdapat banyak metode dalam melakukan prediksi perubahan tutupan lahan yang tergantung pada kebutuhan data, algoritma pemodelan yang dilakukan dan output apa saja yang diperlukan. Penelitian ini dilakukan untuk mengkaji model prediksi perubahan tutupan lahan dari tahun 2007 hingga 2020 di DAS Krueng Aceh. Pendekatan yang dilakukan menggunakan penginderaan jauh dan SIG. Model Markov Chain (MC) dan Artificial Neural Network-Markov (ANN-MC) digunakan untuk memahami dinamika spatio-temporal tutupan lahan. Akurasi dari citra penginderaan jauh yang diklasifikasikan diperoleh dari hasil interpretasi visual pada citra resolusi sedang Landsat OLI tahun 2020 dengan nilai Kappa Accuracy sebesar 84%. Kedua model prediksi menggunakan data tutupan lahan tahun 2007 (T1) dan 2017 (T2) untuk membuat probability perubahan yang digunakan dalam memprediksi tutupan lahan pada tahun 2020 (T3). Validasi kedua algoritma menunjukkan korelasi yang kuat dengan peta tutupan lahan 2020, hal tersebut membuktikan kehandalan model kedua simulasi (ANN=87,81% dan MC=88,69%).

scholarly journals Modeling land cover change based on an artificial neural network for a semiarid river basin in northeastern Brazil

2020 ◽  
Vol 21 ◽  
pp. e00811 ◽  
Author(s):  
Leonardo Pereira e Silva ◽  
Ana Paula Campos Xavier ◽  
Richarde Marques da Silva ◽  
Celso Augusto Guimarães Santos
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Land Cover ◽  
Land Cover Change ◽  
River Basin ◽  
Northeastern Brazil ◽  
Artificial Neural

scholarly journals Modelling of Expansion Changes of Vilnius City Area and Impacts on Landscape Patterns Using an Artificial Neural Network

2021 ◽  
Vol 28 (3) ◽  
pp. 429-447
Author(s):  
Mir Mehrdad Mirsanjari ◽  
Jurate Suziedelyte Visockiene ◽  
Fatemeh Mohammadyari ◽  
Ardavan Zarandian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Land Cover ◽  
Urban Expansion ◽  
Landscape Patterns ◽  
Forest Land ◽  
Dense Forest ◽  
Land Cover Dynamics ◽  
Artificial Neural ◽  
Surrounding Areas

Abstract The present study aimed to analyse changes in the land cover of Vilnius city and its surrounding areas and propose a scenario for their future changes using an Artificial Neural Network. The land cover dynamics modelling was based on a multilayer perceptron neural network. Landscape metrics at a class and landscape level were evaluated to determine the amount of changes in the land uses. As the results showed, the Built-up area class increased, while the forest (Semi forest and Dense forest) classes decreased during the period from 1999 to 2019. The predicted scenario showed a considerable increase of about 60 % in the Built-up area until 2039. The vegetation plant areas consist about 47 % of all the area in 2019, but it will be 36 % in 2039, if this trend (urban expansion) continues in the further. The findings further indicated the major urban expansion in the vegetation areas. However, Built-up area would expand over Semi forest land and Dense forest land, with a large part of them changed into built- up areas.

scholarly journals Spatio-Temporal Groundwater Drought Monitoring Using Multi-Satellite Data Based on an Artificial Neural Network

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1953 ◽  
Author(s):  
Seo ◽  
Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Satellite Data ◽  
Severity Index ◽  
Drought Severity ◽  
Human Society ◽  
Groundwater Storage ◽  
Groundwater Drought ◽  
Artificial Neural ◽  
Spatio Temporal

Drought is a complex phenomenon caused by lack of precipitation that affects water resources and human society. Groundwater drought is difficult to assess due to its complexity and the lack of spatio-temporal groundwater observations. In this study, we present an approach to evaluate groundwater drought based on relatively high spatial resolution groundwater storage change data. We developed an artificial neural network (ANN) that employed satellite data (Gravity Recovery and Climate Experiment (GRACE) and Tropical Rainfall Measuring Mission (TRMM)) as well as Global Land Data Assimilation System (GLDAS) models. The Standardized Groundwater Level Index (SGI) was calculated by normalizing ANN-predicted groundwater storage changes from 2003 to 2015 across South Korea. The ANN-predicted 25 km groundwater storage changes correlated well with both the in situ and the water balance equation (WBE)-estimated groundwater storage changes, with mean correlation coefficients of 0.87 and 0.64, respectively. The Standardized Precipitation–Evapotranspiration Index (SPEI), having an accumulation time of 1–6 months, and the Palmer Drought Severity Index (PDSI) were used to validate the SGI. The results showed that the SGI had a pattern similar to that of SPEI-1 and SPEI-2 (1- and 2-month accumulation periods, respectively), and PDSI. However, the SGI performance fluctuated slightly due to its relatively short study period (13 years) as compared to SPEI and PDSI (more than 30 years). The SGI, which was developed using a new approach in this study, captured the characteristics of groundwater drought, thus presenting a framework for the assessment of these characteristics.

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