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 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 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 Artificial Neural Network Model for Estimation of Suspended Sediment Load in Krishna River Basin, India

2020 ◽  
Vol 9 (3) ◽  
pp. 3701-3705
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Level ◽  
Suspended Sediment ◽  
River Basin ◽  
Flood Control ◽  
Sediment Load ◽  
Water Discharge ◽  
Suspended Sediment Load ◽  
Artificial Neural

The correct assessment of amount of sediment during design, management and operation of water resources projects is very important. Efficiency of dam has been reduced due to sedimentation which is built for flood control, irrigation, power generation etc. There are traditional methods for the estimation of sediment are available but these cannot provide the accurate results because of involvement of very complex variables and processes. One of the best suitable artificial intelligence technique for modeling this phenomenon is artificial neural network (ANN). In the current study ANN techniques used for simulation monthly suspended sediment load at Vijayawada gauging station in Krishna river basin, Andhra Pradesh, India. Trial & error method were used during the optimization of parameters that are involved in this model. Estimation of suspended sediment load (SSL) is done using water discharge and water level data as inputs. The water discharge, water level and sediment load is collected from January 1966 to December 2005. This approach is used for modelled the SSL. By considering the results, ANN has the satisfactory performance and more accurate results in the simulation of monthly SSL for the study location.

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