Estimating the road edge effect on adjacentEucalyptus grandisforests in KwaZulu-Natal, South Africa, using texture measures and an artificial neural network

2012 ◽  
Vol 57 (2) ◽  
pp. 153-173 ◽  
Author(s):  
Romano Lottering ◽  
Onisimo Mutanga
Keyword(s):  
Neural Network ◽  
South Africa ◽  
Artificial Neural Network ◽  
Edge Effect ◽  
The Road ◽  
Kwazulu Natal ◽  
Artificial Neural

scholarly journals Application of Artificial Neural Network for Predicting Maize Production in South Africa

2019 ◽  
Vol 11 (4) ◽  
pp. 1145 ◽  
Author(s):  
Omolola Adisa ◽  
Joel Botai ◽  
Abiodun Adeola ◽  
Abubeker Hassen ◽  
Christina Botai ◽  
...  
Keyword(s):  
Neural Network ◽  
South Africa ◽  
Artificial Neural Network ◽  
Free State ◽  
Maximum Temperature ◽  
Maize Production ◽  
Free State Province ◽  
North West ◽  
Kwazulu Natal ◽  
Artificial Neural

The use of crop modeling as a decision tool by farmers and other decision-makers in the agricultural sector to improve production efficiency has been on the increase. In this study, artificial neural network (ANN) models were used for predicting maize in the major maize producing provinces of South Africa. The maize production prediction and projection analysis were carried out using the following climate variables: precipitation (PRE), maximum temperature (TMX), minimum temperature (TMN), potential evapotranspiration (PET), soil moisture (SM) and land cultivated (Land) for maize. The analyzed datasets spanned from 1990 to 2017 and were divided into two segments with 80% used for model training and the remaining 20% for testing. The results indicated that PET, PRE, TMN, TMX, Land, and SM with two hidden neurons of vector (5,8) were the best combination to predict maize production in the Free State province, whereas the TMN, TMX, PET, PRE, SM and Land with vector (7,8) were the best combination for predicting maize in KwaZulu-Natal province. In addition, the TMN, SM and Land and TMN, TMX, SM and Land with vector (3,4) were the best combination for maize predicting in the North West and Mpumalanga provinces, respectively. The comparison between the actual and predicted maize production using the testing data indicated performance accuracy adjusted R2 of 0.75 for Free State, 0.67 for North West, 0.86 for Mpumalanga and 0.82 for KwaZulu-Natal. Furthermore, a decline in the projected maize production was observed across all the selected provinces (except the Free State province) from 2018 to 2019. Thus, the developed model can help to enhance the decision making process of the farmers and policymakers.

scholarly journals Prediction of Land Use and Land Cover Changes for North Sumatra, Indonesia, Using an Artificial-Neural-Network-Based Cellular Automaton

2019 ◽  
Vol 11 (11) ◽  
pp. 3024 ◽  
Author(s):  
Muhammad Hadi Saputra ◽  
Han Soo Lee
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Artificial Neural Network ◽  
Land Cover ◽  
Cellular Automaton ◽  
Lulc Changes ◽  
The Road ◽  
Ca Model ◽  
Artificial Neural ◽  
North Sumatra

Land use and land cover (LULC) form a baseline thematic map for monitoring, resource management, and planning activities and facilitate the development of strategies to balance conservation, conflicting uses, and development pressures. In this study, changes in LULC in North Sumatra, Indonesia, are simulated and predicted using an artificial-neural-network-based cellular automaton (ANN-CA) model. Five criteria (altitude, slope, aspect, distance from the road, and soil type) are used as exploratory data in the learning process of the ANN-CA model to determine their impacts on LULC changes between 1990 and 2000; among the criteria, altitude and distance from the road have strong impacts. Comparison between the predicted and the real LULC maps for 2010 illustrates high agreement, with a Kappa index of 0.83 and a percentage of correctness of 87.28%. Then, the ANN-CA model is applied to predict LULC changes in 2050 and 2070. The LULC predictions for 2050 and 2070 demonstrate high increases in plantation area of more than 4%. Meanwhile, forest and crop area are projected to decrease by approximately 1.2% and 1.6%, respectively, by 2050. By 2070, forest and crop areas will decrease by 1.2% and 1.7%, respectively, indicating human influences on LULC changes from forest and cropland to plantations. This study illustrates that the simulation of LULC changes using the ANN-CA model can produce reliable predictions for future LULC.

scholarly journals The effect of climate change on cholera disease: The road ahead using artificial neural network

PLoS ONE ◽  
2019 ◽  
Vol 14 (11) ◽  
pp. e0224813 ◽  
Author(s):  
Zahra Asadgol ◽  
Hamed Mohammadi ◽  
Majid Kermani ◽  
Alireza Badirzadeh ◽  
Mitra Gholami
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Artificial Neural Network ◽  
The Road ◽  
Artificial Neural

Study on the Prediction Meth of Automobile Braking Distance Based on Artificial Neural Network

2015 ◽  
Vol 734 ◽  
pp. 515-521
Author(s):  
Pei Ye ◽  
Xiu Mei Zhang ◽  
Tao Jiang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Influence Factors ◽  
The Road ◽  
The Neural Network ◽  
Artificial Neural Network Method ◽  
Braking Distance ◽  
Brake Performance ◽  
Artificial Neural

The automobile braking distance is one of the important indexes to measure the brake performance, so the study of automobile braking distance is very important. Domestic and foreign scholars research on automobile brake performance and braking distance, and achieved fruitful results, but there are only little research on the braking distance predicting of the car. In the paper, the process of automobile braking, effect of the braking distance, the influence factors and the road adhesion coefficient are studied. In the paper, it also discussed the effective methods to calculate the braking distance. On these basses, the author puts forward the prediction model of automobile braking distance with artificial neural network method. In this model, the author takes the running state and parameters of the car as samples for the input and output. After training, the author gets the curing prediction model based on each layer of network weights and threshold of the neural network.

scholarly journals MODEL PERUBAHAN PENUTUPAN/PENGGUNAAN LAHAN UNTUK IDENTIFIKASI LAHAN KRITIS DI KABUPATEN BOGOR, KABUPATEN CIANJUR, DAN KABUPATEN SUKABUMI

2016 ◽  
Vol 18 (1) ◽  
pp. 21 ◽  
Author(s):  
Siti Hadjar Kubangun ◽  
Oteng Haridjaja ◽  
Komarsa Gandasasmita
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Artificial Neural Network ◽  
Spatial Models ◽  
Change Models ◽  
The Road ◽  
Sustainable Environment ◽  
Main Factors ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<div class="WordSection1"><p class="abstrak">Pemanfaatan lahan yang melampaui kemampuan lahannya, dapat mengakibatkan degradasi lahan. Degradasi lahan jika dibiarkan akan menimbulkan lahan kritis. Dampak yang terjadi akibat lahan kritis mengakibatkan lahan mengalami penurunan kualitas sifat-sifat tanah, penurunan fungsi konservasi, fungsi produksi, hingga berpengaruh pada kehidupan sosial dan ekonomi masyarakat yang memanfaatkan lahan tersebut. Penelitian ini bertujuan untuk mengidentifikasi lahan kritis, berdasarkan pemodelan perubahan penutupan/penggunaan lahan dengan metode <em>Artificial Neural Network</em> (ANN). Hasil penelitian ini menunjukkan bahwa lahan-lahan yang tergolong kritis mencakup lahan berlereng dengan penutupan/penggunaan lahan yang telah terkonversi. Faktor utama penyebab konversi lahan adalah tingginya kebutuhan hidup terhadap pangan, sandang, dan papan, akibat meningkatnya kepadatan penduduk. Selain hal tersebut, kemiringan lereng, jarak dari jalan, dan jarak dari permukiman juga menjadi faktor penyebab perubahan lahan. Upaya pemanfaatan lahan sebaiknya didukung oleh peningkatan kualitas sumber daya manusia, yang tidak hanya berorientasi pada kebutuhan sosial dan ekonomi, namun juga berorientasi pada lingkungan yang berkelanjutan.</p><p class="katakunci"><strong>Kata kunci</strong>:  jaringan saraf tiruan, perubahan penutupan/penggunaan lahan, model spasial, lahan kritis.</p><p class="judulABS"><strong>ABSTRACT</strong></p><p class="Abstrakeng"><em>Over used of land can caused the degradation, it can be lead to the critical of the land. The impacts of this issue such as the decreasing of the soil characteristics quality, conservation function, production, affecting social and economic of the society which used the land.  This research aims to identify the critical land based on the land use cover change models with Artificial Neural Network (ANN) method. This research shows the critical lands including land with the slope which has been converted with the land use cover change models. The main factors caused land converse are the high of need of food, clothing, and shelter, cause of the increasing population density. Besides those factors, the shape of slope, distance from the road and settlements   are also the result of the land changing. The efforts in using the land should be supported by the increasing of the human resources, which are not only be oriented on the need of social and economic, but also on the sustainable environment.</em></p><p class="katakunci"><em><strong>Keywords</strong>: artificial neural network (ANN), land use cover change (LUCC), spatial models, critical land.</em></p></div><strong><br clear="all" /></strong>

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