Optimization for prediction model of palm oil land suitability using spatial decision tree algorithm

Land suitability evaluation has a vital role in land use planning aimed to increase food production effectiveness. Palm oil is a leading and strategic commodity for Indonesian people, which is predicted consumption will exceed production in the future. This study aims to evaluate palm oil land suitability using a spatial decision tree algorithm that is conventional decision tree modification for spatial data classification with adding spatial join relation. The spatial dataset consists of eight explanatory layers (soil nature and characteristics), and a target layer (palm oil land suitability) in Bogor District, Indonesia. This study produced three models, where the best model was obtained based on optimizing accuracy (98.18 %) and modeling time (1.291 seconds). The best model has 23 rules, soil texture as the root node, two variables (drainage and cation exchange capacity) are uninvolved, with land suitability visualization obtains percentage S2 (29.94 %), S3 (53.16 %), N (16.57 %), and water body (0.33 %).

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Spatial decision tree model for garlic land suitability evaluation

IAES International Journal of Artificial Intelligence (IJ-AI) ◽

10.11591/ijai.v10.i3.pp666-675 ◽

2021 ◽

Vol 10 (3) ◽

pp. 666

Author(s):

Andi Nurkholis ◽

Imas Sukaesih Sitanggang ◽

Annisa Annisa ◽

Sobir Sobir

Keyword(s):

Decision Tree ◽

Land Suitability ◽

Decision Tree Algorithm ◽

Tree Model ◽

Spatial Join ◽

Tree Algorithm ◽

Suitability Evaluation ◽

Root Node ◽

Target Layer ◽

Weather Characteristics

Predicting land and weather characteristics as indicators of land suitability is very important in increasing effectiveness in food production. This study aims to evaluate the suitability of garlic land using spatial decision tree algorithm. The algorithm is the improvement of the conventional decision tree algorithm in which spatial join relation is included to grow up spatial decision tree. The spatial dataset consists of a target layer that represents garlic land suitability and ten explanatory layers that represent land and weather characteristics in the study areas of Magetan and Solok district, Indonesia. This study generated the best spatial decision trees for each study area. On Magetan dataset, the best model has 33 rules with 94.34% accuracy and relief variable as the root node, whereas on Solok dataset, the best model has 66 rules with 60.29% accuracy and soil texture variable as the root node.

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C4.5 Decision Tree Algorithm for Spatial Data, Alternatives and Performances

Journal of Computing and Information Technology ◽

10.20532/cit.2019.1004651 ◽

2020 ◽

Vol 27 (3) ◽

pp. 29-43

Author(s):

Sihem Oujdi ◽

Hafida Belbachir ◽

Faouzi Boufares

Keyword(s):

Data Mining ◽

Decision Tree ◽

Spatial Data ◽

Computation Time ◽

Spatial Relations ◽

Spatial Data Analysis ◽

Data Organization ◽

Decision Tree Algorithm ◽

Tree Algorithm ◽

C4.5 Decision Tree

Using data mining techniques on spatial data is more complex than on classical data. To be able to extract useful patterns, the spatial data mining algorithms must deal with the representation of data as stack of thematic layers and consider, in addition to the object of interest itself, its neighbors linked through implicit spatial relations. The application of the classification by decision trees combined with the visualization tools represents a convenient decision support tool for spatial data analysis. The purpose of this paper is to provide and evaluate an alternative spatial classification algorithm that supports the thematic-layered data organization, by the adaptation of the C4.5 decision tree algorithm to spatial data, named S-C4.5, inspired by the SCART and spatial ID3 algorithms and the adoption of the Spatial Join Index. Our work concerns both data organization and the algorithm adaptation. Decision tree construction was experimented on traffic accident dataset and benchmarked on both computation time and memory consumption according to different experimentations: study of phenomenon by a single and then by multiple other phenomena, including one or more spatial relations. Different approaches used show compromised and balanced results between memory usage and computation time.

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