Publications

@article{Vatresia2023b,

title = {Automatic image segmentation model for indirect land use change with deep convolutional neural network},

author = {Arie Vatresia and Ferzha Utama and Nanang Sugianto and Astri Widyastiti and Rendra Rais and Rido Ismanto},

doi = {10.1007/s41324-023-00560-y},

issn = {2366-3294},

year  = {2023},

date = {2023-11-15},

journal = {Spat. Inf. Res.},

publisher = {Springer Science and Business Media LLC},

keywords = {Artificial Intelligence, Computer Science Applications, Computers in Earth Sciences, Geography},

pubstate = {published},

tppubtype = {article}

}

@article{Ismanto2023,

title = {Development of Flood-Hazard-Mapping Model Using Random Forest and Frequency Ratio in Sumedang Regency, West Java, Indonesia},

author = {Rido Dwi Ismanto and Hana Listi Fitriana and Johanes Manalu and Alvian Aji Purboyo and Indah Prasasti},

doi = {10.7494/geom.2023.17.6.129},

issn = {2300-7095},

year  = {2023},

date = {2023-10-13},

journal = {GaEE},

volume = {17},

number = {6},

pages = {129--157},

publisher = {AGHU University of Science and Technology Press},

abstract = {Flooding, often triggered by heavy rainfall, is a common natural disaster in Indonesia, and is the third most common type of disaster in Sumedang Regency. Hence, flood-susceptibility mapping is essential for flood management. The primary challenge in this lies in the complex, non-linear relationships between indices and risk levels. To address this, the application of random forest (RF) and frequency ratio (FR) methods has been explored. Ten flood-conditioning factors were determined from the references: the distance from a river, elevation, geology, geomorphology, lithology, land use/land cover, rainfall, slope, soil type, and topographic wetness index (TWI). The 35 flood locations from the flood-inventory map were selected, and the remaining 18 flood locations were used for justifying the outcomes. The flooded areas from the RF model were 28.39%; the rest (71.61%) were non-flooded areas. Also, the flooded areas from the FR method were 8.02%, and the non-flooded areas were 91.98%. The AUC for both methods was a similar value – 83.0%. This result is quite accurate and can be used by policymakers to prevent and manage future flooding in the Sumedang area. These results can also be used as materials for updating existing flood-susceptibility maps.},

keywords = {Computer Science (miscellaneous), Computers in Earth Sciences, Earth-Surface Processes, Environmental Engineering, Geography},

pubstate = {published},

tppubtype = {article}

}