Measuring urban poverty using multi-source data and a random forest algorithm: A case study in Guangzhou

2020 ◽  
Vol 54 ◽  
pp. 102014 ◽  
Author(s):  
Tong Niu ◽  
Yimin Chen ◽  
Yuan Yuan
Water Policy ◽  
2021 ◽  
Author(s):  
Xiang Gao ◽  
Ke Wang ◽  
Kevin Lo ◽  
Ruiyang Wen ◽  
Xingxing Huang ◽  
...  

Abstract This study proposes a random forest algorithm to evaluate water poverty. It shows how the machine learning technique can be used to classify the degree of water poverty into five levels: very severe, severe, moderate, mild, and very mild. The strengths of the proposed random forest method include a high classification accuracy, good operational efficiency, and the ability to handle high-dimensional datasets. The success of the proposed method is empirically illustrated through a case study in Gansu, Northwest China. The analysis shows that from 2000 to 2017, the severity of water poverty in the study area declined. In 2000, most municipalities were classified as level 1 (very severe) or level 2 (severe). In 2017, level 1 water poverty disappeared, with most municipalities classified in as level 3 (moderate) and level 4 (mild). Spatially, there is a significant difference between the water poverty levels of the western, central, and eastern parts of Gansu, and the eastern part is affected by serious water poverty problems.


2021 ◽  
Vol 936 (1) ◽  
pp. 012015
Author(s):  
S Sukristiyanti ◽  
K Wikantika ◽  
I A Sadisun ◽  
L F Yayusman ◽  
E Soebowo

Abstract Landslide susceptibility mapping is an initial measure in the landslide hazard mitigation. This study aims to evaluate landslide susceptibility in the Cisangkuy Sub-watershed, a part of Bandung Basin. Twenty-seven landslide variables were involved in this modeling derived from various data sources. As a target, 25 landslide polygons obtained through a visual interpretation of Google Earth timeseries images and 33 landslide points obtained from a field survey and an official landslide report, were used as landslide inventory data. All spatial data were prepared in the same cell size referring to the highest spatial resolution of data involved in this modeling, i.e., 8.34 m. Fifty-eight (58) landslide locations covering an area of 0.87 Ha are equivalent to 1040 cells in the raster format. In total, 2040 samples consisting of landslides and non-landslides with the same ratio, were trained using random forest algorithm. Non-landslides were sampled randomly from landslide-free cells. This modeling was executed using R environment. In this study, the result was two labels, susceptible and non-susceptible. This model provided an excellent performance, its accuracy reached 98.56%. This research needs an improvement to provide a probability that has a range of 0 to 1 to show the level of landslide susceptibility.


2021 ◽  
Vol 9 (3) ◽  
pp. 267
Author(s):  
Vanesa Mateo-Pérez ◽  
Marina Corral-Bobadilla ◽  
Francisco Ortega-Fernández ◽  
Vicente Rodríguez-Montequín

One of the fundamental tasks in the maintenance of port operations is periodic dredging. These dredging operations facilitate the elimination of sediments that the coastal dynamics introduce. Dredging operations are increasingly restrictive and costly due to environmental requirements. Understanding the condition of the seabed before and after dredging is essential. In addition, determining how the seabed has behaved in recent years is important to consider when planning future dredging operations. In order to analyze the behavior of sediment transport and the changes to the seabed due to sedimentation, studies of littoral dynamics are conducted to model the deposition of sediments. Another methodology that could be used to analyze the real behavior of sediments would be to study and compare port bathymetries collected periodically. The problem with this methodology is that it requires numerous bathymetric surveys to produce a sufficiently significant analysis. This study provides an effective solution for obtaining a dense time series of bathymetry mapping using satellite data, and enables the past behavior of the seabed to be examined. The methodology proposed in this work uses Sentinel-2A (10 m resolution) satellite images to obtain historical bathymetric series by the development of a random forest algorithm. From these historical bathymetric series, it is possible to determine how the seabed has behaved and how the entry of sediments into the study area occurs. This methodology is applied in the Port of Luarca (Principality of Asturias), obtaining satellite images and extracting successive bathymetry mapping utilizing the random forest algorithm. This work reveals how once the dock was dredged, the sediments were redeposited and the seabed recovered its level prior to dredging in less than 2 months.


2014 ◽  
Vol 152 ◽  
pp. 291-301 ◽  
Author(s):  
Weitao Chen ◽  
Xianju Li ◽  
Yanxin Wang ◽  
Gang Chen ◽  
Shengwei Liu

2017 ◽  
Vol 4 (1) ◽  
pp. 82 ◽  
Author(s):  
Noezafri Amar

This research was aimed at describing the accuracy level of Google Translate especially in translating English text into Indonesian based on language error analysis and the use of equivalence strategy. The data were collected by taking one paragraph from Johann Gottfried Herder’s Selected Writings on Aesthetics book as the source text. Then they were translated by Google Translate (GT). The data of GT translation were analyzed by comparing them with the measurement instrument of translation equivalence level and elaborating the equivalence strategy of GT. By doing so the language errors were seen thus the accuracy level of GT translation could be described. The result of this research showed that (1) out of 13 source data only 4 or 31% are accurate translation, 7 or 54% are less accurate translation, and 2 or 15% are inaccurate translation. Therefore it is implied that its reliability for accurate level is only 31%. Half of them is less understandable and a few are not understandable. (2) If the appropriate equivalence translation strategy is sufficiently transposition and literal, GT can produce an accurate translation. (3) If the appropriate equivalence translation strategy is combined strategy between transposition and modulation or descriptive, more difficult strategies, GT just produce less accurate translation because it kept using literal and transposition strategies. (4) But if the appropriate equivalence translation strategy is only modulation, GT just produce inaccurate translation which is not understandable because it can only use transposition strategy. Even if the appropriate equivalence translation strategy is just a transposition strategy, in one case, GT failed to translate and it produced inaccurate translation because its strategy is only literal. In conclusion, especially in this case study, Google Translate can only translate English source text into Indonesian correctly if the appropriate equivalence translation strategy is just literal or transposition.AbstrakPenelitian ini bertujuan untuk mendeskripsikan tingkat keakuratan Google Translate khususnya dalam menerjemahkan teks berbahasa Inggris ke dalam bahasa Indonesia berdasarkan analisis kesalahan bahasa dan penggunaan strategi pemadanan. Data dikumpulkan dengan mengambil satu paragraf dari buku Johann Gottfried Herder yang berjudul ‘Selected Writings on Aesthetics’ sebagai teks sumber. Kemudian data tersebut diterjemahkan oleh Google Translate (GT). Data terjemahan GT itu dianalisis dengan cara membandingkannya dengan instrumen pengukur tingkat kesepadanan terjemahan dan menjelaskan strategi pemadanan yang digunakan. Dengan melakukan hal tersebut kesalahan bahasanya dapat terlihat sehingga tingkat keakuratan terjemahan GT dapat dideskripsikan. Hasil penelitian ini menunjukan bahwa (1) Dari 13 data sumber hanya 4 data atau 31% yang merupakan terjemahan akurat, 7 data atau 54% merupakan terjemahan yang kurang akurat, dan 2 data atau 15% merupakan terjemahan tidak akurat. Dengan demikian tingkat kehandalannya sampai pada tingkat akurat hanya sebesar 31% saja. Sementara sekitar setengahnya lagi kurang dapat dipahami. Sedangkan sisanya tidak bisa dipahami. (2) Apabila strategi pemadanan yang seharusnya dipakai cukup transposisi dan terjemahan literal saja ternyata GT mampu menghasilkan terjemahan yang akurat. (3) Apabila strategi yang harus dipakai adalah strategi kombinasi antara transposisi dan modulasi atau deskriptif, strategi yang lebih sulit, GT hanya mampu menghasilkan terjemahan yang kurang akurat karena tetap menggunakan strategi penerjemahan literal dan transposisi saja. (4) Tetapi apabila strategi yang seharusnya dipakai hanya strategi modulasi saja GT hanya menghasilkan terjemahan tidak akurat, yang tidak bisa dipahami karena hanya mampu memakai strategi transposisi saja. Bahkan jika seharusnya strategi yang dipakai adalah sekedar transposisi, pada satu kasus, GT ternyata gagal menerjemahkan dan menghasilkan terjemahan tidak akurat karena strategi yang dipakainya adalah penerjemahan literal. Sebagai simpulan, khususnya dalam studi kasus ini, Google Translate hanya mampu menerjemahkan teks sumber berbahasa Inggris ke dalam bahasa Indonesia secara akurat jika strategi pemadanannya yang sesuai hanya sekedar literal atau transposisi.


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