Volume 10, Number 3 (April 2023):4479-4494, doi:10.15243/jdmlm.2023.103.4479 ISSN: 2339-076X (p); 2502-2458 (e), www.jdmlm.ub.ac.id
Open Access 4479 Research Article
Developing landslide susceptibility map using Artificial Neural Network (ANN) method for mitigation of land degradation
Heni Masruroh1,2*, Amin Setyo Leksono3, Syahrul Kurniawan4, Soemarno5
1 Doctorate Program of Environmental Studies, Universitas Brawijaya, Jl. Veteran, Malang 65145, Indonesia
2 Geography Department, Universitas Negeri Malang, Jl. Semarang No. 5. Malang 65145, Indonesia
3 Biology Department, Faculty of Mathematics and Natural Science Universitas Brawijaya, Jl. Veteran, Malang 65145, Indonesia
4 Soil Department, Faculty of Agriculture, Universitas Brawijaya, Jl. Veteran, Malang 65145, Indonesia
5 Postgraduate Program, Universitas Brawijaya, Jl. Veteran, Malang 65145, Indonesia
*corresponding author: [email protected], [email protected]
Abstract Article history:
Received 3 December 2022 Accepted 11 January 2023 Published 1 April 2023
Landslides are one of the crucial problems that have an impact on land degradation and human life. This study aimed to develop vulnerability maps using ANN to mitigate land degradation in the Bromo Tengger Semeru with the extending area of Universal Transverse Mercator (UTM) Coordinate System Top 91277639, Bottom 911569, Left 692860, and Right 706860. The method applied the Artificial Neural Network (ANN) model using RStudio machine learning. Landslides were mapped using Sentinel Image and Orthomozaic photo interpretation from data acquisition using Unmanned Aerial Vehicle (UAV). The landslide control factor data was obtained through DEMNAS (National Digital Elevation Model) with a spatial resolution of 8 meters. Data normalisation was conducted using the Mix-Max method before it was processed using RStudio. The landslide existing for ANN workflow was processed using the Bioclim model. The results showed landslide susceptibility was categorised into four classes i.e., low susceptibility (29.83%), which was spatially spread on most in the lower slopes, moderate susceptibility (3.11%), high susceptibility (2.99%), and very high susceptibility (15.94) which is scattered on the upper slope to the middle slope of the watershed. The most significant factor influencing the landslide is the topography factor, with a Relative Importance (RI) value of 0.86; the hydrological factor, with an RI of 0.833 and the surface feature, with an RI of 0.355. The results of the landslide susceptibility model are very proper for land degradation mitigation strategies. It has high accuracy through an Area Under Curve (AUC) of 0.965 and a Precision Recall Curve (PRC) of 0.976.
Keywords:
artificial neural network landslide susceptibility remote sensing data RStudio
To cite this article: Masruroh, H., Leksono, A.S., Kurniawan, S. and Soemarno. 2023. Developing landslide susceptibility map using Artificial Neural Network (ANN) method for mitigation of land degradation. Journal of Degraded and Mining Lands Management 10(3):4479-4494, doi:10.15243/jdmlm.2023.103.4479.
Introduction
Land degradation is one of the problems in several countries that have potential disasters (Chalise et al., 2019; Kirui et al., 2021). Currently, 25% of the total land area in the world has experienced land degradation. Meanwhile, 3.2 billion people are directly
affected by land degradation (Chasek, 2022). Food insecurity, climate change, environmental damage and biodiversity also impact land degradation. The occurrence of hydro-meteorological and tectonic disasters will have an impact on the redistribution of soil layers. One of the hydro-meteorological disasters that impact land conditions is landslides. Landslides
Open Access 4480 are the movement of soil mass movements that
descends the slope, so the process of landslides produces a distinctive surface appearance change (Bièvre et al., 2016). The landslide that occurred on the foot of volcanic foot and thick land, primarily on the island of Java left scars in typical surface morphology.
Landslides gravitate, distribute soil mass and change the position of the soil layer, which generally has different characteristics and potential use. The results of land redistribution due to landslides can be observed in three slope zones from the highest to the lowest elevation. As a result, this process causes a decrease in land quality or land degradation. Over time, land that has experienced degradation can impact decreasing vegetation productivity.
At present, disaster phenomenon is a matter of state -ranking. According to UNDRR (2021), there are 13 natural disasters besides the pandemic disaster that occurred in the world in 2020. A landslide is a third place that is very common and harms human life. Data from UNDRR (2021) showed that based on the percentage of disaster events, the rate of landslides in 2021 reached 6% of the total 313 natural disasters that occurred, the number of people who died was 5% of the entire 15,082 people, had an impact on the population density of 1% of a total of 98,996,652 and 0.20% causing world economic losses from $173,133 billion.
Indonesia is one island passed by active tectonic plates (Burton and Wood, 2010). Indonesia's geomorphological conditions cause Indonesia to have a variety of morphology and high rainfall (Zaki et al., 2021). Indonesia's geographical conditions have an impact on the increased potential for disasters.
Landslides are one of the most common disasters in Indonesia (Hidayat et al., 2019; Muntohar et al., 2021).
Java island is one area with a high potential for landslide (Zamroni et al., 2020). The island of Java has a very varied topography and many volcanoes, so sometimes volcanic activity can trigger landslides and produce more erodible surface material. East Java, as part of the island of Java has massive potential for landslides (Nisa et al., 2019; Putra et al., 2021).
Reducing the risk of landslides requires a strategy focusing on preventive efforts, one of which is mapping landslides. A map of landslide vulnerability can be used as a basis for future land management, strategies, and effective ways to reduce risk. One uses spatial technology and remote sensing data (Metternicht et al., 2010).
The integration of Geographic Information Systems (GIS), remote sensing data, and the application of empirical models use machine learning such as Artificial Neural Networks (ANN). It is a technique that researchers widely use to develop landslide vulnerability maps (Biswajeet and Saro, 2007; Abbaszadeh Shahri et al., 2019; Bragagnolo et al., 2020). ANN is a model that imitates a neuron framework system and is widely used to solve classification problems, pattern recognition, and
predictions. Certain mathematical equations are integrated into the ANN model to build a method that can predict the outcome of the input values that have not been used in the modelling process. Landslide vulnerability models based on statistical and machine learning play an important role in reducing overestimation so that the model can distinguish between low and high susceptibility. It will undoubtedly be different if in the development of a landslide vulnerability map using the heuristic method because of the application of the heuristic method for the development of an expert-based landslide vulnerability, so it is often overestimated (Gessler et al., 2006; Francipane et al., 2014)
The application of statistical methods and machine learning in the development of landslide vulnerability is strongly influenced by the variables used, so this study explored the landslide determining factor and the application of the Bioclim model as a sampling technique. The statistical model is the most widely used in developing landslide vulnerability (Conforti, 2014). Through the statistical method, essential factors that affect the landslide can be obtained, but most of the sampling does not consider the data training set as a test of accuracy. Most model accuracy tests only assess landslides with random sampling techniques (Hong et al., 2019; Gameiro et al., 2021). Using random sampling can cause vulnerability to overestimate landslides and may be areas with low to high susceptibility. Incorrect sampling selection can reduce the quality of data set training and affect the accuracy of the vulnerability maps. The mapping of landslides has now integrated geographic information systems (GIS) and remote sensing by applying statistical methods with the Fuzzy logic model, regression, weight of evidence, and other statistical methods. Although statistical methods have a high accuracy value compared to the heuristic method, the statistical approach does not use training data before data processing. Training data is crucial in producing high-accuracy hazard maps (Hong et al., 2019). ANN application for landslide vulnerability has a data training step used to ensure that the landslide area data used for data input in ANN workflow is the correct data. Gede watershed was chosen as a study area to achieve this goal because of many experienced landslides before.
This study aimed to produce a landslide susceptibility map using the ANN method, which can be used as a mitigation effort for land degradation to reduce landslide risk, especially in hilly areas with agricultural land use.
Materials and Methods Research site
Pre-research, data collection and analysis were conducted for 10 months from January to October 2022. The research location is in the Gede watershed, part of the Bromo Tengger Semeru area (BTS). It is
Open Access 4481 a volcanic area stretching from the UTM coordinate
system above 91277639, under 9115639, left 692860 and right 706860 (Figure 1). Bromo and Tengger volcanic ash deposits are produced from eruptions during volcanic bodies forming very thick soil in the zone of the volcanic foot slopes. So, the geological formation consists of volcanic activity on a young quarter volcano and an old quarter volcano. The surface lithology of this area consists of andesite, basal, and most of the volcanic tuff. The geological
structure of this area produces rocks that are not dense and do not have a strong grain bond, so it is easily eroded. Very thick soil with a high elevation and steep slope makes the slopes of volcanic foot prone to landslides. The landslide that occurred on the foot of volcanic foot and thick land, primarily on the island of Java, left scars in typical surface morphology.
Landslides gravitate distributed soil mass and change the position of the soil layer, which generally has different characteristics and potential use.
Figure 1. Research area.
ANN method for developing landslide susceptibility index (LSI)
The LSI is produced through several stages, namely preparing data on the causes of landslides, pre- processing DEMNAS data, processing data for each factor causing landslides, and processing LSI using ANN. One of the important parts of generating LSI using ANN is the training of landslide data samples because the ANN work system has an architectural model consisting of input layers (i.e., landslide control factors), hidden layers (learning processes), and output layers (landslide vulnerability), and added bias values to hidden and outputs that function the same as intercepts in the regression model (Figure 2). The ANN model is flexible because it can identify the problem of linear and non-linear relationships in data and has a single or compound network (Nhu et al., 2020; Pijanowski et al., 2002). Broadly speaking, the initial weight is determined in each input, multiplied, added up, and the non-linear activation function is used to build results (Lee et al., 2003). The sample data used in LSI processing using ANN must match the
land where landslides occurred and land that did not occur, as represented by the Bioclim model. Applied Bioclim model to consider data training sets. The Bioclim algorithm is widely used for modelling species distribution by calculating the similarity of locations by comparing environmental variables in each area with the distribution of the percentage of the value of the event in the known location, i.e. training data (Carvalho et al., 2015). This model uses controlling factors such as weather conditions to determine the area’s suitability of the site in the species. In this study, we used RStudio software to run Bioclim models (RStudio Team, 2020). Several landslide control factors are regulated as environmental variables, and an evaluation of the Bioclim model is carried out based on prediction data and the actual data of the landslide point. Area Sampling Space (i.e., High-and-Susceptibility) for non-landslide is determined based on the threshold value in which the amount of sensitivity and specificity is the highest. This modelling process uses Dismo Packages available at CRAN R-Project (Hijmans et al., 2011).
Indonesia
Indonesia
Java Island
Mt. Bromo
Mt. Semeru
Open Access 4482 Several factors, such as topographical, hydrological,
anthropogenic, and geological factors, were prepared for landslide hazard modelling. Topographic sub- factors, i.e. aspect, elevation, topographic ruggedness index (TRI), plan curvature, profile curvature, total curvature, and slope, were produced in SAGA GIS v7.8.2 and ArcGIS v10.5 software. Topography factors were derived from digital elevation model data, i.e. DEMNAS. This data is the result of the combined transformation of IFSAR (5 m), TERRASAR-X (5m), and ALOS-PALSAR (11.25 m) by adding stereo- plotting mass point data so that DEMNAS data processing results have a resolution of 8 meters (BIG, 2008). The processing flow of input-output is known
as feed-forward. In addition, Back-Propagation Neural Network (BPNN) aims to update the weight value in the output, hidden, bias, and input layers until it reaches the smallest error iteratively. BPNN is one of the algorithms in the ANN model that is popular because the learning process uses a gradient-descent technique to get a minimum error (Dou et al., 2015).
The sigmoid function is generally used as the activation function (i.e., linear or non-linear) between hidden and output. Specifically, the activation function calculates the number of input weights and biases in determining whether a neuron can be activated. Then, the loss function calculates the error value between the prediction and the target data.
Figure 2. ANN model network structure that starts from data input, the learning process to landslide susceptibility index predictions.
.
Acquisition of presence and absence data
The presence and absence of data are essential parts of making a vulnerability map. Based on the results of field surveys and identification using Google Earth imagery, there were 85 landslides with translational and rotational types. Avalanche identification with FUFS by applying on-screen digitalisation key interpretation.
Landslide areas were identified from hue, colour, size and arrangement with the surrounding area. In more detailed identification, the identification of landslides using Google Earth imagery determined the activity and the type of landslide (Figures 3 and 4).
Absence data also plays an important role in mapping
landslide susceptibility using statistical methods. With absent data training, it can reduce the overestimation of the landslide susceptibility map.
Data analysis
Normalisation of landslide control factor data Data normalisation at the input layer was carried out to transform values in the data into a range of 0 to 1. The effect of data normalisation is that it can increase the accuracy of predictions in the ANN model. According to research results normalisation of data by (Ogasawara et al., 2010). The BPNN model is sensitive to the normalisation method used for input vector data.
Open Access 4483 Figure 3. Translational landslide identification using imagery.
Figure 4. Rotational landslide identification using imagery.
The data normalisation method used was min-max because it can improve accuracy and speed up network convergence (Nawi et al., 2013) with the following equation:
Y = 0.8 ∗ X − X
X − X + 0.1
Bioclim model
The Bioclim algorithm is widely used for modelling species distribution by calculating site similarity by comparing environmental variables at each site with the percentile distribution of known site occurrence values, i.e., training data (Nix, 1986; Busby, 1991;
Carvalho et al., 2015). This model uses controlling factors such as weather conditions to determine the suitability of areas within a species based on the range of the appropriate percentile distribution. If the value
of each variable is in the proper range, then the model shows the right location for that species. In a sense, the location is suitable for the species when the value is close to the 50th percentile (Hong et al., 2019). The tail values of the distributions at the 10th percentile and 90th percentile are assumed to be the same for this model (Hijmans and Graham, 2006). This research used the RStudio software to run the Bioclim model (RStudio Team, 2020). Several landslide control factors were set as environmental variables, and an evaluation of the Bioclim model was carried out based on predicted data and actual data on landslide points. The area sampling space (i.e., high- and low-susceptibility) for non- landslides was determined by taking the threshold value at which the sum of the sensitivity and specificity is highest. This modelling process used the Dismo packages available on the CRAN r-project (Hijmans et al., 2011).
Landslide in research area can be interpretation using 9 key of imagery interpretation i.e:
Landslide body : A little bright Colour : Brown
Land use : Arrangement with corn plants
Vegetation height : around 5-10 meter and does not show a shadows Perimeter : Curve
Texture Landslide Body: Rough Colour: Brown
Arrangement: River, upper landslide has vegetation i.e corn
Colour : Brown
Arrangement : Arrangement: The direction of the landslide faces the road because there is a slope cutting, the top of the landslide there is a corn vegetation, on the foot of the landslide. There are a sheet erosion with curve form
Landslide Body : Brown
Colour : Brown Texture : Rough Arrangement : Near with river Land use : Mixed Garden
Open Access 4484 Implementation of the landslide vulnerability model
The landslide hazard model was built based on landslide inventory data from the temporal interpretation of Google Earth and non-landslide samples determined through the sampling space using the Bioclim model. The presence/absence of landslide data, as much as 70% of the total data was divided for training the landslide hazard model data and 30% for evaluating the model's predictive ability. Landslide
vulnerability was predicted using training data as the dependent variable and 17 landslide control factors as independent variables. The number of hidden neurons, as much as 11 was set through a trial-and-error process 10 times because the initial weight values are different in each building of a diverse network model (Dou et al., 2015; Bachri et al., 2021). There are several stages in developing an avalanche vulnerability map with ANN. Figure 5 shows the flowchart of the steps in the research activity.
Figure 5. Research flowchart.
LSI performance with ANN
The results of the landslide susceptibility model are evaluated using testing data. The accuracy of the model was tested using the receiver operating characteristics (ROC) curve, the area under the curve (AUC), and the precision-recall curve. Generally, the ROC curve is often used to evaluate the performance of various models (Fawcett, 2006). The AUC value measures how well the model can differentiate between classes. Meanwhile, the precision-recall curve can provide additional information about reliable model performance when paired with the ROC
curve (Davis and Goadrich, 2006). The following are the performance metrics used to evaluate the performance of the landslide hazard model:
1 − Specificity = FP FP + TN Sensitivity = TP
TP + FN Precision = TP
TP + FP Recall = TP
TP + FN
Open Access 4485 where: FP, FN, TP, and TN are false positives and false
negatives are the number of pixels that are incorrectly classified; in contrast, true positives and true negatives are the numbers of correctly classified pixels.
Results and Discussion Landslide conditioning factors
Aspect is the direction of the slope and is one of the main factors for landslides because it is related to precipitation, sun exposure, humidity, vegetation cover, and soil permeability (Figure 6a). However, the aspect is not always the main factor because it depends on the type of landslide, deposit material (Capitani et al., 2013) and the influence of other geo- environmental factors. The elevation is a critical factor for the analysis of capturing surface morphological structure information (Figure 6b). In addition, elevation changes affect precipitation patterns, temperature, and vegetation cover in an area. Elevation can also be linked to human intervention to change morphological structures, such as cutting slopes for roads and vegetation, which can cause landslides (Lineback Gritzner et al., 2001). TRI (Figure 6c) is defined as the mean difference between the central and peripheral pixels (Wilson et al., 2007), and measures the local relief (Różycka et al., 2017), which can trigger shallow landslides (Donnarumma et al., 2013).
The curvature factor (Figure 6d-f) is a sloped shape that can trigger slope stability caused by softening of the material due to the accumulation of water, especially concave slopes (He et al., 2019). The slope angle (Figure 6g) is a key parameter in hazard estimation for developing earth flows (Donnarumma et al., 2013).
Hydrological factors are considered in this study as the key to controlling landslide events. Rivers can stimulate mass movement, which can stabilise slopes through erosion and saturation of soil material, especially by the river density factor (Dehnavi et al., 2015). Rainfall data in the study area can be limited due to the lack of locations for rain gauges, so spatial downscaling analysis is performed as an alternative (Chen et al., 2019) to construct a sensor-based rainfall map (Figure 7c). The image used is TerraClimate (i.e., precipitation data) monthly averages over ten years acquired from the Climate Engine web (https://app.climateengine.com/climateEngine ClimateEngine, 2014), and Sentinel-2 for extracting the vegetation index from the Copernicus Hub web by
the European Space Agency
(https://scihub.copernicus.eu/ (European Space Agency, 2015) and DEMNAS for elevation variables.
Rain factor plays an important role in triggering slope instability due to high pore water pressure during periods of heavy rain. Then, the topographic wetness index (TWI) and stream power index (SPI) is derived from DEM data which refer to the accumulation and strength of water flows, as well as erosivity on the
surface that triggers mass movement (Figures 7d and 7e).
Apart from topographical and hydrological factors, human intervention (i.e., anthropogenic factors) also plays an important role in landslide events. The normalised difference vegetation index (NDVI) is representative of land use, which means that artificial structures can exert more pressure on the surface and thereby cause displacement of the surrounding surface (Figure 8a). In addition, NDVI also describes the condition of vegetation density and aboveground biomass, which significantly affects landslide events (Viet et al., 2016; Jacquemart and Tiampo, 2021). NDVI extracted from Sentinel-2 images. Road construction can directly or indirectly trigger landslides due to slope modification factors that disrupt slope stability (Kavzoglu et al., 2014; Bachri et al., 2020). Road vector data is also acquired from Digital Topographical Maps. Distance to the road (Figure 8b) and road density (Figure 8c) are constructed similarly on the river factor map.
Information on geological units (i.e., geo-units) and major structures (i.e., faults) extracted from geological maps based on sensory interpretation by the Geological Agency from the Geological Survey Center (Badan Geologi, 2013). Geology (Figure 8d) is used in landslide hazard modelling because different geological units provide different levels of vulnerability (Moharrami et al., 2020). Likewise, geological faults (Figure 8e) may trigger landslides due to fracturing and shearing factors (Bui et al., 2011), as well as soil and rock material discontinuities, so the slopes are unstable.
Bioclim model for determination of non-landslide samples
Statistical and machine learning-based landslide vulnerability models play an essential role in reducing overestimates so that the model can distinguish between low-susceptibility and high-susceptibility. In fact, non-landslide locations cannot be determined directly. In general, non-landslides are determined using random sampling under conditions where it is not known whether the sites taken are in a high- or low- susceptibility area. Hong et al. (2019) reported that determining the range of sampling space and sample size on non-landslides can affect the accuracy of landslide hazards. The Bioclim model was used in this study to create a sampling space as an indication for determining non-landslide locations (Figure 3). If the sampling is done randomly, then the absence of landslide data can only be done randomly, so the sampling of the absence of landslide data does not follow areas with high vulnerability. In the Bioclim method, the absence of landslide data and the presence of landslide data are translated into a range of 0, and 1 obtained from point multi-value extraction using Arc GIS 10.3 and the results of value extraction are entered through RStudio machine learning.
Open Access 4486 Figure 6. Topographic factors, i.e. a) aspect, b) elevation, c) topographic ruggedness index, d) plan curvature, e)
profile curvature, f) total curvature, and g) slope.
a b
c d
e f
g
Open Access 4487 Figure 7. Hydrological factors, i.e. a) distance to stream, b) stream density, c) rainfall, d) topographic wetness
index, dan e) stream power index.
An area with a value of 0 (green) is a random sampling area for the absence of landslide data, while a value of 1 (orange colour) is a sampling area for landslide presence data. The results of the values 0 and 1 are used as a basis for making a sampling space using the package-Bioclim RStudio. Training data for a sample of 70% was taken in areas at high vulnerability. Until now, no clear reference can be used as training data (Hong et al., 2019). The sampling technique is a significant and crucial part of creating and developing landslide hazard maps because inappropriate sampling can impact the accuracy of the modelling results. The
use of training data in making hazard maps is a very important part because the concept of using ANN in vulnerability development is that the system is taught to practice the character of each input factor. The results of Bioclim modeling with training data (Figure 9) were followed by Artificial Neural Network (ANN) processing using R-studio.
Landslide Susceptibility Index (LSI)
The landslide hazard in the Gede watershed is calculated and analysed using the Artificial Neural Network (ANN) method using an algorithmic formula.
a b
c d
e
Open Access 4488 This hazard map is constructed using input factors
from topographical, hydrological, anthropogenic and geological factors. There are 136 landslide inventories obtained using data acquisition from Google Earth Imagery in multi-temporal. Each factor inputted for vulnerability does not show a positive value. It means that the input of these factors does not increase landslide vulnerability. Some input factors that show
positive values are TRI, Rainfall, elevation, curvature profile and distance to the fault. TRI gives the greatest value because the topography in the study area is very varied. In more detail, the landslides in the study area occur in micro-micro topography, which generally has the same slope, so in this study, the slope factor input shows a negative value; namely, the slope value has no impact on increasing landslide susceptibility.
Figure 8. Anthropogenic factors, i.e. a) normalised difference vegetation index, b) road distance, c) road density, geology factor, i.e., d) distance to faults, and e) geological units.
a b
c d
e
Open Access 4489 Figure 9. Sampling space for determining non-landslide locations based on the Bioclim model.
The TRI value in this study lies in the range of <3.61 to >12.48. The higher the TRI value indicates, the rougher the topography of the area. Most of the landslides with a high vulnerability index are at a TRI
>12.48. Rainfall in this study also shows a positive value; a high level of landslide vulnerability occurs in rainfall >300, located on the upper slope of the watershed. The shape of the slope represented through the plan curvature in this study also provides a positive input value, so an increase in the plan curvature value impacts the value of landslide vulnerability. Plan curvature values represent concave to convex slope shapes. Most of the landslides occurred in the form of concave slopes, which indicate post-slide events. Plan curvature values (-4.38) to (3.83). A minus value on the plan curvature indicates a concave slope shape, while a positive value indicates a convex slope. The influence of the plan and curvature profile is the speed up and slow down of the water flow, which influences the water's ability to run off or be properly infiltrated.
Stream Power Index (SPI) affects the erosion potential of streams at specific points on the surface and controls the potential erosion power of flowing water. Massive soil erosion describes a geomorphological process and directly impacts landslide potential. In this study, an increase in SPI was followed by an increase in landslide susceptibility.
SPI value (-1.12) to >0.65. Most of the landslides occurred at SPI values of 0.24-0.65. Based on Figure 10, it shows that 15.94% of the study area is in a very high landslide vulnerability class which is located on the slopes that have a rough topography. This is, of course, by the relative importance factor, which indicates that the TRI in this study shows the highest value, so an increase in the TRI value impacts the vulnerability of landslides in the study area.
Figure. 10 shows several points of landslide occurrence in the study area. The typology of landslides in the study area is active rotational landslides (9a, 9b, 9e), namely landslides that have the shape of a curvy body and are mostly associated with vegetation in the form of corn which is located at the top of the existing landslide. The rotational landslides in the study area leave a unique surface morphology divided into residual, depletion, and accumulation zones. The existence of new morphological formations due to landslides will cause changes in soil quality and fertility. Some landslides in the study area occurred up to 3 meters depth, so in the depletion zone, this condition causes a decrease in the quality of soil fertility. Translational active landslides were also found in the study area (9c, 9d, 9f) which are associated with roads because most translational landslides are initiated by cutting slopes used for roads. The types of vegetation found around the existing translational landslides are grass and wild flower-type plants.
Based on research conducted in 2020, which compared the rate of soil loss in several vegetations, it shows that vegetation differences will impact sand fraction, silt fraction, clay fraction, permeability, and bulk density (Muddarisna et al., 2021). The texture in the research area shows silty loam, while the structure is granular. The percentage content of each soil fraction, permeability, and bulk density in various vegetation will have an impact on the potential movement of soil material. Thus, in the conservation of landslide-prone land in the study area, it is necessary to consider the vegetation aspect because it relates to the root system, canopy, Index of Root Anchoring (IRA), and Index of Root Binding (IRB) (Hairiah et al., 2020).
Open Access 4490 Figure 10. Landslide hazard map using ANN.
Landslide hazard mapping provides information spatially on areas with high, medium, to low vulnerability. As a consequence, the treatment in land management for each vulnerability class is different.
Based on findings in the field, there are differences in community treatment in mitigation for rotational and translational type landslides. Most of the community optimises the former landslide bodies in the depletion zone for planting mixed gardens with types of plants such as coffee, chillies, and eggplants. one mitigation measure. For the former landslides of the translational type, most of the people of the Gede watershed do not use them for certain crops because they have a steep slope of more than 31o, while for rotational type landslides, the maximum occurs at a slope of 280. The difference in slope for different landslides causes differences in landslide mitigation efforts.
Correlation analysis and importance of the variables The input factor to make landslide hazards must be evaluated because it is very possible that the quality of the landslide control factors can influence the prediction of landslide vulnerability. In this study, an analysis of the landslide control factors that have a significant influence on landslides was carried out.
Correlation analysis determines the effect of each landslide control factor on landslide vulnerability. The contribution of each controlling factor was evaluated using the Garson method and the olden (search citation) method. Eleven variables were analysed.
Based on the results of the study, it shows that the minimum and maximum values are (-0.421) and (0.836).
The correlation analysis shows positive and negative values. A positive value in the correlation
analysis indicates that the input value (controlling factor) increases, and the output value (vulnerability index) will also increase. A positive input value that is close to 1 indicates that the input value has a powerful correlation with landslides. If the input value is negative, an increase in the input value causes a decrease in the vulnerability index value. Thus, positive input values are factors that influence vulnerability. Figure 11 shows the correlation weight of the overall landslide control factors obtained using the Olden method. Based on the correlation value, Figure 11 shows that the positive input values of the landslide control factors are TRI, rainfall, elevation, profile curvature, and distance to the fault. The highest positive value and close to 1 is TRI, with a value of 0.836. It shows that TRI has the most significant influence on the intensity of landslide vulnerability. As for the negative values, the input factors are total curvature, aspect, stream density, distance to road, TWI, NDVI, slope, Geological Unit, road density and distance to the river. The lowest negative value that does not affect landslide vulnerability linearly is the distance to the river, with a value of (-0.421).
Analysis of accuracy
Many studies have been conducted on making landslide hazard maps using the ANN method (Abbaszadeh Shahri et al., 2019; Amato et al., 2021).
The study results showed that ANN is an appropriate and effective method for producing landslide hazard maps (Bragagnolo et al., 2020). Research results of Conforti et al. (2014) showed that landslide vulnerability using ANN has an accuracy of 85%, while several other studies show an accuracy range of 73%-93% (Lee et al., 2018; Gameiro et al., 2021).
Open Access 4491 This study has applied the Bioclim method for data
training before processing in the ANN workflow stage so that the existing landslide data is adequately categorised. The use of the Bioclim method affected the accuracy value in this study. Accuracy is an
essential part of the development of landslide hazard models. The accuracy value will indicate how much the acceptable landslide vulnerability map is. Based on Figure 12, the model accuracy can be well received if the accuracy value shows more than 0.85.
Figure 11. Correlation between avalanche trigger variables.
Figure 12. Accuracy of landslide hazard map.
This study showed the accuracy test through the Receiver Operating Characteristic (ROC) curve. The ROC curve is a dot plot showing the tradeoff level accuracy between the TP (True Positive) and FP (False Positive) levels. The TP rate is generally referred to as the sensitivity and (1-FP rate) the specificity. The
sensitivity value of a model is obtained from a division between values tested correctly between the model and the conditions in the field and the values in the model that are not prone but are in a prone field. The higher sensitivity value means the susceptibility model is in accordance with reality. While the specificity shows Relative Importance
Variables
Open Access 4492 that the model's invulnerability corresponds to the
actual conditions' invulnerability. The results of the ROC analysis are realised in the Area Under Curve (AUC), which shows a value of 0.965. The AUC value closer to 1 indicates that the model has good accuracy.
Conclusion
An essential part of the development of the landslide hazard model is the strategy of determining the sample because the determination of the sample affects the accuracy of the hazard model. Sampling strategy with the Bioclim model that considers the sampling space area. The sampling space area (i.e., high- and low- susceptibility) for non-landslides will affect the sensitivity and specificity values of the model. Based on the results of the landslide presence sample, which was then analysed with input factors in the form of topography, anthropogenic and hydrology using RStudio machine learning, it produced a low vulnerability value of 29.8%, moderate 3.11%, high 2.99% and very high 15.94%. The accuracy results for the vulnerability model are represented by the Area Under Curve (AUC) value of 0.965 and the precision recall of 0.976.
Acknowledgement
The authors thank all the field teams who have assisted in acquiring research data.
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