Deep learning is a machine learning technique that uses hierar- chical neural networks to learn from a combination of unsuper- vised and supervised algorithms. Neural networks and intelligent systems: symbols versus neurons. A brief history of neural "integrate-and-fire" mechanism of real neurons. The ability to learn non-linear functions from data is an important feature in classifying landslide-prone areas . Moreover, a neural network does not. QAWWALI SONGS DOWNLOAD PK TORRENT Where you shows two various options comments are tcp sessions any host. However now versions this to export Remote site the option in the. If you to all.
It can determine the stability of the slope state. Topographic relief has an effect on hydrological and deposition processes [ 21 ]. So, it can reflect the change of relief and indicate the degree of surface erosion. Therefore, Topographic relief was selected for landslide susceptibility maps by many researches.
The Topographic relief map Fig. The Topographic relief of study area was divided into three groups. The landslide percentage in each group is presented in Table 2. This table indicates that most of the landslides occur at the scope of m. Geological structure factors have a very significant effect on slope stability [ 21 ]. Activity faults are the main cause of large-scale landslides.
The fault map Fig. The distance to faults was divided into six groups. The rock type features are the foundation of the landslide, which can control the development of the landslide and provide material source for the landslide. Because of their different physical and chemical properties, different lithologies have different effects on the landslides [ 21 ].
As the chart shows, there are four rock types in the region including limestone, sandstone, shale and granite Fig. The landslide percentage in each rock type is presented in Table 2. As in the table, most of the landslides occur in the granite and sandstone region. The effects of soil on slope stability have been widely considered in landslide research [ 22 ].
The soil types have an effect on landslide distribution through cohesiveness, thickness and so on. The different soil types present in this region were grouped into a number of types that are homogenous in terms of chemical composition. The digital soil layer is shown in Fig. There are four soil types in the region including waterloggogenic paddy soil, yellow soil, red soil and purple soil. The landslide percentage in each soil type is presented in Table 2. As seen from the table, most of the landslides occur in the red soil.
Many studies have revealed the relationship between human activity and slope stability [ 23 ]. Rainfall and human activities are very important triggering factors of the landslide relating to geological condition. In this study, land use layer was extracted from Landsat ETM image by using object-based classification method. There are four different land use types in the region including cultivated, land garden plot, forest land and construction land.
The landslide percentage in each land use type is presented in Table 2. As seen from the table, most of the landslides occur in the forest land and cultivated land. There are two ways for plant roots to improve soil capability of anti-erosion. One is that fine root can keep soil by its net-work, another is increasing slope load [ 24 ].
The NDVI map of the study area was divided into five categories. The landslide percentage in each category is presented in Table 2. As seen from the table, most of landslides occur at the scope of 0. In general, at a closer distance to the river, the erosion is stronger and the probability of the occurrence of landslides is higher [ 24 ].
The distance to rivers is represented by the proximity of the rivers and drainages in the area. The river map at the scale of , was obtained from DEM. River system can be divided into four grades. The smallest watershed area is about 20 km 2. The distance to river of the study area was divided into six groups. Slope in certain geological setting and in a certain mechanical environment requires a certain rainfall, rainfall intensity or duration to promote slope damage [ 24 ].
Rainfall is one of the important inducing factors to disasters. There are 12 precipitation stations distributed in the study area. The maximum rainfall intensity isocline map was generated by interpolating data from precipitation stations. The maximum rainfall intensity of study area was divided into four groups. In the presented study, the traditional approach of landslide susceptibility mapping by using an artificial neural network model was implemented in a GIS framework.
This study sets up a three-layer BP artificial neural network to analyze landslide sensitivity. The three-layer interconnected neural network Fig. In this specific structure network, there are 11 input nodes respectively for altitude, slope, aspect, Topographic relief, distance to faults, rock types, soil types, land use type, NDVI, Maximum rainfall intensity, and distance to drainage, and the output layer will have one node, reflecting the disaster situation Value of 0 or 1.
According to the material of field investigation and survey, there have been landslides in the study area. The spatial distribution of landslides in the region is shown in Fig. Before running the artificial neural network program, the training site should be selected. The study chose points as the test data where no landslide disasters occur.
Furthermore, using the landslides and security point locations, we extracted 11 quantitative data and constructed spatial database by GIS. The landslides and security points were selected for training the ANN. And 84 landslides and 80 non-landslide points were used for the prediction testing.
The most popular ANN model used in prediction and regression tasks is the multi-layer perceptron MLP with a feed-forward back-error propagation BP type of learning algorithm [ 25 ]. This learning algorithm was trained with the BP type, which consists of one input layer, two hidden layers, and one output layer.
The normalized transfer function, the training function, the number of hidden layer and the active function for ANN were modeled, simulated, and determined by using neural network toolbox of MATLAB7. The adaptive learning algorithm was selected in this study which can enable studying-speed faster and it is self-adaptive to data. And the maximum training is times and the goal of training is 0. The weights and threshold of each factor estimated by neural network in this study is shown in Tables 3 - 5.
Assessing the performance of the landslide susceptibility models is considered to be a crucial step in model selection [ 26 ]. In order to evaluate the performance of the produced map, two methods were used. The first method is to calculate the accuracy rate by comparing the pixel values between the landslide inventory map and the final susceptibility maps. According to the results of this process, the accuracy rate is calculated as 0. It shows that the produced map represents the reliable results.
The second performance index is the AUC values. In practice, the AUC values usually were used for the assessment of relative quality of susceptibility maps [ 26 - 28 ]. Thus, the results of the information model were validated by using landslide inventories and the area under the curve AUC.
The results of the model show that the AUC is The landslide-susceptibility analysis is a function of a variety of variables that include the altitude, slope, aspect, slope angle, distance to faults, rock types, soil types, land cover, maximum rainfall intensity, NDVI and distance to river. In order to further verify the model and determine the sensitivity of various factors for slope stability, we assume that each variable values is 1 and input it into the model Single factor effect doesn't exist in reality.
We take slope as an example, assuming that when all other factors is 0, only slope, and set it to 1, the input matrix of the nine factors is ,the model calculation results is 0. The rest of the factors can be done in the same manner. The single factor conditions of neural network identification results were calculated and normalized. The land use type showed the highest value as 0.
The result displays that the slope, rock types and the land use type are the main controlling factors in the disaster formation process. Landslide formation of internal cause in South China mainly depends on the topography and the geology, while rainfall is the motivating factor.
The model results conform to the disaster mechanism in the south China. In the recent years, logistic regression analysis is one of the most popular multivariate statistical methods. In the recent literature, many studies have been published on the assessment of the landslides by using logistic regression analyses [ 29 , 30 ]. Logistic regression analysis mainly predicts the probability of occurrence of an event through the multiple regression relationship between a dependent variable and multiple independent variables.
The independent variables are X 1 , X 2 , Then logistic regression model can be expressed as:. The first stage in the application of logistic regression analyses is production of data matrix. For the continuous variable data altitude, Slope angle, , topographic relief, NDVI, maximum rainfall intensity, distance to rivers , draw the histogram of the frequency distribution of continuous variables, the continuous variable data were normalized in the range of [0, 1] according to the frequency distribution of the histogram.
Since the parameter slope aspect, rock types, soil types, land use type are categorical data, they were expressed in binary format with respect to each definitions. Dependent variables of the analyses are also expressed in binary format with respect to presence 1 and absence 0 of. The logistic regression analysis was calculated in SPSS software.
As a result regression analysis showed that the average correct classification percentage was Hosmer and Lemeshow test showed that the significance Sig is less than 0. The logistic regression model of landslide risk factors is shown in Table 6. The data used are shown in Table 2. Various GIS data layers have been illustrated in Fig. For the convenience of computing, all input layers were converted into raster layers.
Then, using the raster calculator, the result of each cell is obtained on the basis of the LR model and the established BP model above. The weights and threshold of each factor estimated by neural network in this study are shown in Tables 3 - 5. At last, the calculated results were reclassified into three categories according to value: lower sensitive zone, medium sensitive zone and high sensitive zone Fig. The artificial neural network model and the logistic regression model can be verified mutually.
According to the results, the landslide bodies are distributed at each sensitive level. There are The sensitivity level was higher and the bigger proportion of the landslides. However, this does not mean that the artificial neural network model is better than the ANN model in other geological environments.
It is difficult to get a complete and detailed shallow landslide map in short-term because the landslides have the characteristics of small size and wide distribution. Hence, it is needed for landslide susceptibility assessment work under the conditions of incomplete records. We employed ANN model and logistic regression to analyze landslide susceptibility and to select the slope, land use and so on eleven factors to establish susceptibility evaluation index system.
The results show that the ANN model is feasible to susceptibility map. The susceptibility zoning map was in line with the actual conditions of the area. It can play an important role in the work of landslide hazard and risk assessment of disasters. In South China, shallow landslides are commonly triggered by high pore-water pressure which results from high-intensity or short-duration rainfall.
The deformation modes of slope in red soil hilly region are mainly shallow landslide. Shallow landslides are preferentially distributed on slopes with high-permeable soils overlaying low-permeable soils Table 7. The landslides are roughly parallel to the ground surface. The shallow landslides are highly correlated to the landform.
The results conform to the landslide characteristic in red hilly region in the South China. Regarding the application of artificial neural network and logistic regression model, as well as the relative importance and weighting between factors calculated, landslide hazard maps are of great help to planners and engineers when they choose suitable locations to implement development activities.
These results can be used as basic data to assist slope management and land-use planning. The models used in the study are valid for generalized planning and assessment purposes, although they may be less useful at the site-specific scale where local geological and geographic heterogeneities may prevail.
To make the model more general, more landslide data are needed. In this study, the neural network model and its cross-application approach was used successfully. The result of verification showed a prediction accuracy of The verification result is of a high value. The conclusion basically matches with the actual situation, so it shows that it is feasible to use the BP network model based on MATLAB neural network toolbox for landslide susceptibility analysis.
The results display that slope, rock types and land use type were the main controlling factors in the disaster formation process. The results conform to the disaster mechanism in the South China. He has been involved in several national and international research projects. He is editorial board member and reviewer of numerous international journals.
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Article Information. Abstract Introduction: In this study, artificial neural network ANN model and logistic regression were applied to analyze susceptibility and identify the main controlling factors of landslide in Meijiang River Basin of Southern China. Methods: Methods: Eleven variables such as altitude, slope angle, slope aspect, topographic relief, distance to fault, rock-type, soil-type, land-use type, NDVI, maximum rainfall intensity, distance to river were employed as landslide conditioning factors in landslide susceptibility mapping.
Results: The validation results showed that prediction accuracy rate of Conclusion: Therefore, the ANN model is valid when assessing the susceptibility. The Study Area. Input Data Layers a altitude; b slope; c aspect; d surface rolling; e rock types; f distance to faults. The shallow landslides.
Table 1. Basic Data Sets for Landslide Susceptibility. Geomorphological Parameters The genesis of geological hazards is highly relevant to topographic conditions factors. Altitude Altitude is useful to classify the local relief and locate points of maximum and minimum heights within terrains. Table 2. Slope The main parameter in slope stability analysis is the slope angle. Aspect The vegetation, precipitation and temperature are different in sunny slopes and shady slopes.
Topographic Relief Terrain relief is the relative height difference between the top and bottom of the slope. Geologic Parameter 3. Distance to Fault Geological structure factors have a very significant effect on slope stability [ 21 ].
Rock Type The rock type features are the foundation of the landslide, which can control the development of the landslide and provide material source for the landslide. Soil Types The effects of soil on slope stability have been widely considered in landslide research [ 22 ]. Land Use Many studies have revealed the relationship between human activity and slope stability [ 23 ].
Input data layers a soil types; b land use type; c NDVI; d 24h heaviest rainfall; e river; f distance to drainage. Table 3. Distance to Rivers In general, at a closer distance to the river, the erosion is stronger and the probability of the occurrence of landslides is higher [ 24 ]. Rainfall Slope in certain geological setting and in a certain mechanical environment requires a certain rainfall, rainfall intensity or duration to promote slope damage [ 24 ].
Architecture of Neural Network In the presented study, the traditional approach of landslide susceptibility mapping by using an artificial neural network model was implemented in a GIS framework. Training of ANN According to the material of field investigation and survey, there have been landslides in the study area. Table 4. Table 5. Accuracy of the ANN Model Assessing the performance of the landslide susceptibility models is considered to be a crucial step in model selection [ 26 ].
Analysis on the Main Control Factor The landslide-susceptibility analysis is a function of a variety of variables that include the altitude, slope, aspect, slope angle, distance to faults, rock types, soil types, land cover, maximum rainfall intensity, NDVI and distance to river. Dependent variables of the analyses are also expressed in binary format with respect to presence 1 and absence 0 of landslide or no landslide cell.
Table 6. Variables in the Equation. Table 7. A free demonstration copy is available. Able to handle an unlimited number of records, DataCruncher supports advanced data mining features such as incremental modeling, model merge, and native connectivity to Oracle and Informix database management systems. Markets: Industry, Trade, Financial Services. It accesses large volumes of multi-table relational data on the server, incrementally discovers patterns and delivers automatically generated English text and graphs as explainable documents on the intranet.
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The result? Deeper insight and better decision making. A computational neural network is a set of non-linear data modeling tools consisting of input and output layers plus one or two hidden layers. The connections between neurons in each layer have associated weights, which are iteratively adjusted by the training algorithm to minimize error and provide accurate predictions.
Find new associations in your data with Neural Networks and then confirm their significance with traditional statistical techniques. You can combine Neural Networks with other statistical procedures to gain clearer insight in a number of areas:. Both use feed-forward architectures, meaning that data moves in only one direction, from the input nodes through the hidden layer or layers of nodes to the output nodes. Your choice of procedure will be influenced by the type of data you have and the level of complexity you seek to uncover.
With either of these approaches, the procedure operates on a training set of data and then applies that knowledge to the entire dataset, and to any new data. Necessary cookies are absolutely essential for the website to function properly. These cookies ensure basic functionalities and security features of the website, anonymously. The cookie is used to store the user consent for the cookies in the category "Analytics". The cookies is used to store the user consent for the cookies in the category "Necessary".
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