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51.
Globally, groundwater plays a major role in supplying drinking water for urban and rural population and is used for irrigation to grow crops and in many industrial processes. A novel self-learning random forest (SLRF) model is developed and validated for groundwater yield zonation within the Yeondong Province in South Korea. This study was conducted with an inventory data initially divided randomly into 70% for training and 30% for testing and 13 groundwater-conditioning factors. SLRF was optimized using Bayesian optimization method. We also compared our method to other machine learning methods including support vector machine (SVM), artificial neural networks (ANN), decision trees (DT), and voting ensemble models. Model validation was accomplished using several methods, including a confusion matrix, receiver operating characteristics, cross-validation, and McNemar’s test. Our proposed self-learning method improves random forest (RF) generalization performance by about 23%, with SLRF success rates of 0.76 and prediction rates of 0.83. In addition, the optimized SLRF performed better [according to a threefold cross-validated AUC (area under curve) of 0.75] than that using randomly initialized parameters (0.57). SLRF outperformed all of the other models for the testing dataset (RF, SVM, ANN, DT, and Voted ANN-RF) when the overall accuracy, prediction rate, and cross-validated AUC metrics were considered. The SLRF also estimated the contribution of individual groundwater conditioning factors and showed that the three most influential factors were geology (1.00), profile curvature (0.97), and TWI (0.95). Overall, SLRF effectively modeled groundwater potential, even within data-scarce regions.
相似文献52.
Coupling of remote sensing data aided with field investigations for geological hazards assessment in Jazan area,Kingdom of Saudi Arabia 总被引：5，自引：2，他引：3
Ahmed M. Youssef Biswajeet Pradhan Abdullah A. Sabtan Hassan M. El-Harbi 《Environmental Earth Sciences》2012,65(1):119-130
The city of Jazan is situated on the eastern flank of the Read Sea and considered as one of the fastest growing cities in
the Kingdom of Saudi Arabia. This zone attracts a lot of investors for various development projects. Recently, many new projects
have been implemented and constructed in this region including new urban areas, infrastructures, and industrial projects.
However, historically this area has been challenged from different types of geological hazards. These geological hazards are
catastrophic events that can cause human injury, loss of life, and economic devastation. The current study is aimed at evaluating
the different types of geological hazards in Jazan city. This study is based on interpretation of satellite data such as LANDSAT
and QuickBird images, existing geological maps, and physiographical characteristics with the help of field and laboratory
analyses. The results of the analysis indicate that there exist various types of geological hazards in the study area mostly
related to the natural factors which include (1) Sabkha soil; (2) Salt dome; (3) Loess soil; and (4) Sand dune/drift. Further,
the findings of this study revealed that, most of these geological hazards have a severe impact on the ongoing development
activities in Jazan area. 相似文献
53.
Amar Deep Regmi Kohki Yoshida Hidehisa Nagata Ananta Man Singh Pradhan Biswajeet Pradhan Hamid Reza Pourghasemi 《Natural Hazards》2013,66(2):501-532
The present study was conducted along the Mugling–Narayanghat road section and its surrounding region that is most affected by landslide and related mass-movement phenomena. The main rock types in the study area are limestone, dolomite, slate, phyllite, quartzite and amphibolites of Lesser Himalaya, sandstone, mudstone and conglomerates of Siwaliks and Holocene Deposits. Due to the important role of geology and rock weathering in the instabilities, an attempt has been made to understand the relationship between these phenomena. Consequently, landslides of the road section and its surrounding region have been assessed using remote sensing, Geographical information systems and multiple field visits. A landslide inventory map was prepared and comprising 275 landslides. Nine landslides representing the whole area were selected for detailed studies. Field surveys, integrated with laboratory tests, were used as the main criteria for determining the weathering zones in the landslide area. From the overall study, it is seen that large and complex landslides are related to deep rock weathering followed by the intervention of geological structures as faults, joints and fractures. Rotational types of landslides are observed in highly weathered rocks, where the dip direction of the foliation plane together with the rock weathering plays a principle role. Shallow landslides are developed in the slope covered by residual soil or colluviums. The rock is rather fresh below these covers. Some shallow landslides (rock topples) are related to the attitude of the foliation plane and are generally observed in fresh rocks. Debris slides and debris flows occur in colluviums or residual soil-covered slopes. In few instances, they are also related to the rock fall occurring at higher slopes. The materials from the rock fall are mixed with the colluviums and other materials lying on the slope downhill and flow as debris flow. Rock falls are mainly related to the joint pattern and the slope angle. They are found in less-weathered rocks. From all these, it is concluded that the rock weathering followed by geological structures has prominent role in the rock slope instability along Mugling–Narayanghat road section and its surrounding regions. 相似文献
54.
Krishna Chandra Devkota Amar Deep Regmi Hamid Reza Pourghasemi Kohki Yoshida Biswajeet Pradhan In Chang Ryu Megh Raj Dhital Omar F. Althuwaynee 《Natural Hazards》2013,65(1):135-165
Landslide susceptibility maps are vital for disaster management and for planning development activities in the mountainous country like Nepal. In the present study, landslide susceptibility assessment of Mugling?CNarayanghat road and its surrounding area is made using bivariate (certainty factor and index of entropy) and multivariate (logistic regression) models. At first, a landslide inventory map was prepared using earlier reports and aerial photographs as well as by carrying out field survey. As a result, 321 landslides were mapped and out of which 241 (75?%) were randomly selected for building landslide susceptibility models, while the remaining 80 (25?%) were used for validating the models. The effectiveness of landslide susceptibility assessment using GIS and statistics is based on appropriate selection of the factors which play a dominant role in slope stability. In this case study, the following landslide conditioning factors were evaluated: slope gradient; slope aspect; altitude; plan curvature; lithology; land use; distance from faults, rivers and roads; topographic wetness index; stream power index; and sediment transport index. These factors were prepared from topographic map, drainage map, road map, and the geological map. Finally, the validation of landslide susceptibility map was carried out using receiver operating characteristic (ROC) curves. The ROC plot estimation results showed that the susceptibility map using index of entropy model with AUC value of 0.9016 has highest prediction accuracy of 90.16?%. Similarly, the susceptibility maps produced using logistic regression model and certainty factor model showed 86.29 and 83.57?% of prediction accuracy, respectively. Furthermore, the ROC plot showed that the success rate of all the three models performed more than 80?% accuracy (i.e. 89.15?% for IOE model, 89.10?% for LR model and 87.21?% for CF model). Hence, it is concluded that all the models employed in this study showed reasonably good accuracy in predicting the landslide susceptibility of Mugling?CNarayanghat road section. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose. 相似文献
55.
The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature; 2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage; and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation training method has been used for the selection of the five different random training sites in order to calculate the factor’s weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide test locations that were not used during the training phase of the neural network. Our findings of verification results show an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis. The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide areas. 相似文献
56.
Naghibi Seyed Amir Vafakhah Mehdi Hashemi Hossein Pradhan Biswajeet Alavi Seyed Jalil 《Natural Resources Research》2020,29(3):1915-1933
Natural Resources Research - Lack of water resources is a common issue in many countries, especially in the Middle East. Flood spreading project (FSP) is an artificial recharge technique, which is... 相似文献
57.
Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia 总被引：20，自引：5，他引：15
Biswajeet Pradhan 《Environmental Earth Sciences》2011,63(2):329-349
Landslides are one of the most frequent and common natural hazards in Malaysia. Preparation of landslide susceptibility maps
is one of the first and most important steps in the landslide hazard mitigation. However, due to complex nature of landslides,
producing a reliable susceptibility map is not easy. For this reason, a number of different approaches have been used, including
direct and indirect heuristic approaches, deterministic, probabilistic, statistical, and data mining approaches. Moreover,
these landslides can be systematically assessed and mapped through a traditional mapping framework using geoinformation technologies.
Since the early 1990s, several mathematical models have been developed and applied to landslide hazard mapping using geographic
information system (GIS). Among various approaches, fuzzy logic relation for mapping landslide susceptibility is one of the
techniques that allows to describe the role of each predisposing factor (landslide-conditioning parameters) and their optimal
combination. This paper presents a new attempt at landslide susceptibility mapping using fuzzy logic relations and their cross
application of membership values to three study areas in Malaysia using a GIS. The possibility of capturing the judgment and
the modeling of conditioning factors are the main advantages of using fuzzy logic. These models are capable to capture the
conditioning factors directly affecting the landslides and also the inter-relationship among them. In the first stage of the
study, a landslide inventory was complied for each of the three study areas using both field surveys and airphoto studies.
Using total 12 topographic and lithological variables, landslide susceptibility models were developed using the fuzzy logic
approach. Then the landslide inventory and the parameter maps were analyzed together using the fuzzy relations and the landslide
susceptibility maps produced. Finally, the prediction performance of the susceptibility maps was checked by considering field-verified
landslide locations in the studied areas. Further, the susceptibility maps were validated using the receiver-operating characteristics
(ROC) success rate curves. The ROC curve technique is based on plotting model sensitivity—true positive fraction values calculated
for different threshold values versus model specificity—true negative fraction values on a graph. The ROC curves were calculated
for the landslide susceptibility maps obtained from the application and cross application of fuzzy logic relations. Qualitatively,
the produced landslide susceptibility maps showed greater than 82% landslide susceptibility in all nine cases. The results
indicated that, when compared with the landslide susceptibility maps, the landslides identified in the study areas were found
to be located in the very high and high susceptibility zones. This shows that as far as the performance of the fuzzy logic
relation approach is concerned, the results appeared to be quite satisfactory, the zones determined on the map being zones
of relative susceptibility. 相似文献
58.
Ahmed M. Youssef Biswajeet Pradhan Elhami Tarabees 《Arabian Journal of Geosciences》2011,4(3-4):463-473
Urban, industrial, and tourist developments are considered of high priority in Egypt. In the current research, the site suitability investigation for rating the different environmental, geological, and geotechnical conditions facing civil engineering projects were assessed using a geographic information system (GIS) multi-criteria approach. The study area is one of the most promising areas for urban and touristic as well as industrial developments in Egypt, which is located on the NW coast of the Gulf of Suez. This area may face several geo-environmental problems that will limit its suitability for civil projects. Weighted GIS model, which integrates different types of data sources, such as land use/cover, geological, geomorphological, geophysical, environmental, remote sensing, and field data, can be achieved to create a site suitability map. In this paper, an analytical hierarchy process approach has been used to develop the weighted model for different factors. As a result of this study, areas of potential geotechnical and geo-environmental hazards that could impact the design and construction of civil projects were identified. Therefore, changes can be made early in the design process before significant design efforts are being invested. 相似文献
59.
Vahid Nourani Biswajeet Pradhan Hamid Ghaffari Seyed Saber Sharifi 《Natural Hazards》2014,71(1):523-547
Without a doubt, landslide is one of the most disastrous natural hazards and landslide susceptibility maps (LSMs) in regional scale are the useful guide to future development planning. Therefore, the importance of generating LSMs through different methods is popular in the international literature. The goal of this study was to evaluate the susceptibility of the occurrence of landslides in Zonouz Plain, located in North-West of Iran. For this purpose, a landslide inventory map was constructed using field survey, air photo/satellite image interpretation, and literature search for historical landslide records. Then, seven landslide-conditioning factors such as lithology, slope, aspect, elevation, land cover, distance to stream, and distance to road were utilized for generation LSMs by various models: frequency ratio (FR), logistic regression (LR), artificial neural network (ANN), and genetic programming (GP) methods in geographic information system (GIS). Finally, total four LSMs were obtained by using these four methods. For verification, the results of LSM analyses were confirmed using the landslide inventory map containing 190 active landslide zones. The validation process showed that the prediction accuracy of LSMs, produced by the FR, LR, ANN, and GP, was 87.57, 89.42, 92.37, and 93.27 %, respectively. The obtained results indicated that the use of GP for generating LSMs provides more accurate prediction in comparison with FR, LR, and ANN. Furthermore; GP model is superior to the ANN model because it can present an explicit formulation instead of weights and biases matrices. 相似文献
60.
Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS 总被引：4，自引：0，他引：4
Biswajeet Pradhan Mohammed Hasan Abokharima Mustafa Neamah Jebur Mahyat Shafapour Tehrany 《Natural Hazards》2014,73(2):1019-1042
Land subsidence is one of the frequent geological hazards worldwide. Urban areas and agricultural industries are the entities most affected by the consequences of land subsidence. The main objective of this study was to estimate the land subsidence (sinkhole) hazards at the Kinta Valley of Perak, Malaysia, using geographic information system and remote sensing techniques. To start, land subsidence locations were observed by surveying measurements using GPS and using the tabular data, which were produced as coordinates of each sinkhole incident. Various land subsidence conditioning factors were used such as altitude, slope, aspect, lithology, distance from the fault, distance from the river, normalized difference vegetation index, soil type, stream power index, topographic wetness index, and land use/cover. In this article, a data-driven technique of an evidential belief function (EBF), which is in the category of multivariate statistical analysis, was used to map the land subsidence-prone areas. The frequency ratio (FR) was performed as an efficient bivariate statistical analysis method in order compare it with the acquired results from the EBF analysis. The probability maps were acquired and the results of the analysis validated by the area under the (ROC) curve using the testing land subsidence locations. The results indicated that the FR model could produce a 71.16 % prediction rate, while the EBF showed better prediction accuracy with a rate of 73.63 %. Furthermore, the success rate was measured and accuracies of 75.30 and 79.45 % achieved for FR and EBF, respectively. These results can produce an understanding of the nature of land subsidence as well as promulgate public awareness of such geo-hazards to decrease human and economic losses. 相似文献