Article
Engineering, Environmental
Jian Hu, Kaibin Xu, Genglong Wang, Youcun Liu, Muhammad Asim Khan, Yimin Mao, Maosheng Zhang
Summary: The study proposes a novel method for constructing landslide susceptibility maps using the OPTICS algorithm and Hausdorff distance. This approach effectively divides mapping units into multiple subclasses and categorizes them into five susceptibility levels based on landslide density values. Applying this method can significantly enhance the assessment of landslide susceptibility.
BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
(2021)
Article
Geosciences, Multidisciplinary
Marelyn Telun Daniel, Tham Fatt Ng, Mohd. Farid Abdul Kadir, Joy Jacqueline Pereira
Summary: The study conducted landslide susceptibility assessment in Canada Hill, Sarawak, Malaysia using a combined bivariate statistics and expert consultation approach with geographical information system. The resultant susceptibility map with five classes was able to capture local conditions effectively, leading to a success rate of 75.8%. This approach improved data quality in landslide inventories and delineated key conditioning parameters for landslide management.
FRONTIERS IN EARTH SCIENCE
(2021)
Article
Environmental Sciences
Ali Polat
Summary: This study utilized the LSAT toolbox to produce landslide susceptibility maps for the Akincilar region, achieving prediction rates ranging from 70% to 73% using five different methods. LSAT proved to be effective in reducing time-consuming processes associated with constructing LSM, allowing for quick and automated data preparation, visualization of modeling results, and accuracy assessment of LSM.
ENVIRONMENTAL EARTH SCIENCES
(2021)
Article
Engineering, Civil
Yaser A. Nanehkaran, Yimin Mao, Mohammad Azarafza, Mustafa K. Kockar, Hong-Hu Zhu
Summary: Using fuzzy logic-based multi-criteria decision-making method, this study evaluated landslide hazard zonation in the Tabriz region of Iran. By identifying five main factors and creating hazard maps as well as susceptibility maps, risk zones were delineated, with high sensitivity areas identified in the northern parts of the region.
GEOMECHANICS AND ENGINEERING
(2021)
Article
Geosciences, Multidisciplinary
Hassan Abedi Gheshlaghi, Bakhtiar Feizizadeh
Summary: This study proposed and verified an ensemble approach based on fuzzy system and bivariate statistics for landslide susceptibility assessment in Azarshahr Chay Basin, Iran. The FMV_IOE model showed the best performance in terms of sensitivity, accuracy, and specificity, with altitude, lithology, and slope degree identified as the main drivers of landslide occurrence based on the results of the IOE analysis.
Article
Environmental Sciences
Zhu Liang, Changming Wang, Zhijie Duan, Hailiang Liu, Xiaoyang Liu, Kaleem Ullah Jan Khan
Summary: This study employed a hybrid model that utilized the advantages of both supervised and unsupervised learning, and through a two-stage modeling process, constructed a robust landslide prediction model with improved performance.
Article
Engineering, Environmental
Areeba Qazi, Kanwarpreet Singh, Dinesh Kumar Vishwakarma, Hazem Ghassan Abdo
Summary: The aim of this study is to determine and evaluate the landslide susceptibility zonation of Kinnaur district in HP, providing preventive and remedial measures. Three statistical methods were used to map the susceptibility of landslide hazard. The landslide inventory was created using data acquired from the Geological Survey of India. Various geo-environmental factors were selected to create landslide susceptibility maps, which were categorized into five categories based on mapping data. The study emphasizes the importance of identifying landslide areas and assisting local authorities in implementing precautionary measures and improving contingency plans.
BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
(2023)
Article
Engineering, Environmental
Hossein Hamedi, Ali Asghar Alesheikh, Mahdi Panahi, Saro Lee
Summary: Using deep learning algorithms including CNN and LSTM, landslide prone areas were identified in Ardabil province, Iran. The LSTM model showed slightly better performance compared to the CNN model, but both models have close performance with acceptable accuracy. AUC values for CNN and LSTM models were 0.821 and 0.832, respectively, indicating the effectiveness of the models in landslide susceptibility mapping.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2022)
Article
Geosciences, Multidisciplinary
Vahed Ghiasi, Seyed Amir Reza Ghasemi, Mahyar Yousefi
Summary: Landslide susceptibility mapping methods are categorized into data-driven and knowledge-driven approaches, with issues of stochastic and systemic errors. This paper proposes a continuous weighting method to overcome these disadvantages, demonstrating its effectiveness in comparison to existing methods.
Article
Environmental Sciences
Fakhrul Islam, Salma Riaz, Bushra Ghaffar, Aqil Tariq, Safeer Ullah Shah, Muhammad Nawaz, Mian Luqman Hussain, Naz Ul Amin, Qingting Li, Linlin Lu, Munawar Shah, Muhammad Aslam
Summary: Landslides are a recurring environmental hazard in hilly regions of Pakistan, affecting socioeconomic development. This study aims to create a landslide susceptibility mapping (LSM) of the Hindu Kush Himalayan, Swat District, using three bivariate models (WOE, FR, and IV) that have not been previously applied in the area. The models were validated using AUROC, with the FR model proving to be the most reliable technique. Policymakers can utilize the findings to mitigate landslide hazards.
FRONTIERS IN ENVIRONMENTAL SCIENCE
(2022)
Article
Engineering, Geological
Md Sharafat Chowdhury, Bibi Hafsa
Summary: This study used GIS and statistical models to create a Landslide Susceptibility Map for Chattagram District in Bangladesh and found that higher elevated areas are more prone to landslides. These findings are important for local land use planning, management, and hazard mitigation efforts.
GEOTECHNICAL AND GEOLOGICAL ENGINEERING
(2022)
Article
Engineering, Environmental
Mansheng Lin, Shuai Teng, Gongfa Chen, Bo Hu
Summary: This study investigates the performance of CNNs with different architectures and training options for transmission tower foundation landslide spatial prediction using Bayesian optimization. Fourteen influencing factors related to landslides are considered, and 424 historical landslide locations in Guangdong Province, China are randomly divided for training and testing the CNNs. Bayesian optimization is used to draw conclusions about the CNNs, including the optimal number of convolution layers, the absence of a pooling layer, and the use of a piece-wise decay learning rate strategy. The excellent performance of the CNN obtained by Bayesian optimization is validated by comparisons with other models, indicating its potential for reducing the impact of landslides on power supply.
BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
(2023)
Article
Chemistry, Multidisciplinary
Zelang Miao, Renfeng Peng, Wei Wang, Qirong Li, Shuai Chen, Anshu Zhang, Minghui Pu, Ke Li, Qinqin Liu, Changhao Hu
Summary: Earthquakes worldwide lead to landslides that cause significant fatalities and financial losses. Precise and timely landslide susceptibility mapping (LSM) is crucial for assessing and mitigating landslide hazards in earthquake-affected areas. This study presents a new LSM model that integrates data modality and machine learning methods, achieving higher performance than existing LSM methods.
APPLIED SCIENCES-BASEL
(2022)
Article
Engineering, Environmental
Hamid Bourenane, Aghiles Abdelghani Meziani, Dalila Ait Benamar
Summary: The research validated and compared landslide susceptibility maps in the urban area of Azazga using four GIS-based statistical approaches. The study found that the FR method provided more accurate prediction in generating LSMs than the other three models, and all the statistical models showed good accuracy in landslide susceptibility mapping.
BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
(2021)
Article
Environmental Sciences
Hui Deng, Xiantan Wu, Wenjiang Zhang, Yansong Liu, Weile Li, Xiangyu Li, Ping Zhou, Wenhao Zhuo
Summary: Landslide susceptibility evaluation using the IV-RF model was carried out in a deep valley area. Historical landslide data and various remote sensing techniques were used to develop a landslide inventory and select input factors for the evaluation model. The results showed that the evaluation accuracy was highest under the optimal slope unit scale, and the landslide susceptibility map matched the actual distribution of landslides.
Article
Environmental Sciences
Ataollah Kavian, Elham Allahi, Alan D. Ziegler, Mohamad Ayob Mohamadi, Seyed Majid Zabihzadeh, Zeinab Hazbavi
LAND DEGRADATION & DEVELOPMENT
(2020)
Article
Environmental Sciences
Artemi Cerda, Jesus Rodrigo-Comino, Tugrul Yakupoglu, Turgay Dindaroglu, Enric Terol, Gaspar Mora-Navarro, Alireza Arabameri, Maja Radziemska, Agata Novara, Ataollah Kavian, Magdalena Daria Vaverkova, Sameh Kotb Abd-Elmabod, Hafiz Mohkum Hammad, Ioannis N. Daliakopoulos
Article
Environmental Sciences
Mohammad Golshan, Ataollah Kavian, Abazar Esmali, Alan D. Ziegler
ENVIRONMENTAL EARTH SCIENCES
(2020)
Article
Water Resources
Ataollah Kavian, Narges Javidan, Abdolreza Bahrehmand, Yeboah Gyasi-Agyei, Zeinab Hazbavi, Jesus Rodrigo-Comino
HYDROLOGICAL SCIENCES JOURNAL
(2020)
Article
Environmental Sciences
Ataollah Kavian, Mahin Kalehhouei, Leila Gholami, Zeinab Jafarian, Maziar Mohammadi, Jesus Rodrigo-Comino
Article
Environmental Sciences
Leila Gholami, Abdulavahed Khaledi Darvishan, Veliber Spalevic, Artemi Cerda, Ataollah Kavian
Summary: The study reveals that rainfall intensity significantly affects runoff, soil erosion, and sediment concentration during storms, and identifies the prioritization of effects of four storm patterns.
JOURNAL OF MOUNTAIN SCIENCE
(2021)
Article
Multidisciplinary Sciences
Narges Javidan, Ataollah Kavian, Hamid Reza Pourghasemi, Christian Conoscenti, Zeinab Jafarian, Jesus Rodrigo-Comino
Summary: This study focused on creating a multi-hazard map in a watershed area in the Golestan Province, Iran, integrating susceptibility maps for flood, landslides, and gully erosion. The research demonstrated that 60% of the area is subjected to hazards, with landslides covering up to 21.2% of the entire territory. This type of multi-hazard map may serve as a useful tool for local administrators to identify areas susceptible to hazards on a large scale.
SCIENTIFIC REPORTS
(2021)
Article
Environmental Sciences
Nazanin Mohammadzade Miyab, Ramin Fazloula, Manouchehr Heidarpour, Ataollah Kavian, Jesus Rodrigo-Comino
Summary: Designing correct engineering infrastructures and considering natural elements are crucial for managing potential natural hazards. This research studied the impact of vegetation and pile groups on scour depth and velocity around bridge piles. It was found that using vegetation and pile groups significantly reduced scour and changed the flow, helping to protect river ecosystems.
Article
Environmental Sciences
Mohammadtaghi Avand, Maziar Mohammadi, Fahimeh Mirchooli, Ataollah Kavian, John P. Tiefenbacher
Summary: This study proposes a new approach by integrating empirical and artificial intelligence models for soil erosion modeling. Using the random forest model, it achieves the highest prediction performance. The results provide insights into the factors influencing soil erosion and help in identifying erosion hot spots.
ENVIRONMENTAL MODELING & ASSESSMENT
(2023)
Editorial Material
Geosciences, Multidisciplinary
Maria Fernandez-Raga, Ataollah Kavian, Yang Yu, Jesus Rodrigo-Comino
FRONTIERS IN EARTH SCIENCE
(2022)
Article
Ecology
Zahra Fadaei, Ataollah Kavian, Karim Solaimani, Leila Zandi Sarabsoreh, Mahin Kalehhouei, Victor Hugo Duran Zuazo, Jesus Rodrigo-Comino
Summary: This study aimed to investigate the effects of wildfire on soil quality characteristics in a forest area in Iran. The results showed that forest fires significantly influenced the physical and chemical properties of soil, with a decreasing trend in clay content and infiltration observed.
Article
Environmental Sciences
Maziar Mohammadi, Markus Egli, Ataollah Kavian, Ivan Lizaga
Summary: Unsustainable human activities have disrupted the natural cycle of trace elements, causing the accumulation of chemical pollutants and making it challenging to determine their sources. A novel approach combining fingerprinting techniques, geochemical data, and statistical models was used to identify and quantify the contribution of trace elements from rivers to soils. Results showed that both upland sub-watersheds and land use play important roles in transferring trace elements to the study area.
SCIENCE OF THE TOTAL ENVIRONMENT
(2023)
Article
Environmental Sciences
Karim Solaimani, Noorangiz Gholami, Ataollah Kavian, Vahid Gholami
Summary: The formation of point bars plays a crucial role in altering hydraulic behavior during floods, affecting flow resistance, direction, erosion, depth, and flood zone. Research indicates that point bars reduce flow capacity in cross sections of river channels, leading to increased flow depth, flood area, and water shear stress in channel buffers.
MODELING EARTH SYSTEMS AND ENVIRONMENT
(2022)
Article
Ecology
Maryam Rezaei Pasha, Kaka Shahedi, Qorban Vahabzadeh, Ataollah Kavian, Mehdi Ghajar Sepanlou, Pascal Jouquet
Summary: The study found that vermicompost can improve soil properties, but its impact on plant growth and runoff is limited to the short term.
COMPOST SCIENCE & UTILIZATION
(2020)
Article
Meteorology & Atmospheric Sciences
Abazar Esmali, Mohammad Golshan, Ataollah Kavian
Summary: This study compared the performance of the simple IHACRES model with the complex SWAT model in different climatic regions of Iran, and found that SWAT outperformed IHACRES in most cases, especially in arid watersheds.
