Convolutional neural network (CNN) with metaheuristic optimization algorithms for landslide susceptibility mapping in Icheon, South Korea

Comparative study for coastal aquifer vulnerability assessment using deep learning and metaheuristic algorithms

Coastal aquifer vulnerability assessment (CAVA) studies are essential for mitigating the effects of seawater intrusion (SWI) worldwide. In this research, the vulnerability of the coastal aquifer in the Lahijan region of northwest Iran was investigated. A vulnerability map (VM) was created applying hydrogeological parameters derived from the original GALDIT model (OGM). The significance of OGM parameters was assessed using the mean decrease accuracy (MDA) method, with the current state of SWI emerging as the most crucial factor for evaluating vulnerability. To optimize GALDIT weights, we introduced the biogeography-based optimization (BBO) and gray wolf optimization (GWO) techniques to obtain to hybrid OGM-BBO and OGM-GWO models, respectively. Despite considerable research focused on enhancing CAVA models, efforts to modify the weights and rates of OGM parameters by incorporating deep learning algorithms remain scarce. Hence, a convolutional neural network (CNN) algorithm was applied to produce the VM. The area under the receiver-operating characteristic curves for OGM-BBO, OGM-GWO, and VMCNN were 0.794, 0.835, and 0.982, respectively. According to the CNN-based VM, 41% of the aquifer displayed very high and high vulnerability to SWI, concentrated primarily along the coastline. Additionally, 32% of the aquifer exhibited very low and low vulnerability to SWI, predominantly in the southern and southwestern regions. The proposed model can be extended to evaluate the vulnerability of various coastal aquifers to SWI, thereby assisting land use planers and policymakers in identifying at-risk areas. Moreover, deep-learning-based approaches can help clarify the associations between aquifer vulnerability and contamination resulting from SWI.

Fast hybrid methods for modeling landslide susceptibility in Ardal County

Recently, machine learning models have received huge attention for environmental risk modeling. One of these applications is landslide susceptibility mapping which is a necessary primary step for dealing with the landslide risk in prone areas. In this study, a conventional machine learning model called multi-layer perceptron (MLP) neural network is built upon advanced optimization algorithms to achieve a firm prediction of landslide susceptibility in Ardal County, West of Iran. The used geospatial dataset consists of fourteen conditioning factors and 170 landslide events. The used optimizers are electromagnetic field optimization (EFO), symbiotic organisms search (SOS), shuffled complex evolution (SCE), and electrostatic discharge algorithm (ESDA) that contribute to tuning MLP's internal parameters. The competency of the models is evaluated using several statistical methods to provide a comparison among them. It was discovered that the EFO-MLP and SCE-MLP enjoy much quicker training than SOS-MLP and ESDA-MLP. Further, relying on both accuracy and time criteria, the EFO-MLP was found to be the most efficient model (time = 1161 s, AUC = 0.879, MSE = 0.153, and R = 0.657). Hence, the landslide susceptibility map of this model is recommended to be used by authorities to provide real-world protective measures within Ardal County. For helping this, a random forest-based model showed that Elevation, Lithology, and Land Use are the most important factors within the studied area. Lastly, the solution discovered in this study is converted into an equation for convenient landslide susceptibility prediction.

Evaluating the influence of road construction on landslide susceptibility in Saudi Arabia's mountainous terrain: a Bayesian-optimised deep learning approach with attention mechanism and sensitivity analysis

In the mountainous region of Asir region of Saudi Arabia, road construction activities are closely associated with frequent landslides, posing significant risks to both human life and infrastructural development. This highlights an urgent need for a highly accurate landslide susceptibility map to guide future development and risk mitigation strategies. Therefore, this study aims to (1) develop robust well-optimised deep learning (DL) models for predicting landslide susceptibility and (2) conduct a comprehensive sensitivity analysis to quantify the impact of each parameter influencing landslides. To achieve these aims, three advanced DL models-Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Bayesian-optimised CNN with an attention mechanism-were rigorously trained and validated. Model validation included eight matrices, calibration curves, and Receiver Operating Characteristic (ROC) and Precision-Recall curves. Multicollinearity was examined using Variance Inflation Factor (VIF) to ensure variable independence. Additionally, sensitivity analysis was used to interpret the models and explore the influence of parameters on landslide. Results showed that road networks significantly influenced the areas identified as high-risk zones. Specifically, in the 1-km buffer around roadways, CNN_AM identified 10.42% of the area as 'Very High' susceptibility-more than double the 4.04% indicated by DNN. In the extended 2-km buffer zone around roadways, Bayesian CNN_AM continued to flag a larger area as Very High risk (7.46%), in contrast to DNN's 3.07%. In performance metrics, CNN_AM outshined DNN and regular CNN models, achieving near-perfect scores in Area Under the Curve (AUC), precision-recall, and overall accuracy. Sensitivity analysis highlighted 'Soil Texture', 'Geology', 'Distance to Road', and 'Slope' as crucial for landslide prediction. This research offers a robust, high-accuracy model that emphasises the role of road networks in landslide susceptibility, thereby providing valuable insights for planners and policymakers to proactively mitigate landslide risks in vulnerable zones near existing and future road infrastructure.

Monitoring the impacts of crop residue cover on agricultural productivity and soil chemical and physical characteristics

To the best of our knowledge, the impacts of crop residue cover (CRC) on agricultural productivity and soil fertility have not been studied by previous researchers. In this regard, this study aims to apply an integrated approach of remote sensing and geospatial analysis to detect CRC and monitor the effects of CRC on agricultural productivity, as well as soil chemical and physical characteristics. To achieve this, a series of Landsat images and 275 ground control points (GCPs) collected from the study areas for the years 2013, 2015, and 2021 were used. A convolutional neural network (CNN), a class of artificial neural network has commonly applied to analyze visual imagery, was employed in this study for CRC detection in two classes (Not-CRC and CRC) for the years 2013, 2015, and 2021. To assess the effects of CRC, the Normalized Difference Vegetation Index (NDVI) was applied to Landsat image series for the years 2015 (22 images), 2019 (20 images), and 2022 (23 images). Furthermore, this study evaluates the impacts of CRC on soil fertility based on collected field observation data. The results show a high performance (Accuracy of > 0.95) of the CNN for CRC detection and mapping. The findings also reveal positive effects of CRC on agricultural productivity, indicating an increase in vegetation density by about 0.1909 and 0.1377 for study areas 1 and 2, respectively, from 2015 to 2022. The results also indicate an increase in soil chemical and physical characteristics, including EC, PH, Na, Mg, HCO3, K, silt, sand, and clay from 2015 to 2022, based on physical examination. In general, the findings underscore that the value of an integrated approach of remote sensing and geospatial analysis for detecting CRC and monitoring its impacts on agricultural productivity and soil fertility. This research can offer valuable insight to researchers and decision-makers in the field of soil science, land management and agriculture.

Analysis of landslide explicative factors and susceptibility mapping in an andean context: The case of Azuay province (Ecuador)

Landslides are one of the natural phenomena with more negative impacts on landscape, natural resources, and human health worldwide. Andean geomorphology, urbanization, poverty, and inequality make it more vulnerable to landslides. This research focuses on understanding explanatory landslide factors and promoting quantitative susceptibility mapping. Both tasks supply valuable knowledge for the Andean region, focusing on territorial planning and risk management support. This work addresses the following questions using the province of Azuay-Ecuador as a study area: (i) How do EFA and LR assess the significance of landslide occurrence factors? (ii) Which are the most significant landslide occurrence factors for susceptibility analysis in an Andean context? (iii) What is the landslide susceptibility map for the study area? The methodological framework uses quantitative techniques to describe landslide behavior. EFA and LR models are based on a historical inventory of 665 records. Both identified NDVI, NDWI, altitude, fault density, road density, and PC2 as the most significant factors. The latter factor represents the standard deviation, maximum value of precipitation, and rainfall in the wet season (January, February, and March). The EFA model was built from 7 latent factors, which explained 55% of the accumulated variance, with a medium item complexity of 1.5, a RMSR of 0.02, and a TLI of 0.89. This technique also identified TWI, fault distance, plane curvature, and road distance as important factors. LR's model, with AIC of 964.63, residual deviance of 924.63, AUC of 0.92, accuracy of 0.84, and Kappa of 0.68, also shows statistical significance for slope, roads density, geology, and land cover factors. This research encompasses a time-series analysis of NDVI, NDWI, and precipitation, including vegetation and weather dynamism for landslide occurrence. Finally, this methodological framework replaces traditional qualitative models based on expert knowledge, for quantitative approaches for the study area and the Andean region.

Deep learning-assisted automated sewage pipe defect detection for urban water environment management

A healthy sewage pipe system plays a significant role in urban water management by collecting and transporting wastewater and stormwater, which can be assessed by hydraulic model. However, sewage pipe defects have been observed frequently in recent years during regular pipe maintenance according to the captured interior videos of underground pipes by closed-circuit television (CCTV) robots. In this case, hydraulic model constructed based on a healthy pipe would produce large deviations with that in real hydraulic performance and even be out of work, which can result in unanticipated damages such as blockage collapse or stormwater overflows. Quick defect evaluation and defect quantification are the precondition to achieve risk assessment and model calibration of urban water management, but currently pipe defects assessment still largely relies on technicians to check the CCTV videos/images. An automated sewage pipe defect detection system is necessary to timely determine pipe issues and then rehabilitate or renew sewage pipes, while the rapid development of deep learning especially in recent five years provides a fantastic opportunity to construct automated pipe defect detection system by image recognition. Given the initial success of deep learning application in CCTV interpretation, the review (i) integrated the methodological framework of automated sewage pipe defect detection, including data acquisition, image pre-processing, feature extraction, model construction and evaluation metrics, (ii) discussed the state-of-the-art performance of deep learning in pipe defects classification, location, and severity rating evaluation (e.g., up to ~96 % of accuracy and 140 FPS of processing speed), and (iii) proposed risk assessment and model calibration in urban water management by considering pipe defects. This review introduces a novel practical application-oriented methodology including defect data acquisition by CCTV, model construction by deep learning, and model application, provides references for further improving accuracy and generalization ability of urban water management models in practical application.

Application of convolutional neural networks based on Bayesian optimization to landslide susceptibility mapping of transmission tower foundation

The stability of tower foundation slopes is an important factor to maintain the operation of a power system. However, it is time-consuming and expensive to evaluate tower foundation slopes one by one due to the large area. The aim of this study is to investigate the performance of CNNs with different architectures and training options for transmission tower foundation landslide spatial prediction (LSP) by Bayesian optimization. Accordingly, fourteen influencing factors related to landslide evaluated by gain ratio technique are considered and 424 historical landslide locations in Luoding and Xinyi Counties (Guangdong Province, China) are randomly divided into 80% for training and 20% for testing the CNNs. The CNN performances are investigated by permutating and combining different numbers of convolutional layers, pooling layers and learning rate strategy. In 59 Bayesian optimized cases, three conclusions are drawn: (a) the CNNs yielded the best result with 3 convolution layers, (b) the CNN without a pooling layer performs best, and (c) a piece-wise decay learning rate strategy yields better performance. Meanwhile, the excellent performance of the CNN obtained by Bayesian optimization (CNNB) has also been validated by comparisons with gravitational search optimization algorithm and other landslide spatial models, which indicates that CNNB can be applied to generate the susceptibility maps for locating transmission tower foundations in high landslide susceptibility zones and reducing the impact of landslides on power supply by taking measures in advance.

Landslide Susceptibility Evaluation Based on Potential Disaster Identification and Ensemble Learning

Catastrophic landslides have much more frequently occurred worldwide due to increasing extreme rainfall events and intensified human engineering activity. Landslide susceptibility evaluation (LSE) is a vital and effective technique for the prevention and control of disastrous landslides. Moreover, about 80% of disastrous landslides had not been discovered ahead and significantly impeded social and economic sustainability development. However, the present studies on LSE mainly focus on the known landslides, neglect the great threat posed by the potential landslides, and thus to some degree constrain the precision and rationality of LSE maps. Moreover, at present, potential landslides are generally identified by the characteristics of surface deformation, terrain, and/or geomorphology. The essential disaster-inducing mechanism is neglected, which has caused relatively low accuracies and relatively high false alarms. Therefore, this work suggests new synthetic criteria of potential landslide identification. The criteria involve surface deformation, disaster-controlling features, and disaster-triggering characteristics and improve the recognition accuracy and lower the false alarm. Furthermore, this work combines the known landslides and discovered potential landslides to improve the precision and rationality of LSE. This work selects Chaya County, a representative region significantly threatened by landslides, as the study area and employs multisource data (geological, topographical, geographical, hydrological, meteorological, seismic, and remote sensing data) to identify potential landslides and realize LSE based on the time-series InSAR technique and XGBoost algorithm. The LSE precision indices of AUC, Accuracy, TPR, F1-score, and Kappa coefficient reach 0.996, 97.98%, 98.77%, 0.98, and 0.96, respectively, and 16 potential landslides are newly discovered. Moreover, the development characteristics of potential landslides and the cause of high landslide susceptibility are illuminated. The proposed synthetic criteria of potential landslide identification and the LSE idea of combining known and potential landslides can be utilized to other disaster-serious regions in the world.

Estimating the Pre-Historical Volcanic Eruption in the Hatangang River Volcanic Field: Experimental and Simulation Study

The volcanic landforms associated with fluvial topography in the Hantangang River Volcanic Field (HRVF) have geoheritage value. The Hantangang basalt geological landform stretches along 110 km of the paleoriver channel of the Hantangang River. Since the eruption that formed this basalt occurred from 0.15 to 0.51 Ma, estimating the eruption in the HRVF that originated from two source vents in North Korea (Orisan Mountain and the 680 m peak) is challenging due to the limited recorded data for this eruption. In this study, we estimated this prehistorical eruption using 3D printing of a terrain model and Q-LavHA simulation. The results from the experiment were further analyzed using findings from an artificial neural network (ANN) and support vector machine (SVM) to classify the experimental lava area. The SVM classification results showed higher accuracy and efficiency in the computational process than the ANN algorithm. Results from the single eruptive vent scenario showed that the experiment had a higher accuracy than the Q-LavHA simulation. Further analysis of multiple vent scenarios in the Q-LavHA simulation has improved the accuracy compared with the single eruptive vent scenarios.