Geological Environment and Engineering
Sayed Naeim Emami; Saleh Yousefi
Abstract
Mass movements are among the most dangerous natural hazards in mountainous regions. The present study employs machine learning (ML) models for mass movement susceptibility mapping (MMSM) in Iran based on a comprehensive dataset of 864 mass movements which include debris flow, landslide, and rockfall ...
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Mass movements are among the most dangerous natural hazards in mountainous regions. The present study employs machine learning (ML) models for mass movement susceptibility mapping (MMSM) in Iran based on a comprehensive dataset of 864 mass movements which include debris flow, landslide, and rockfall during the last 42 years (1977–2019) as well as 12 conditional factors. The results of validation stage show that RF (random forest) is the most viable model for mass movement susceptibility maps. In addition, MARS (multivariate adaptive regression splines), MDA (mixture discriminant additive), and BRT (boosted regression trees) models also provide relatively accurate results. Results of the AUC for validation of produced maps were 0.968, 0.845, 0.828, and 0.765 for RF, MARS, MDA, and BRT, respectively. Based on MMSM generated by RF model, 32% of study area is identified to be under high and very high susceptibility classes. Most of the endangered areas for mass movement are in the west and central parts of the Chaharmahal and Bakhtiari Province. In addition, our findings indicate that elevation, slope angle, distance from roads, and distance from faults are critical factors for mass movement. Our results provide a perspective view for decision makers to mitigate natural hazards.
M. Gharekhani; A. A. Nadiri; A. Asghari Moghaddam
Abstract
Due to the infiltration of contaminants from surface to underground water systems, groundwater pollution is one of the serious problems, especially in arid and semi-arid areas that encounter with lack of quality and quantity of water resources. Therefore, groundwater vulnerability evaluation is necessary ...
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Due to the infiltration of contaminants from surface to underground water systems, groundwater pollution is one of the serious problems, especially in arid and semi-arid areas that encounter with lack of quality and quantity of water resources. Therefore, groundwater vulnerability evaluation is necessary to manage the groundwater resources by identifying areas with high potential of contamination. In this study, groundwater vulnerability in Ardabil plain aquifer was evaluated by applying DRASTIC model. DRASTIC model was prepared by seven effective parameters on vulnerability, including groundwater depth, net recharge, aquifer media, soil media, topography, impact of vadose zone, and hydraulic conductivity. These parameters were prepared as seven raster layers, and DRASTIC index was then calculated after ranking and weighting. The DRASTIC index value was obtained between 82 to 151 for the Ardabil plain. The main problem of this model is the subjectivity in determining rates and weights of the parameters. Therefore, the purpose of this study is to improve DRASTIC model using 5 methods of artificial intelligence (AI), such as Feedforward network (FFN), Recurrent neural network (RNN), Sugeno fuzzy logic (SFL), Mamdani fuzzy logic (MFL), and Committee machine (CM) to obtain the most accurate results of vulnerability evaluation. Because of heterogeneity in the Ardabil Plain, it is divided into 3 sections including west, east and north, and each section needs an individual model. For this purpose, the DRASTIC parameters and the vulnerability index were defined as inputs data and output data respectively for models, and nitrate concentration data were divided into two categories for training and test steps. The output of model in training step was corrected by the related nitrate concentration, and after model training, the output of model in test step was verified by the nitrate concentration. The results show that all of the artificial intelligence methods are able to improve the DRASTIC model, but the supervised committee machine artificial intelligence (SCMAI) model had the best results. According to this model, the most of high pollution potential areas located in western and northern parts of the plain, and need more protection.
A Asghari Moghaddam; E Fijani; A Nadiri
Abstract
Aquifer vulnerability assessment to define critical zones of pollution risk is an important method for groundwater resource management. By applying the DRASTIC model in this study, groundwater vulnerability in the Maragheh-Bonab Plain aquifer was evaluated. The DRASTIC model uses seven environmental ...
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Aquifer vulnerability assessment to define critical zones of pollution risk is an important method for groundwater resource management. By applying the DRASTIC model in this study, groundwater vulnerability in the Maragheh-Bonab Plain aquifer was evaluated. The DRASTIC model uses seven environmental parameters (Depth to water, net Recharge, Aquifer media, Soil media, Topography, Impact of vadose zone, and hydraulic Conductivity) as seven layer in GIS media and finally a groundwater vulnerability map was created by overlaying the available hydrogeological data and categorized to low, moderate, and high risk. The DRASTIC index value was evaluated 81 to 116 for the study area. The vulnerability map created by DRASTIC is compared to nitrate data and the results indicate a relative correlation between the nitrate level and vulnerability index. In order to improve the model, four artificial intelligence (AI) models are adopted by optimizing the weights of the DRASTIC parameters. The four AI models are the Sugeno fuzzy logic (SFL), the Mamdani fuzzy logic (MFL), the artificial neural network (ANN), and the neurofuzzy (NF). For this purpose, the AI model input (the DRASTIC parameters), output (the vulnerability index), and nitrate concentration data was divided into two categories for training and test steps. The output of model in training step was corrected by related nitrate concentration, and after model training, the output of model in test step was verified by nitrate concentration. The results show that the four AI models are applicable to improve the correlation between nitrate level and vulnerability index using DRASTIC model for groundwater vulnerability assessment. The NF model by taking advantage of FL and ANN has the best results that high nitrate level at observation well location has high vulnerable index and was selected as a final model. According to the final model, the western areas of the aquifer are classified as high pollution risk. In conclusion, the AI approach proved to be an effective way to improve the DRASTIC model and provides a confident estimate of pollution risk for the study area.
