A novel integrated approach of relevance vector machine optimized by imperialist competitive algorithm for spatial modeling of shallow landslides

This research aims at proposing a new artificial intelligence approach (namely RVM-ICA) which is based on the Relevance Vector Machine (RVM) and the Imperialist Competitive Algorithm (ICA) optimization for landslide susceptibility modeling. A Geographic Information System (GIS) spatial database was generated from Lang Son city in Lang Son province (Vietnam). This GIS database includes a landslide inventory map and fourteen landslide conditioning factors. The suitability of these factors for landslide susceptibility modeling in the study area was verified by the Information Gain Ratio (IGR) technique. A landslide susceptibility prediction model based on RVM-ICA and the GIS database was established by training and prediction phases. The predictive capability of the new approach was evaluated by calculations of sensitivity, specificity, accuracy, and the area under the Receiver Operating Characteristic curve (AUC). In addition, to assess the applicability of the proposed model, two state-of-the-art soft computing techniques including the support vector machine (SVM) and logistic regression (LR) were used as benchmark methods. The results of this study show that RVM-ICA with AUC = 0.92 achieved a high goodness-of-fit based on both the training and testing datasets. The predictive capability of RVM-ICA outperformed those of SVM with AUC = 0.91 and LR with AUC = 0.87. The experimental results confirm that the newly proposed model is a very promising alternative to assist planners and decision makers in the task of managing landslide prone areas

Development of an Artificial Intelligence Approach for Prediction of Consolidation Coefficient of Soft Soil: A Sensitivity Analysis

This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0),Background: Consolidation coefficient (Cv) is a key parameter to forecast consolidation settlement of soft soil foundation as well as in treatment design of soft soil foundation, especially when drainage consolidation is used in foundation treatment of soft soil. Objective: In this study, the main objective is to predict accurately the consolidation coefficient (Cv) of soft soil using an artificial intelligence approach named Random Forest (RF) method. In addition, we have analyzed the sensitivity of different combinations of factors for prediction of the Cv. Method: A total of 163 soil samples were collected from the construction site in Vietnam. These samples at various depth (m) were analyzed in the laboratory for the determination of clay content (%), moisture content (%), liquid limit (%), plastic limit (%), plasticity index (%), liquidity index (%), and the Cv for generating datasets for modeling. Performance of the models was validated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Correlation Coefficient (R) methods. In the present study, various combinations of soil parameters were applied and eight models were developed using RF algorithm for predicting the Cv of soft soil. Results: Results of model’s study show that performance of the models using different combinations of input factors is much different where R value varies from 0.715 to 0.822. Conclusion: Present study suggested that RF model with appropriate combination of soil properties input factors can help in better and accurate prediction of the Cv of soft soil.publishedVersio

Monitoring and modeling of runoff generating areas in a small agricultural watershed

It is presently well-known that more than 50% of total water quality impairment originates from non-point sources (NPS) of pollution. As an important NPS pollutant, runoff from agricultural lands contributes to water quality problems more than other non-point sources. Therefore, the identification and quantification of runoff generation areas is crucial for source water protection and nutrient management. Runoff generation is a complicated landscape process, affected by various factors in different seasons of the year. In this research, monitoring and modeling were selected as two important approaches to identify the mechanisms of runoff generation, runoff generating areas (RGAs) and its variability in time and space in a small agricultural watershed in southern Ontario. A wireless sensor network (WSN) was designed to monitor runoff generating areas in the study watershed with the ability to measure the depth of surface runoff and soil moisture over ten minutes time intervals. Eight pressure and soil moisture sensors were installed at the outlet of eight fields in the watershed. Data from eighteen natural rainfall events for the period from July 2008 to April 2009 were analyzed to study the spatial and temporal variability of runoff generation areas in the study watershed. The results showed that runoff generating areas in the watershed are highly dynamic in summer, fall and spring with differences in 100%-contribution-status persistency. The results also indicated that 15% of the watershed generates 75% of surface runoff during summer, 100% in fall and 45% during spring. In spite of the dynamic nature of RGAs, the sensitivity of different fields in the watershed in response to rainfall events remained constant, such that some specific fields responded first in all three seasons. This finding led to the introduction of a Slope/Area index for the identification of sensitive fields in the study watershed. Statistical analyses of the factors affecting RGAs indicated that the factors affecting the spatial and temporal variability of RGAs in three seasons vary; however, the soil moisture and rainfall intensity played important roles in the runoff generation mechanism and variability of contributing areas in all seasons. Based on monitored results and field observations, a hydrological model was developed to simulate runoff generating area and to classify the sensitivity of the fields to runoff generation on the basis of the modified Soil Conservation Service Curve Number approach. The model was able to identify the fields that generate runoff and classify the sensitivity of the fields in the watershed. The developed model could simulate RGAs for the summer season with higher degree of accuracy than fall. The developed model needs further improvements for simulation of runoff generating area in the spring season

Quantification of Land use/Land cover change in Qorveh-Dehgolan Basin, Kurdistan Province, Iran Using Remote Sensing and GIS

This research aimed to analyze the land use/ land cover (LULC) change in Qorveh-Dehgolan Basin (Kurdistan, Iran) from 2000 to 2017 (four sets of data) using Landsat (7 and 8) images. Supervised classification using maximum likelihood generated four series of LULC maps by ENVI 5.3 software. Overall, six major classes including bare soil, water body, vegetation cover, agriculture land, grassland, and settlements were identified and mapped.The LULC style has changed over 17 years. It was determined that the waterbody class has continuously reduced about 173.66 km2 from 2000 to 2017 by 63%. The agriculture class has considerably increased from 2000 to 2017 about 129.43 km2 and finally, the area of settlement class increased about 54.06. km2. The overall accuracy was 81.50%, 85.0%, 92.00%, 92.00% for the years of 2000, 2006, 2013 and 2017 respectively.</jats:p

Influence of structural lineaments on drainage morphometry in Qorveh-Dehgolan basin, Kurdistan, Iran

In the current study, a combination of automated lineament extraction from Landsat 8 satellite imagery and 3D interactive visual interpretation (using DEM) alongside image processing techniques (Gram-Schmidt pan-sharpening, convolution directional filter) was carried out to investigate expression and influence of tectonic activity on drainage morphometry within Qorveh-Dehgolan basin. The watersheds, derived from burned DEM using SWAT, were categorized into high and low-altitude watersheds depending on the associated relief. Analysis of spatial relationship of lineament density with drainage density and basin slope revealed that structural lineaments have a decisive control over drainage density distribution particularly at higher basin slopes of certain watersheds. These high-altitude watersheds, associated with more-resistant lithology, were also found to cover higher percentage of total lineaments compared to the lower-altitude watersheds associated with less-resistant lithology. The analysis presented significant insights into the influence of tectonic activity on drainage network in a relatively unexplored area and provided vital baseline information for future investigations

Effect of conversion of rangelands to planted forests on some physical and chemical soil properties

So far it is well known that the conversion of degraded rangelands to planted forests through afforestation practices affects soil properties. Therefore, we selected one rangeland and two planted forest area to study the physical and chemical properties of soil in different land uses in Hassanabad region of Sanandaj in Kurdistan province. Physical soil characteristics such as the percentage of water content and soil bulk density were sampled in two-week intervals during 2012 to 2013 at three points in each area. In addition, a set of double rings was conducted to measure infiltration rate at three points within each area. The saturated hydraulic conductivity was computed using the experimental infiltration data. The soil samples for measuring chemical properties (e.g. pH, EC, organic carbon content, phosphorus and total nitrogen) were taken from 3 points in each area at the depth of 20 cm with six repeated measurements. The results showed that converting degraded rangelands to tree plantations has resulted in enhancing the quality of soil physical characteristics, whereas only a portion of the chemical characteristics (organic carbon content, phosphorus and total nitrogen) were positively affected

‘Multivariate statistical analysis of relationship between tectonic activity and drainage behavior in Qorveh-Dehgolan basin Kurdistan, Iran’

In this study, morphotectonic analysis was performed to investigate the influence of tectonic activity on drainage network in Qorve-Dehgolan basin, located in Sanandaj - Sirjan Zone (SSZ). Eight watersheds were delineated using burnt DEM and eleven morphotectonic indices including stream length gradient index (SL); drainage basin asymmetry (Af); hypsometric curve (HC); hypsometric integral (HI); roughness (R); elongation ratio (Re); mountain front sinuosity (Smf); basin shape (Bs); curvature of surface (C), basin slope (Sb) and river sinuosity index (K) were calculated. The higher altitude watersheds specifically showed various anomalies in the calculated indices. These indices when subjected to factor analysis (using Varimax rotation) resulted in extraction of “steepness factor” and “asymmetric factor” as two principal factors influencing the drainage network. The integrated analysis revealed that steepness factor, indicating ongoing tectonism, plays a significant role in the hydrology of higher altitude watersheds, whereas asymmetric factor is more dominant in lower altitude watersheds