Application of AHP Model in Selection of Most Appropriate Area to Establish Soil Damp for Artificial Recharge of Underground Aquifers (Case Study: Tabas Basin)

In recent years water exploitation has become greater for many reasons such as population growth industrial development urbanization growth and consequently increased demand for food products Hence the rate of exploitation and consumption ground water become greater than recharge of them in other words input of ground water system is less than its output and system with negative balance sheet has positive feedback and it is collapsing Thus it is very significant to determine the suitable position for Artificial Recharge of ground water One of the management methods for water resources is Multi Criteria Decision Making The analytic hierarchy process AHP is a structured technique for dealing with complex decisions that was developed by Thomas L Saaty in the 1980 year It provides a comprehensive and rational framework for structuring a decision problem for representing and quantifying its elements for relating those elements to overall goals and for evaluating alternative solutions The base of this model is comparing variables by pair wise by Matrix relationship In this way pair wise of the effective variables on the concrete Pavement were considered and based on relative weights the output was extent In the present research combination of Indexing system Method with Analytical Hierarchy Process has been applied to assess the Selection of most appropriate area to establish soil damp for artificial recharge of underground aquifers The findings of the research show that zone 3 with 0 3606 points promotes in first rank among 5 studied zones and thus it is the most appropriate zone for Artificial Recharge of ground waters in contrast zone 5 with 0 1731 point goes down to the last rank and so it isn t suitable for Artificial Recharge and zones 2 4 1 are located in next rank

Zoning Mashhad Watershed for Artificial Recharge of Underground Aquifers using TOPSIS Model and GIS Technique

In recent years coincide with population growth and industrial expansion in many countries in the world Extract water of underground sources expanded and annual withdrawal of ground water is higher than the annual feeding This means extracting and using the water in layers that has been saved over thousands of years in the underground Consequently groundwater levels in the area will be extracted every day and eventually drop where the water will not exist While proper management and control of these resources will eliminate the problems of drop in water level One way to managing groundwater resources is artificial recharge of groundwater and determine suitable locations for these purpose growth and development trend of Mashhad city and excessive Extracting of ground water in recent years has been essential groundwater resources management strategy in the region more than ever implied The purpose of this study is Zoning Mashhad watershed for artificial recharge of underground aquifers using TOPSIS Model and GIS technique TOPSIS algorithm is a Multi Criteria Decision Making a type of compensatory model and an adaptable subgroup with strong ability to solve multi alternative problems because of having ability to overlap indicators in weak and power points In this model if quantitative criteria can change in to qualitative criteria qualitative criteria can be used besides quantitative criteria In aforementioned model it is supposed that each indicator and criterion has steady increasing and decreasing utility in decision making matrix it means if criteria gain more positive amount they will be more appropriate on the contrary the more negative amount the less appropriate The result and findings of different studies show that in TOPSIS method zone 3 with 0 669 point promotes in first rank among 5 studied zones and thus it is the most appropriate zone to establish the proper area for artificial recharge of underground aquifers in contrast zone

Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in GIS

© 2018 Elsevier Ltd Every year, gully erosion causes substantial damage to agricultural land, residential areas and infrastructure, such as roads. Gully erosion assessment and mapping can facilitate decision making in environmental management and soil conservation. Thus, this research aims to propose a new model by combining the geographically weighted regression (GWR) technique with the certainty factor (CF) and random forest (RF) models to produce gully erosion zonation mapping. The proposed model was implemented in the Mahabia watershed of Iran, which is highly sensitive to gully erosion. Firstly, dependent and independent variables, including a gully erosion inventory map (GEIM) and gully-related causal factors (GRCFs), were prepared using several data sources. Secondly, the GEIM was randomly divided into two groups: training (70%) and validation (30%) datasets. Thirdly, tolerance and variance inflation factor indicators were used for multicollinearity analysis. The results of the analysis corroborated that no collinearity exists amongst GRCFs. A total of 12 topographic, hydrologic, geologic, climatologic, environmental and soil-related GRCFs and 150 gully locations were used for modelling. The watershed was divided into eight homogeneous units because the importance level of the parameters in different parts of the watershed is not the same. For this purpose, coefficients of elevation, distance to stream and distance to road parameters were used. These coefficients were obtained by extracting bi-square kernel and AIC via the GWR method. Subsequently, the RF-CF integrated model was applied in each unit. Finally, with the units combined, the final gully erosion susceptibility map was obtained. On the basis of the RF model, distance to stream, distance to road and land use/land cover exhibited a high influence on gully formation. Validation results using area under curve indicated that new GWR–CF–RF approach has a higher predictive accuracy 0.967 (96.7%) than the individual models of CF 0.763 (76.3%) and RF 0.776 (77.6%) and the CF-RF integrated model 0.897 (89.7%). Thus, the results of this research can be used by local managers and planners for environmental management

Identification of erosion-prone areas using different multi-criteria decision-making techniques and gis

© 2018 The Author(s). The awareness of erosion risk in watersheds provides the possibility of identifying critical areas and prioritising protective and management plans. Soil erosion is one of the major natural hazards in the rainy mountainous regions of the Neka Roud Watershed in Mazandaran Province, Iran. This research assesses soil erosion susceptibility through morphometric parameters and the land use/land cover (LU/LC) factor based on multiple-criteria decision-making (MCDM) techniques, remote sensing and GIS. A set of 17 linear, relief and shape morphometric parameters and 5 LU/LC classes are used in the analysis. The aforementioned factors are selected as indicators of soil erosion in the study area. Then, four MCDM models, namely, the new additive ratio assessment (ARAS), complex proportional assessment (COPRAS), multi-objective optimisation by ratio analysis and compromise programming, are applied to the prioritisation of the Neka Roud sub-watersheds. The Spearman’s correlation coefficient test and Kendall’s tau correlation coefficient test indices are used to select the best models. The validation of the models indicates that the ARAS and COPRAS models based on morphometric parameters and LU/LC classes, respectively, achieve the best performance. The results of this research can be used by planners and decision makers in soil conservation and in reducing soil erosion

Assessment of landslide susceptibility using statistical- and artificial intelligence-based FR-RF integrated model and multiresolution DEMs

© 2019 by the authors. Landslide is one of the most important geomorphological hazards that cause significant ecological and economic losses and results in billions of dollars in financial losses and thousands of casualties per year. The occurrence of landslide in northern Iran (Alborz Mountain Belt) is often due to the geological and climatic conditions and tectonic and human activities. To reduce or control the damage caused by landslides, landslide susceptibility mapping (LSM) and landslide risk assessment are necessary. In this study, the efficiency and integration of frequency ratio (FR) and random forest (RF) in statistical- and artificial intelligence-based models and different digital elevation models (DEMs) with various spatial resolutions were assessed in the field of LSM. The experiment was performed in Sangtarashan watershed, Mazandran Province, Iran. The study area, which extends to 1072.28 km2, is severely affected by landslides, which cause severe economic and ecological losses. An inventory of 129 landslides that occurred in the study area was prepared using various resources, such as historical landslide records, the interpretation of aerial photos and Google Earth images, and extensive field surveys. The inventory was split into training and test sets, which include 70 and 30% of the landslide locations, respectively. Subsequently, 15 topographic, hydrologic, geologic, and environmental landslide conditioning factors were selected as predictor variables of landslide occurrence on the basis of literature review, field works and multicollinearity analysis. Phased array type L-band synthetic aperture radar (PALSAR), ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), and SRTM (Shuttle Radar Topography Mission) DEMs were used to extract topographic and hydrologic attributes. The RF model showed that land use/land cover (16.95), normalised difference vegetation index (16.44), distance to road (15.32) and elevation (13.6) were the most important controlling variables. Assessment of model performance by calculating the area under the receiving operating characteristic curve parameter showed that FR-RF integrated model (0.917) achieved higher predictive accuracy than the individual FR (0.865) and RF (0.840) models. Comparison of PALSAR, ASTER, and SRTM DEMs with 12.5, 30 and 90 m spatial resolution, respectively, with the FR-RF integrated model showed that the prediction accuracy of FR-RF-PALSAR (0.917) was higher than FR-RF-ASTER (0.865) and FR-RF-SRTM (0.863). The results of this study could be used by local planners and decision makers for planning development projects and landslide hazard mitigation measures

The Use of Artificial Neural Network (ANN) for Modeling of Ammonia Nitrogen Removal from Landfill Leachate by the Ultrasonic Process

Background: The study examined the implementation of artificial neural network (ANN) for the prediction of Ammonia nitrogen removal from landfill leachate by ultrasonic process.Methods: A three-layer backpropagation neural network was optimized to predict Ammonia nitrogen removal from landfill leachate by ultrasonic process. Considering the smallest mean square error (MSE), The configuration of the backpropagation neural network was three-layer ANN with tangent sigmoid transfer function (Tansig) at hidden layer with 14 neurons, linear transfer function (Purelin) at output layer and Levenberg–Marquardt backpropagation training algorithm (LMA).Results: ANN predicted results were very close to the experimental results with correlation coefficient (R2) of 0.993 and MSE 0.000334. The sensitivity analysis showed that all studied variables (Contact time, ultrasound frequency and power and pH) had strong effect on Ammonia nitrogen removal. In addition, pH was the most influential parameter with relative importance of 44.9%.Conclusions: The results showed that neural network modeling could effectively predict Ammonia nitrogen removal from landfill leachate by ultrasonic process

The Use of Artificial Neural Network (ANN) for Modeling of Ammonia Nitrogen Removal from Landfill Leachate by the Ultrasonic Process

Background: The study examined the implementation of artificial neural network (ANN) for the prediction of Ammonia nitrogen removal from landfill leachate by ultrasonic process.Methods: A three-layer backpropagation neural network was optimized to predict Ammonia nitrogen removal from landfill leachate by ultrasonic process. Considering the smallest mean square error (MSE), The configuration of the backpropagation neural network was three-layer ANN with tangent sigmoid transfer function (Tansig) at hidden layer with 14 neurons, linear transfer function (Purelin) at output layer and Levenberg–Marquardt backpropagation training algorithm (LMA).Results: ANN predicted results were very close to the experimental results with correlation coefficient (R2) of 0.993 and MSE 0.000334. The sensitivity analysis showed that all studied variables (Contact time, ultrasound frequency and power and pH) had strong effect on Ammonia nitrogen removal. In addition, pH was the most influential parameter with relative importance of 44.9%.Conclusions: The results showed that neural network modeling could effectively predict Ammonia nitrogen removal from landfill leachate by ultrasonic process

Spatial modelling of Gully erosion using GIS and R programing: A comparison among three data mining algorithms

© 2018 by the authors. Gully erosion triggers land degradation and restricts the use of land. This study assesses the spatial relationship between gully erosion (GE) and geo-environmental variables (GEVs) using Weights-of-Evidence (WoE) Bayes theory, and then applies three data mining methods-Random Forest (RF), boosted regression tree (BRT), and multivariate adaptive regression spline (MARS)-for gully erosion susceptibility mapping (GESM) in the Shahroud watershed, Iran. Gully locations were identified by extensive field surveys, and a total of 172 GE locationsweremapped. Twelve gully-related GEVs: Elevation, slope degree, slope aspect, plan curvature, convergence index, topographic wetness index (TWI), lithology, land use/land cover (LU/LC), distance from rivers, distance from roads, drainage density, and NDVI were selected to model GE. The results of variables importance by RF and BRT models indicated that distance from road, elevation, and lithology had the highest effect on GE occurrence. The area under the curve (AUC) and seed cell area index (SCAI) methods were used to validate the three GE maps. The results showed that AUC for the three models varies from 0.911 to 0.927, whereas the RF model had a prediction accuracy of 0.927 as per SCAI values, when compared to the other models. The findings will be of help for planning and developing the studied region