Marand Plain Research Articles

Transforming subsidence vulnerability indexing based on ALPRIFT into risk indexing using a new fuzzy-catastrophe scheme

Poor anthropogenic activities give rise to declining water table in aquifers, which in turn induces land subsidence, an issue of concern to planning systems. Modelling subsidence depends on data and this paper considers impacts over plains with sparse data by building on the ALPRIFT framework, the acronym of 7 data layers, similar to DRASTIC. It uses a scoring system of prescribed rates in terms of physical variations and prescribed weights in terms relative importance of each data layer. The conceptual innovation on ALPRIFT by the paper includes its transformation from Vulnerability Indexing (VI) into Risk Indexing (RI) by discerning the data layers into 5 ALRIF and two PT data layers. These cater for Passive Vulnerability Indices (PVI) and Active Vulnerability Indices (AVI) respectively. Their additions are equivalent to ALPRIFT VI but their products lead to innovative RI capabilities. The paper presents the proof-of-concept for RI and learning its inherent weight values by using an innovative hybrid scheme of fuzzy logic and catastrophe theory. The case study of Marand plain provides a challenging case, where the data are sparse but the analysis of the results provides an insight into the study areas. Subsidence RI is readily applicable to any similar problems.

Evaluation of geostatistical techniques and their hybrid in modelling of groundwater quality index in the Marand Plain in Iran.

In many parts of the world, groundwater is considered as one of the main sources of urban and rural drinking water. Over the past three decades, the qualitative and quantitative characteristics of aquifers have been negatively affected by different factors such as excessive use of chemical fertilizers in agriculture, indiscreet, and over-exploitation use of groundwater. Therefore, finding the effective method for mapping the water quality index (WQI) is important for locating suitable and non-suitable areas for urban and rural drinking waters. In the present paper, the best method to estimate the spatial distribution of WQI was assessed using the inverse distance weighted, kriging, cokriging, geographically weighted regression (GWR), and hybrid models. Creating hybrid models can increase modeling capabilities. Hybrid methods make use of a combination of estimated model capabilities. In addition, to improve the results of cokriging, GWR, and hybrid methods, the auxiliary parameters of land slope, groundwater table, and groundwater transmissibility were used. In order to assess the proposed methodology, 11 qualitative parameters obtained from 63 observation wells in Marand Plain (Iran) were utilized. Four statistical measures, namely the root mean square error (RMSE), the mean absolute error (MAE), the Akaike coefficient (AIC), and the correlation coefficient (R2) along with the Taylor diagram, have been done. Classification of the WQI index showed that the quality of a number of 1, 27, 18, and 17 wells was, respectively, in excellent, good, moderate, and poor grades. The results of modeling the WQI index based on IDW, kriging, cokriging, GWR, and hybrid methods showed that the best estimate of WQI was obtained by using hybrid GWR-kriging method with three input parameters of land slope, groundwater table, and groundwater transmissibility. Therefore, hybrid kriging and GWR methods have been fairly well able to simulate the WQI index.

Assessment of land use and climate change effects on land subsidence using a hydrological model and radar technique

The impact of land use and climate change (LUCC) on the pattern of groundwater and land subsidence at the Marand Plain was investigated in this study. Earth deformation was derived from ENVISAT ASAR (2003.12.17–2006.03.01) and Sentinel-1 (2015.05.03–2015.10.18) satellite data via the Synthetic Aperture Radar Interferometry (InSAR) small baseline subset (SBAS) time series analysis method. Time series analysis results showed the maximum deformation rate of 24 and 39 cm/yr extracted from ENVISAT ASAR and Sentinel-1, respectively. About 3141 ha of the plain area have been affected by the subsidence phenomenon, which has caused severe damage to constructions. Observed groundwater level (GWL) observed data, indicate a reduction of five meters on average for the period of 1997–2015. Linear regression was derived between GWL abatement and subsidence with average correlation coefficient of 0.73 and the rate of subsidence was about 2.6–8 cm for a one meter drop in the GWL. Owing to the obtained relations between GWL reduction and subsidence, the effects of LUCC on groundwater storage (GWS) variability, and consequently the plain subsidence was studied using the Soil and Water Assessment Tool (SWAT). Climate change scenarios were represented by the delta change approach based on the trend for the past three (1985–2014) and last decade (2005–2014), respectively by low delta change (LDC) and high delta change (HDC), where the trend of the most recent decade yield a higher delta change when extrapolating into 2030. After calibration and validation of the SWAT model using the monthly flow data, baseline (Sb) and five different LUCC scenarios (S1-S5) were simulated and their effects on the GWS variability were compared with together. The simulated results of Sb vs. S4 and S5, which represent plausible scenarios show that LUCC may decrease GWS about 52–99% and 85–100% under LDC and HDC, respectively. The results of the scenarios show the significant value of climate change (CC), under HDC, compared to land use change (LUC) in the diminishing of the GWS and as a result, the severe occurrence of the subsidence in the region.

Analyses of Meteorological Drought and its Impacts on Groundwater Fluctuations, a Case Study: Marand Plain (Iran)

Analyses of Meteorological Drought and its Impacts on Groundwater Fluctuations, a Case Study: Marand Plain (Iran)

Assessment of Groundwater-Level Susceptibility to Degradation Based on Analytical Network Process (ANP)

Groundwater resources are among the world’s most important and vital supplies which are highly required for continuation of human life as well as wildlife. The growth of world’s population and the occurrence of climatic drought in recent decades have put a severe strain on these blessings all over the world so that most of the aquifers have dried up and the depth to groundwater in wells has deepened severely. In the present study the Analytic Network Process was used to extract degradation risk zones of groundwater in Marand Plain. To do this, all the effective factors including precipitation, air temperature, land use, soil permeability, flow direction, proximity to rivers and groundwater exploitation via wells and qanats were used. After construction of the database in GIS environment, the structure of the network was designed in Super Decision software. Clustering and pair-wise comparisons of the elements were executed based on the results of the Expert Knowledge questionnaire and the priority weights for each cluster and parameter was extracted consequently. Finally, the priority weights of the criteria were applied on the map layers and the final degradation risk map created overlaying all the maps. According the final output of the study, the Eastern and Western parts of Marand Plain have the highest and lowest susceptibilities respectively. It’s also found that the groundwater decline trend mimics the surface topography and flow direction in which has an East-West pattern.

A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping

In this paper, we evaluate the predictive performance of an adaptive neuro-fuzzy inference system (ANFIS) using six different membership functions (MF). In combination with a geographic information system (GIS), ANFIS was used for land subsidence susceptibility mapping (LSSM) in the Marand plain, northwest Iran. This area is prone to droughts and low groundwater levels and subsequent land subsidence damages. Therefore, a land subsidence inventory database was created from an extensive field survey. Areas of land subsidence or areas showing initial signs of subsidence were used for training, while one-third of inventory database were reserved for testing and validation. The inventory database randomly divided into three different folds of the same size. One of the folds was chosen for testing and validation. Other two folds was used for training. This process repeated for every fold in the inventory dataset. Thereafter, land subsidence related factors, such as hydrological and topographical factors, were prepared as GIS layers. Areas susceptible to land subsidence were then analyzed using the ANFIS approach, and land subsidence susceptibility maps were created, whereby six different MFs were applied. Lastly, the results derived from each MF were validated with those areas of the land subsidence database that were not used for training. Receiver operating characteristics (ROC) curves were drawn for all LSSMs, and the areas under the curves were calculated. The ROC analyses for the six LSSMs yielded very high prediction values for two out of the six methods, namely the difference of DsigMF (0.958) and GaussMF (0.951). The integration of ANFIS and GIS generally led to high LSSM prediction accuracies. This study demonstrated that the choice of training dataset and the MF significantly affects the results.

Mapping groundwater contamination risk of multiple aquifers using multi-model ensemble of machine learning algorithms

Constructing accurate and reliable groundwater risk maps provide scientifically prudent and strategic measures for the protection and management of groundwater. The objectives of this paper are to design and validate machine learning based-risk maps using ensemble-based modelling with an integrative approach. We employ the extreme learning machines (ELM), multivariate regression splines (MARS), M5 Tree and support vector regression (SVR) applied in multiple aquifer systems (e.g. unconfined, semi-confined and confined) in the Marand plain, North West Iran, to encapsulate the merits of individual learning algorithms in a final committee-based ANN model. The DRASTIC Vulnerability Index (VI) ranged from 56.7 to 128.1, categorized with no risk, low and moderate vulnerability thresholds. The correlation coefficient (r) and Willmott's Index (d) between NO3 concentrations and VI were 0.64 and 0.314, respectively. To introduce improvements in the original DRASTIC method, the vulnerability indices were adjusted by NO3 concentrations, termed as the groundwater contamination risk (GCR). Seven DRASTIC parameters utilized as the model inputs and GCR values utilized as the outputs of individual machine learning models were served in the fully optimized committee-based ANN-predictive model. The correlation indicators demonstrated that the ELM and SVR models outperformed the MARS and M5 Tree models, by virtue of a larger d and r value. Subsequently, the r and d metrics for the ANN-committee based multi-model in the testing phase were 0.8889 and 0.7913, respectively; revealing the superiority of the integrated (or ensemble) machine learning models when compared with the original DRASTIC approach. The newly designed multi-model ensemble-based approach can be considered as a pragmatic step for mapping groundwater contamination risks of multiple aquifer systems with multi-model techniques, yielding the high accuracy of the ANN committee-based model.

Identification of hydrogeochemical processes and pollution sources of groundwater resources in the Marand plain, northwest of Iran

The main aims of the present study are to identify the major factors affecting hydrogeochemistry of groundwater resources in the Marand plain, NW Iran and to evaluate the potential sources of major and trace elements using multivariate statistical analysis such as hierarchical clustering analysis (HCA) and factor analysis (FA). To achieve these goals, groundwater samples were collected in three sampling periods in September 2013, May 2014 and September 2014 and analyzed with regard to ions (e.g., Ca2+, Mg2+, Na+ and K+, HCO3−, SO42−, Cl−, F− and NO3−) and trace metals (e.g., Cr, Pb, Cd, Mn, Fe, Al and As). The piper diagrams show that the majority of samples belong to Na–Cl water type and are followed by Ca–HCO3 and mixed Ca–Na–HCO3. Cross-plots show that weathering and dissolution of different rocks and minerals, ion exchange, reverse ion exchange and anthropogenic activities, especially agricultural activities, influence the hydrogeochemistry of the study area. The results of the FA demonstrate that 6 factors with 81.7% of total variance are effective in the overall hydrogeochemistry, which are attributed to geogenic and anthropogenic impacts. The HCA categorizes the samples into two clusters. Samples of cluster C1, which appear to have higher values of some trace metals like Pb and As, are spatially located at the eastern and central parts of the plain, while samples of cluster C2, which express the salinization of the groundwater, are situated mainly westward with few local exceptions.

Investigate the Effect Meteorological and Hydrological Drought on Groundwater Quantity and Quality (Case Study: Marand Plain)

Investigate the Effect Meteorological and Hydrological Drought on Groundwater Quantity and Quality (Case Study: Marand Plain)

A comprehensive interferometric process for monitoring land deformation using ASAR and PALSAR satellite interferometric data

This study was conducted to apply the enhanced differential interferometric process to interferometric data obtained from C-band Advanced Synthetic Aperture Radar (ASAR) and Phased Array type L-Band Synthetic Aperture Radar (PALSAR) systems, in the Marand plain, Iran. In advance, the capability of each sensor was examined with regard to the signal coherency for sensor–target interactions, which emphasized on the terrific excellency of PALSAR (L-band) to ASAR (C-band) measurements in study area. In the interferometric process, in addition to resolving the topography and baseline related errors (which is conventional in the standard D-InSAR process), subtle quantitative methods were outlined to minimize the secondary, but momentous errors (i.e., orbital and atmospheric) from differential phases. Subsequently, based on the outlined process, the mean deformation as well as three-dimensional displacement maps (using ascending and descending modes) were generated. The deformation maps significantly indicated the downward motion of the surface with the maximum rate of −0.5 millimetre per day for the period, i.e., from 2004 to 2010, with relative dominance in eastern and central to northern parts of the Marand plain. Finally, the results were validated and the source of deformation was inspected using field data such as seismic history; changes in piezometric levels and cross-checking the results from radar measurements itself.

Water Erosion in Zonouz River Basin

This study focuses on the land erosion by water in Zonouz valley. It is located in the N.W. part of Iran which is surrounded by two mountain ranges and has very cold and snowy weather in winter and cool summers. River terraces and alluvial fans which were built by Zonouz river are considerably important in the area. The main river of the area which flows from east to the west covers an area of 4630km. The highest point of this basin is Soltan Sanjar Mt with 3200 meter elevation in the east and the lowest point is on the basin outlet in boundary city of Jolfa 300m elevation. It derives from Soltan- Sanjar Mt.in the east and flows to the west. In Cher- Cher area, it joins to Zilber river in Marand plain and finally enters into Aras river at border between Iran and Azerbaijan, finally reaches to the Caspian sea. This paper based on GIS methods, topographic map 1:50.000 and geologic map 1:100.000 scale in order to find out, the elevations, gradients, hydrographic layers, lithologic layers and orogenic movements which have affected on degradation of Zonouz valley. The precipitation, type of soil, gradient of land, effect of climate and hydrological cycles are the main factors that affect on land erosion. After eight years, a considerable change occurred in river discharge by making a dam on the river. The purpose of paper is to show the terraces and alluvial fans were formed before the construction of dam would be built on it. Flow of river in the area plays a major role on land degradation and creating various landforms. Thus, fluvial processes are the geomorphic process associated with running water. Therefore, fluvial landforms and landscapes are produced by currents of streams.Gradient of valley, the volume of water and form of valley play a major role on land degradation Rivers degrade (erode) and aggrade (deposit). Hence, the landscape contains degradational or erosional landforms which are created when gravels pebble and sands are removed. Depositional landforms are resulted from the accumulation of sediment. Badland of Zonouz is an example of such an erosional landscape. Resistant material on hills has given a beautiful shape to the hills.

Hydrogeochemical Characteristics and Evaluation of the Drinking and Irrigation Water Quality in Marand Plain, East Azerbaijan, NW Iran

In order to studyhydrogeochemistryof groundwater, samples of water were collected from Marand located in Eastern Azerbaijan province, NW of Iran