Landslide susceptibility assessment in the Anfu County,China: comparing different statistical and probabilistic models considering the new topo-hydrological factor (HAND)

The present study is aimed at producing landslide susceptibility map of a landslide-prone area (Anfu County, China) by using evidential belief function (EBF), frequency ratio (FR) and Mahalanobis distance (MD) models. To this aim, 302 landslides were mapped based on earlier reports and aerial photographs, as well as, carrying out several field surveys. The landslide inventory was randomly split into a training dataset (70%; 212landslides) for training the models and the remaining (30%; 90 landslides) was cast off for validation purpose. A total of sixteen geo-environmental conditioning factors were considered as inputs to the models: slope degree, slope aspect, plan curvature, profile curvature, the new topo-hydrological factor termed height above the nearest drainage (HAND), average annual rainfall, altitude, distance from rivers, distance from roads, distance from faults, lithology, normalized difference vegetation index (NDVI), sediment transport index (STI), stream power index (SPI), soil texture, and land use/cover. The validation of susceptibility maps was evaluated using the area under the receiver operating characteristic curve (AUROC). As a results, the FR outperformed other models with an AUROC of 84.98%, followed by EBF (78.63%) and MD (78.50%) models. The percentage of susceptibility classes for each model revealed that MD model managed to build a compendious map focused at highly susceptible areas (high and very high classes) with an overall area of approximately 17%, followed by FR (22.76%) and EBF (31%). The premier model (FR) attested that the five factors mostly influenced the landslide occurrence in the area: NDVI, soil texture, slope degree, altitude, and HAND. Interestingly, HAND could manifest clearer pattern with regard to landslide occurrence compared to other topo-hydrological factors such as SPI, STI, and distance to rivers. Lastly, it can be conceived that the susceptibility of the area to landsliding is more subjected to a complex environmental set of factors rather than anthropological ones (residential areas and distance to roads). This upshot can make a platform for further pragmatic measures regarding hazard-planning actions.     

Trend analysis of hydroclimatological variables in Urmia lake basin using hybrid wavelet Mann–Kendall and Şen tests

This paper investigates monthly, seasonal, and annual trends in rainfall, streamflow, temperature, and humidity amounts at Urmia lake (UL) basin and analyzes the interaction between these variables and UL’s water level fluctuation during the 1971–2013 period. Two new methods including nonparametric hybrid wavelet Mann–Kendall test and ?en’s methodology have been used to determine potential trends in the variables and their dominant periods. The results showed significant decreasing trends in the water level and streamflow series, moderate decreasing trend in the rainfall and relative humidity series, and increasing trends in the observed temperature data. The 8- , 12-month, and 2-year periods were detected as the dominant periods of the variables in monthly, seasonal, and annual timescales, respectively. The results from the interaction analysis revealed that the main factor influencing the water level at UL is decreasing trend in the streamflow series. Both the monthly series of UL’s water level and the streamflow series of the stations indicated two start points of significant decreasing trend in 1973 and 1998. Furthermore, a comparative analysis among the applied methods indicated a good agreement between the results of hybrid wavelet Mann–Kendall test and ?en’s trend analyzing method.     

Geospatial modeling to identify the effects of anthropogenic processes on landscape pattern change and biodiversity

This research used geospatial data to quantify biodiversity changes and landscape pattern change to track anthropogenic impacts of such changes at the Mouteh Wildlife Refuge (MWR), Isfahan, Iran. Satellite image duration of four decades, LandSat_1-5, and IRS-P6 data were used to develop land cover classification maps for 1971, 1987, 1998, and 2011. The number and size of land cover patches, the degree of naturalness, and the diversity indices were calculated and compared for a 40-year period. The results showed an increasing concern with regard to unplanned human activities. Some improvements of the natural landscape also occurred in the core protected zone of the study area. The number and size of land cover patches, the degree of naturalness, and the diversity indices were calculated. Overall changes in natural land use between 1971 and 1998 at MWR showed that the number of patches for natural land use has increased, but it also showed a decrease in 2011. Similar changes were observed for seminatural land use. Within the artificial classes, the number and area of patches were higher and the largest patch occurred in 2011. The maximum variation of diversity is related to the year 2011. The results showed an increasing concern with regard to unplanned human activities. Some improvements of the natural landscape also occurred in the core protected zone of the study area. Remote sensing and geographic information system offers an important means of detecting and analyzing temporal changes occurring in our landscape.     

Investigation of the Relationship between Sediment Graph and Hydrograph of Flood Events (Case Study: Gharachay River Tributaries,Arak, Iran)

Water Resources - In the comprehensive management of watersheds, the knowledge of the sedimentation process in flood conditions is considered as an important and challenging issue. The aim of this...     

Multi-level Optimization of Reservoir Scheduling Using Multi-resolution Wavelet-Based Up-scaled Models

This paper presents a multi-level procedure for production and injection scheduling through a numerical model-based optimization of well control variables. To calculate the net present value (NPV), the objective function of optimization, this procedure uses a number of discretized systems for a reservoir model with different degrees of up-scaling prepared according to a multi-resolution wavelet technique. These up-scaled models were incorporated into optimization based on a probability function. In early optimization iterations, due to the necessity to explore the search space quickly, the coarsest grid model has a higher chance for selection than the others; however, by a selection (with a low probability) of the finest up-scaled grid model in these iterations, solutions and objective function were tuned. In the later iterations of optimization, the finest up-scaled grid model probability was the highest in order to ensure the reliability of the final solution. The optimization algorithm is an adaptive simulated annealing algorithm coupled with a polytope. This procedure was evaluated in two case studies. The first case study was a horizontal 2D oil model with water flooding. The second case study was a vertical 2D oil model with gas injection. The results show that the proposed optimization procedure provides approximately the same accuracy compared to the situation in which the fine grid model is used for all the optimization iterations. Also, the run-time for the proposed optimization procedure is comparable to the run-time of the optimization in which only the coarsest grid model is used to calculate objective function. Moreover, the superiority of the wavelet-based up-scaling over an analogous multiple grid system optimization using uniformly up-scaled models is presented.

     

Aquifer vulnerability assessment using GIS and fuzzy system: a case study in Tehran–Karaj aquifer,Iran

Aquifer vulnerability assessment techniques have been developed to predict which areas are more likely than others to become contaminated as a result of activities at or near the land surface. This research focuses on the evaluation of groundwater vulnerability to pollution in an urban area. Among several assessment methods, DRASTIC has been selected for this study. ArcGIS has been used to overlay and calculate different layers and obtain the vulnerability map. In order to show the importance of fuzzy algorithms in classification, both Boolean and fuzzy algorithms were used and compared. The fuzzy algorithm could recognize the areas with low and negligible vulnerability potentials whereas the Boolean model classified them as moderate. Two sensitivity tests, the map removal sensitivity analyses and single-parameter sensitivity analysis, were performed to show the importance of each parameter in the index calculation.     

Tidal asymmetry in a tidal creek with mixed mainly semidiurnal tide,Bushehr Port,Persian Gulf

This study investigated the tidal asymmetry imposed by both the interaction of principal tides and the higher harmonics generated by distortions within a tidal creek network with mixed mainly semidiurnal tide in the Bushehr Port, Persian Gulf. Since velocity and water-level imposed by principal triad tides K₁-O₁-M₂ are in quadrature, duration asymmetries during a tidal period in this short, shallow inverse estuary should be manifest as skewed velocities. The principal tides produce periodic asymmetries including a strong ebb-dominance and a weak flood-dominance condition during spring and neap tides respectively. The higher harmonics induced by nonlinearities engender a flood-dominance condition where the convergence effects are higher than frictional effects, and an ebbdominance condition where intertidal storage are extended. Since the triad K₁-O₁-M₂ driven asymmetry is not overcome by higher harmonics close to the mouth, the periodic asymmetry dominates within the creek in which higher harmonics reinforce the weak flood-dominance (strong ebb-dominance) condition in the convergent channel (divergent area). Also, the maximum flood and the maximum ebb from all harmonic constituents occurred close to high water slack time during both spring and neap tides in this short creek. Since occational wetting of intertidal areas happened close to the high water (HW) time during spring tide, the water level flooded slowly close to the HW time of the spring tide.     

Assessment of nitrate contamination in unsaturated zone of urban areas: The case study of Tehran,Iran

Modeling fate of nitrogen in unsaturated and saturated zone is a complex process, which requires detailed geochemical data. Complexities of the process as well as data insufficiencies are two major issues, which make quantitative assessment of the problem more complicated. In this work, a lumped-parameter model (LPM) is proposed that has been evaluated for a data-limited case to study temporal and steady-state behavior of Nitrate and Ammonium in unsaturated zone. The concentration of components in the model are assumed as the depth-averaged concentrations, and dispersive fluxes have been neglected. The case study area is Tehran City aquifer, which is highly contaminated by domestic wastewater discharge into the groundwater. Three sources for Nitrogen contamination have been studied, namely domestic wastewater, park/recreational applications, and wet deposition. Results show that Nitrate production in the first few meters (<5 m) is quite large. To compare the model results with the measurements, error estimation analysis based on Vornoi map has been fulfilled. Finally, it is shown that there is a fairly good correlation coefficient between simulation and measurements, which approves the validity of the LPM.     

Landslide susceptibility mapping at Zonouz Plain,Iran using genetic programming and comparison with frequency ratio,logistic regression,and artificial neural network models

Without a doubt, landslide is one of the most disastrous natural hazards and landslide susceptibility maps (LSMs) in regional scale are the useful guide to future development planning. Therefore, the importance of generating LSMs through different methods is popular in the international literature. The goal of this study was to evaluate the susceptibility of the occurrence of landslides in Zonouz Plain, located in North-West of Iran. For this purpose, a landslide inventory map was constructed using field survey, air photo/satellite image interpretation, and literature search for historical landslide records. Then, seven landslide-conditioning factors such as lithology, slope, aspect, elevation, land cover, distance to stream, and distance to road were utilized for generation LSMs by various models: frequency ratio (FR), logistic regression (LR), artificial neural network (ANN), and genetic programming (GP) methods in geographic information system (GIS). Finally, total four LSMs were obtained by using these four methods. For verification, the results of LSM analyses were confirmed using the landslide inventory map containing 190 active landslide zones. The validation process showed that the prediction accuracy of LSMs, produced by the FR, LR, ANN, and GP, was 87.57, 89.42, 92.37, and 93.27 %, respectively. The obtained results indicated that the use of GP for generating LSMs provides more accurate prediction in comparison with FR, LR, and ANN. Furthermore; GP model is superior to the ANN model because it can present an explicit formulation instead of weights and biases matrices.