امیر قاسم زاده

Due to its environmental diversity, Iran ranks high in terms of crises caused by natural disasters. Flooding, as one of these disasters, is  causing severe social, economic, health, and environmental damage in many areas due to rapid urban growth and climate change. Therefore spatial forecasting of floods is crucial, as failure to identify flood risk areas in a catchment can exacerbate the destructive effects of floods. Recent advances in remote sensing, geographic information systems, machine learning, and statistical modelling have made it possible to produce highly accurate flood prediction maps. This study aims to predict flood risk areas in the Karun watershed using Sentinel satellite images and a novel ensemble approach with six machine learning models.

In this study, Synthetic Aperture Radar (SAR) data from Sentinel-1 images were used to identify areas affected by flooding.  First, the dates of heavy rainfall and flooding events in the study area were identified from various sources of information. Subsequently, Sentinel-1 images were obtained from the Copernicus database, representing the area before and after the flood events. The aforementioned data were processed using the SNAP platform. The identification of flood-affected areas was achieved through the application of the thresholding technique. For this purpose, the Normalized Difference Water Index (NDWI) generated from Sentinel-2 images and land cover classes indicating permanent water bodies were employed to determine the threshold for identifying flood-affected areas. The flood polygon layer was converted to a point layer, resulting in a total of 70 flood occurrence points. A review of previous studies and local characteristics identified seven main factors that significantly affect flood occurrence in the region. These factors include the Normalized Difference Vegetation Index (NDVI), Topographic Wetness Index (TWI), slope, flow direction, flow accumulation, distance from the river, and monthly rainfall. Additionally, the Digital Elevation Model (DEM) of the region was obtained from the SRTM database, and the spatial resolution of all factors was aligned with the DEM layer. Subsequently, various machine learning algorithms were employed to develop a combined model that provides more accurate predictions of flood-prone areas. The individual models include the Generalized Linear Model (GLM), Boosted Regression Tree (BRT), Support Vector Machine (SVM), Random Forest (RF), Multivariate Adaptive Regression Splines (MARS), and Maximum Entropy (MAXENT).                    

The results of this study indicate that the northeast of Aligudarz city, parts of Durud and Azna in Lorestan province, Khademmirza, Shahrekord, and Kiyar in Chaharmahal Bakhtiari province, Dana and Boyer Ahmad in Kohgiluyeh and Boyer Ahmad province, Semirom city in Isfahan province, and the southern border areas of Karun River in Khuzestan province have the highest flood potential in this basin. The performance evaluation of the models revealed that the Random Forest (RF) and Maximum Entropy (MaxEnt) models exhibited the highest accuracy among the individual models. These models, by combining environmental information and flood occurrence data, can produce highly accurate flood susceptibility maps. These maps can serve as crucial management tools to mitigate the adverse effects of floods and prevent development in vulnerable areas.

Overall, this study demonstrates that the use of an ensemble approach which combines machine learning models can provide more reliable results in the prediction of flood risk areas. The findings of this research are beneficial for managers and planners, as they can prevent development in vulnerable areas and consequently help reduce financial losses and human damages in the future.

Asphaltene deposition is one of the most common problems in the petroleum industry that could be very costly and cause many operational problems. In order to select the optimal operating conditions, deposition kinetics models can be very effective in treatment of asphaltene deposition at the field scale. In this study, asphaltene was first extracted from crude oil by standard methods and then its deposition kinetics on metal surfaces coated by Teflon and silica films was evaluated in both static and dynamic flow states. Investigations showed that the flow state is effective on the asphaltene deposition kinetics so that in the static state the nth-order kinetics model and in the dynamic state the double exponential model are in the best agreement with the experimental data. According to the results, at static state and concentration of 2000 ppm, the RF and SF values for Teflon coating are 78.8023 and 21.1976, respectively, indicating faster fast deposition process for this coating. Whereas for silica film, the RF value is lower than SF, indicating a faster slow deposition process for this coating.

Root-knot nematodes cause physiological and anatomical disorders in plants. The present study contains the results of an investigation of the effect of Meloidogyne incognita race2 on the growth of roots and aerial organs of tomato under greenhouse conditions. The experiment in a completely randomized design split split plot on two susceptible (Falat Y) and tolerante (Gina VF) tomatoes variety with four nematode population levels, 0 (control), 500, 1000 and 2000 second stage juveniles in four termination times (20, 40, 60 and 80 days after inoculation) and four replications was carried out. The species and race of nematode were determined after sampling and purification. Inoculum densities were obtained by reproduction of pure population on tomato Rutgers cultivar. Nematode population levels were inoculated at four-leaf stage and characteristics were measured at mentioned time. The results showed that increasing nematode populations levels, increased the number of galls, egg sacs and second stage juvenile in both cultivars, which changing were far more in the susceptible variety. But the reproduction factor decreased. Stress of nematodes affected plant morphological traits and growth indices. In high population levels, plant growth characters such as chlorophyll, leaf relative water content, stem diameter, leaf area, plant height, stem dry and fresh weight, root length/root volume, leaf area/ root level, root density and root dry weight/soil volume were decreased. But root fresh and dry weight, root area, root fresh weight/soil volume, root diameter, root volume and root area density were increased.

Root-knot nematodes (Meloidogyne spp.) are the limiting factors of agriculture crops including pepper. The M. incognita has widely distributed in pepper fields of Gilan Province. In this study, eleven pepper cultivars including AA pepper (Hyb. Coyame- Petoseed, Hyb. Aziz- Petoseed, Hyb. Bounty- Petoseed) and Bell peppers (California Wonder 301- Petoseed, Hyb. Camelot- Petoseed, Hyb. King Arthur- Petoseed, Hyb. Wizard- Petoseed, Hyb. Aristotle (SQ-R)- Petoseed, Hyb. Plato (SQ-R)- Petoseed, Mercury- Petoseed, California Wonder 301- Canyon) were evaluated against M. incognita race 2. Experiments conducted in a completely randomized design with four replications in greenhouse conditions with range of 22 to 28 Celsius degrees. Seedlings with six leaves were inoculated with 2000 second-stage juveniles/kg soils. Eight weeks after inoculation, growth indexes cultivars (Chlorophyll content of leaves, fresh and dry weight of shoot and root, root length and volume, length and diameter of shoot and number of healthy leaves) and nematode reproductive traits (Number of galls, egg masses, juveniles in the soil and nematode reproduction factor) were evaluated. In the final reaction of cultivars were determined based on reproduction factor gall and egg masses and resistance indices. The results revealed that pepper cultivars showed different ranges of sensitivity to the nematode. Based on the growth factors, hybrid wizard, California Wonder 301, Bavnty Hybrid, Mercury and Camelot hybrid cultivars were less sensitive than others cultivars. Among cultivars, California Wonder 301, Bavnty Hybrid, Mercury and Camelot hybrid showed less sensitivity in reproductive. The assessment of gall, egg masses and resistance indices revealed that Mercury and Camelot hybrid were evaluated as tolerant cultivars and King Arthur's hybrid and aziz hybrids as the most sensitive cultivars.

To investigate the effect of root-knot nematode M. incognita race2 on tomato defense enzyme production process, an experiment was conducted under greenhouse conditions. The experiment was carried out in a completely randomized split split plot design with two susceptible (Falat Y) and tolerant (Gina VF) tomatoe varieties with four nematode population levels of 0 (control), 500, 1000 and 2000, the number of larvae of second stage with four different sampling times (0 (control), 3, 5 and 7 days after inoculation) and with four replications. In this experiment, after sampling, purification, identification and propagation of nematode species and race of nematodes were determined. With nematode purified amplification on the Rutgers cultivar tomato inoculum was obtained. The four-leaf stages were inoculated population in levels and the time mentioned of the enzyme was measured. The results showed that increasing levels of nematode population affected plant biochemical activities and the total protein content decreased. The activity of catalase, peroxidase, polyphenol oxidase and phenylalanine ammonia lyase involved in the defense reaction of host plant increased. The highest rates were observed in the catalase enzyme and in the tolerant variety. In the presence of nematode stress, plant activated its defense mechanism that has enabled it to increase its enzymatic activity which was at its maximum value 5 days after inoculation.

Different species of the root knot nematodes (Meloidogyne spp.) and broomrape Orobanche aegyptica are parasitic of different plants roots. In order to study the interaction between the parasites of tomato, an experiment was conducted in a completely randomized design with four treatments 1- control, 2- nematode (2000, second stage juveniles), 3- seeds of orobanche (30 mg per kg of soil) and 4- nematode (2000, second stage juveniles), with seeds of orobanche (30 mg per kg of soil) with four replications under greenhouse conditions. Treated seeds were planted in sterilized soil. Four-leaf stage seedlings were inoculated with treatments and transferred to pots and fluorescence parameters were measured after 60 days. The results showed that both biological stresses caused a significant reduction in shoot dry weight, number of galls, chlorophyll, leaf number, leaf area, plant height, root length, stem diameter and photosystem II quantum efficiency in both light and darkness compared with control. Also the number of second stage larvae, egg sacs, accessories orobanche, nematode reproductive factor, the M, F, QP, QN, NPQ, Fo, Fm and Fv of both light and darkness in the combined treatment were increased. Whereas the dry weight of root in nematodes tension increased and were decreased in stress of aegyptian orobanche. The root volume was highest in alone nematode treatment. The results indicated that root- knot nematode and orobanche had a synergistic effect.

Most of the Iranian oil fields are fractured; these reservoirs tend to produce at high rate in their early production time followed by low rates later on, leading to low overall recovery. Secondary and tertiary recovery processes based on gas injection can extend the life reservoirs by maximizing the oil recovery especially in fractured reservoirs.However, the injection strategy needs to be studied carefully to optimize the overall efficiency. One of the best methods to find out the effectiveness of a certain process for reservoir development is building a small model representing all the properties of that reservoir with lesser complexity in order to gain the results much faster and easier. So in this paper immiscible gas injection is simulated using compositional model in a reservoir insouthwest of Iran and the effect of different parameters on gas injection performance and oil recovery is investigated and finally, the best scenario is selected.