Khabat Khosravi

Florida International University | FIU · Department of Earth and Environment

Accurate assessment of soil water erosion (SWE) susceptibility is critical for reducing land degradation and soil loss, and for mitigating the negative impacts of erosion on ecosystem services, water quality, flooding and infrastructure. Deep learning algorithms have been gaining attention in geoscience due to their high performance and flexibility...

Flood susceptibility maps are useful tool for planners and emergency management professionals in the early warning and mitigation stages of floods. In this study, Sentinel-1 decibel radar images, which provide Synthetic-Aperture Radar (SAR) data were used to delineate flooded and non-flooded locations. 12 input parameters, including elevation, lith...

In the present study, three widely-used modeling approaches: (1) sediment rating curve (SRC) and optimized OSRC, (2) machine learning models (ML) (random forest (RF) and Dagging-RF (DA-RF)) and (3) the semi-physically based soil and water assessment tool (SWAT) are applied to predict suspended sediment load (Qs ) at the Talar watershed in Iran. Var...

Snow Water Equivalent (SWE) is one of the most critical variables in mountainous watersheds and needs to be considered in water resources management plans. As direct measurement of SWE is difficult and empirical equations are highly uncertain, the present study aimed to obtain accurate predictions of SWE using machine learning methods. Five standa...

Suspended sediment load modeling through advanced computational algorithms is of major importance and a challenging topic for developing highly accurate hydrological models. To model the suspended sediment load in the Rampur watershed station in the Mahanadi River Basin, Chhattisgarh State, India, unique integrated computational intelligence regres...

Abstract: The accurate forecasts and estimations of the amount of sediment transported by rivers are critical concerns in water resource management and soil and water conservation. The identification of appropriate and applicable models or improvements in existing approaches is needed to accurately estimate the suspended sediment concentration (SSC...

Current study presents the boosting of two base models, including a Heuristic Search Algorithm for finding the k shortest paths (K*) and an alternating model tree (AM Tree) through combining bagging (BA), dagging (DA), and random subspace (RS) hybridize models, to predict monthly suspended sediment load (SSL) in subtropical monsoon climatic regions...

Soil water erosion (SWE) is an important global hazard that affects food availability through soil degradation, a reduction in crop yield, and agricultural land abandonment. A map of soil erosion susceptiblity is a first and vital step in land management and soil conservation. Several machine learning (ML) algorithms optimized using the Grey Wolf O...

The free overfall is a simple and widely used device for measuring discharge in open irrigation channels and agricultural research projects. However, the direct measurement of discharge can be difficult and time consuming with care needed to minimize potential inaccuracies of empirical equations applied to site-specific conditions. Thus, in the pre...

The objective of this study was to improve the predictability of the GALDIT (G: groundwater occurrence, A: aquifer hydraulic conductivity, L: level of groundwater above sea level, D: distance from the shoreline, I: impact of the seawater intrusion, and T: thickness of the aquifer) groundwater vulnerability model using machine leaning methods. This...

Infiltration is the process by which water enters the soil, and it plays a significant role in the hydrologic cycle. Direct measurement of infiltration is time consuming; however, empirical and physical models are inaccurate. In this study, we compared the results of a deep learning-based convolutional neural network (CNN) algorithm with those of m...

Land subsidence (LS), which mainly results from poor watershed management, is a complex and non-linear phenomenon. In the present study, LS at a country-wide assessment of Iran was mapped by using several geo-environmental conditioning factors (namely, altitude, slope degree and aspect, plan and profile curvature, distance from a river, road, or fa...

Direct soil temperature (ST) measurement is time-consuming and costly; thus, the use of a simple and cost-effective machine learning (ML) tool is helpful. In this study, ML approaches, including KStar, instance-based K-nearest learner (IBK) and locally weighted learner (LWL) coupled with resampling algorithms of bagging (BA) and dagging (DA) were d...

The distribution and transportation of suspended sediment load (Qssl) in rivers have a significant effect on the design of hydraulic structures, river morphology, water quality, and aquatic ecosystems. As direct measurement of Qssl can be costly and time-consuming, reliable estimates are vital for watershed management. In the present study, four st...

Labyrinth weirs are utilized to transport a greater discharge during floods in contrast to conventional weirs due to their increased weir crest length. Nevertheless, due to the increased geometric complexity of labyrinth weirs, determination of accurate discharge coefficients and accordingly, head-discharge ratings are quite essential issues in pra...

Landslides are a geological hazard that can pose a serious threat to human health and the environment of highlands or mountain slopes. Landslide susceptibility mapping is an essential tool for predicting and mitigating landslides. This study aimed to investigate the application of deep learning algorithms based on convolutional neural networks (CNN...

Direct soil temperature (ST) measurement is time-consuming and costly; thus, the use of a simple and cost-effective machine learning (ML) tool is helpful. In this study, ML approaches, including KStar, instance-based K-nearest learner (IBK) and locally weighted learner (LWL) coupled with resampling algorithms of bagging (BA) and dagging (DA) were d...

From a watershed management perspective, streamflow need to be predicted accurately using simple, reliable, and cost-effective tools. Present study demonstrates the first applications of a novel optimized deep-learning algorithm of a convolutional neural network (CNN) using BAT metaheuristic algorithm (i.e., CNN-BAT). Using the prediction powers of...

Random Tree (RT) and Iterative Classifier Optimizer (ICO) based on Alternating Model Tree (AMT) regressor machine learning (ML) algorithms coupled with Bagging (BA) or Additive Regression (AR) hybrid algorithms were applied to forecasting multistep ahead (up to three months) Lake Superior and Lake Michigan water level (WL). Partial autocorrelation...

The objective of the current study is groundwater vulnerability assessment using DRASTIC, modified DRASTIC and three statistical bivariate models (Frequency Ratio (FR), Evidential Belief Function (EBF), and Weights-of-Evidence (WOE)) for Sari-Behshahr plain, Iran. A total of 218 wells were sampled for nitrate concentration measurement in 2015. Data...

Scour depth prediction and its prevention is one of the most important issues in channel and waterway design. However the potential for machine learning algorithms to provide models of scour depth has yet to be explored. This study provides the first quantification of the predictive power of a range of standalone and hybrid machine learning models....

Accurate streamflow (Qt) prediction can provide critical information for urban hydrological management strategies such as flood mitigation, long-term water resources management, land use planning and agricultural and irrigation operations. Since the mid-20th century, Artificial Intelligence (AI) models have been used in a wide range of engineering...

Accurate prediction of stable alluvial hydraulic geometry, in which erosion and sedimentation are in equilibrium, is one of the most difficult but critical topics in the field of river engineering. Data mining algorithms have been gaining more attention in this field due to their high performance and flexibility. However, an understanding of the po...

Labyrinth weirs are utilized to increase the weir crest length to transport a greater discharge during floods in contrast to conventional weirs. Nevertheless, due to the increased geometric complexity of labyrinth weirs, determination of accurate discharge coefficients and accordingly, head-discharge ratings are quite essential issues in practical...

In the current paper, the efficiency of three new standalone data mining algorithms [e.g., M5P, Random Forest (RF), M5Rule (M5R)] and six novel hybrid algorithms of Bagging, BA (BA-M5P, BA-RF and BA-M5R) and Attribute Selected Classifier, ASC (ASC-M5P, ASC-RF and ASC-M5R) for streamflow prediction were assessed and compared with autoregressive inte...

So far, few studies have focused on the concept of critical flow velocity rather than bed shear stress for incipient sediment motion. Moreover, few studies have focused on sediment mixtures (graded sediment) and shape rather than uniform sediment for incipient motion condition. Different experiments were conducted at a hydraulic laboratory at the U...

Trace element (TE) pollution in groundwater resources is one of the major concerns in both developing and developed countries as it can directly affect human health. Arsenic (As), Barium (Ba), and Rubidium (Rb) can be considered as TEs naturally present in groundwater due to water-rock interactions in Campania Plain (CP) aquifers, in South Italy. T...

Sediment transport modeling has been known as an essential issue and challenging task in water resources and environmental engineering. In order to minimize the adverse impacts of the continues sediment deposition that is known as a main source of pollution in the urban area, the self-cleansing method is widely utilized for designing the sewer pipe...

Study region Sixteen different sites from two provinces (Lorestan and Illam) in the western part of Iran were considered for the field data measurement of cumulative infiltration, infiltration rate, and other effective variables that affect infiltration process. Study focus Soil infiltration is recognized as a fundamental process of the hydrologic...

Water infiltration into the soil is an important process in the hydrologic cycle, but its measurement is difficult, time-consuming and costly. Empirical and physical models have been developed to predict cumulative infiltration (CI), but are often found to be inaccurate. In this study, several novel standalone machine learning algorithms (M5P, Deci...

Complex vortex flow patterns around bridge piers, especially during floods, cause scour process that can result in the failure of foundations. Abutment scour is a complex three-dimensional phenomenon that is difficult to predict especially with traditional formulas obtained using empirical approaches such as regressions. This paper presents a test...

Suspended sediment load is a substantial portion of the total sediment load in rivers and plays a vital role in determination of the service life of the downstream dam. To this end, estimation models are needed to compute suspended sediment load in rivers. The application of artificial intelligence (AI) techniques has become popular in water resour...

Due to excessive exploitation, groundwater resources of coastal regions are exposed to seawater intrusion. Therefore, vulnerability assessments are essential for the quantitative and qualitative management of these resources. The GALDIT model is the most widely used approach for coastal aquifer vulnerability assessment, but suffers from subjectivit...

Shear stress distribution prediction in open channels is of utmost importance in hydraulic structural engineering as it directly affects the design of stable channels. In this study, at first, a series of experimental tests were conducted to assess the shear stress distribution in prismatic compound channels. The shear stress values around the whol...

Reliable flash flood susceptibility maps are a vital tool for land planners and emergency management officials for early flood warning and mitigation. We have developed a new ensemble learning model that predicts flash flood susceptibility at Haraz, Iran. The new model couples a Bayesian Belief Network (BBN) model with an extreme learning machine (...

Electrical conductivity (EC), one of the most widely used indices for water quality assessment, has been applied to predict the salinity of the Babol-Rood River, the greatest source of irrigation water in northern Iran. This study uses two individual-M5 Prime (M5P) and random forest (RF)-and eight novel hybrid algorithms-bagging-M5P, bagging-RF, ra...

Iran experiences frequent destructive floods with significant socioeconomic consequences. Quantifying the accurate impacts of such natural hazards, however, is a complicated task. The present study uses a deep learning convolutional neural networks (CNN) algorithm, which is among the newer and most powerful algorithms in big data sets, to develop a...

Groundwater is the primary source of safe water for drinking and agricultural applications in the Maku Plain in northwest Iran. This area is impacted by fluoride contamination, thus, accurate modeling techniques for predicting groundwater fluoride concentration are required. The current paper advances several novel data mining algorithms including...

This study examines the relations between structures and shapes of streambed evolution after dam-break floods. A flume was used to simulate dam-break floods with variations of initial upstream water levels and variance, from uniform to graded, of bed sediments. Detailed measurements of the state and composition were made during these experiments. T...

Due to climate change and growth of urban communities, the need for new sources of freshwater, especially groundwater, is increasing in water-deficient countries like Iran. This study therefore aimed at groundwater potential mapping (GPM) of Nahavand plain, Iran, using optimized adaptive neuro fuzzy inference system (ANFIS) in geographic informatio...

The identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses. In this study, we applied two novel deep learning algorithms, the recurrent neural network (RNN) and convolutional neural network (CNN), for national-scale landslide susceptibility mapping of Iran. We prepared...

Dam break flows and resulting river bed erosion can have disastrous impacts on human safety, infrastructure, and environmental quality. However, there is a lack of research on the mobility of non-uniform sediment mixtures resulting from dam break flows and how these differ from uniform sized sediment. In this paper, laboratory flume experiments rev...

The declining water level in Lake Urmia has become a significant issue for Iranian policy and decision makers. This lake has been experiencing an abrupt decrease in water level and is at real risk of becoming a complete saline land. Because of its position, assessment of changes in the Lake Urmia is essential. This study aims to evaluate changes in...

Human activities are deeply connected with groundwater reservoirs, so protecting them from pollution has become a priority in many regions of the world. Nitrate is considered the main groundwater pollutant since it is directly linked to many human activities. Agricultural activities and domestic wastewater have been identified as the main sources o...

The predictive capability of a new artificial intelligence method, called random subspace (RS), for the prediction of suspended sediment load in rivers has been compared with commonly used methods: random forest (RF) model and two support vector machine (SVM) models using a radial basis function kernel (SVM-RBF) and a normalized polynomial kernel (...

River water quality assessment is one of the most important tasks to enhance water resources management plans. A water quality index (WQI) considers several water quality variables simultaneously. Traditionally WQI calculations consume time and are often fraught with errors during derivations of sub-indices. In this study, 4 standalone (random fore...

The accurate prediction of bedload transport in gravel-bed rivers remains a significant challenge in river science. However the potential for data mining algorithms to provide models of bedload transport have yet to be explored. This study provides the first quantification of the predictive power of a range of standalone and hybrid data mining mode...

Groundwater resources constitute the main source of clean fresh water for domestic use and it is essential for food production in the agricultural sector. Groundwater has a vital role for water supply in the Campanian Plain in Italy and hence a future sustainability of the resource is essential for the region. In the current paper novel data mining...

Mapping flood-prone areas is a key activity in flood disaster management. In this paper, we propose a new flood susceptibility mapping technique. We employ new ensemble models based on bagging as a meta-classifier and K-Nearest Neighbor (KNN) coarse, cosine, cubic, and weighted base classifiers to spatially forecast flooding in the Haraz watershed...

Shear stress distribution prediction in open channels is of utmost importance in hydraulic structural engineering as it directly affects the design of stable channels. In this study, at first, a series of experimental tests were conducted to assess the shear stress distribution in prismatic compound channels. The shear stress values around the whol...

2019): River suspended sediment load prediction based on river discharge information: application of newly developed data mining models, Hydrological Sciences Journal, Abstract Suspended sediment load (SSL) is one of the essential hydrological processes that affects river engineering sustainability. Sediment has major influence on the operation of...

The objective of this study was to experimentally evaluate the difference in the transport of uniform (5.17, 10.35, 14, 20.7 mm) and graded sediment (mixture of these rounded particles with equal weight proportions) under different unsteady flow hydrographs in a 12 m long, 0.5 m wide and deep glass-walled flume. There was a lag time between fractio...

To investigate a regions’ hydrology through modelling, it is critical to first be able to accurately simulate reference evaporation (ET0) from available regional meteorological parameters. Nine models including: (i) five data-mining algorithms (M5P, random forest, random tree, Reduced Error Pruning Tree and Kstar), and (ii) four adaptive neuro-fuzz...

Bed-load transport plays a critical role in river morphological change and has an important impact on river ecology. Although there is good understanding of the role of the variation of river bed grain size on transport dynamics in equilibrium conditions, much less is understood for non-equilibrium conditions when the channel is either aggrading or...

This study proposes a hybrid computational intelligence model that is a combination of alternating decision tree (ADTree) classifier and AdaBoost (AB) ensemble, namely “AB–ADTree”, for groundwater spring potential mapping (GSPM) at the Chilgazi watershed in the Kurdistan province, Iran. Although ADTree and its ensembles have been widely used for en...

The most dangerous landslide disaster always causes serious economic losses and people’s death on humanity. The contribution of this work is to present an integrated landslide modelling framework, in which an adaptive neuro-fuzzy inference system (ANFIS) is combined with the two optimization algorithms of whale optimization algorithm (WOA) and grey...

Since it is not possible to determine the exact time of a natural disaster's occurrence and the amount of physical and financial damage on humans or the environment resulting from their event, decision-makers need to identify areas with potential vulnerability in order to reduce future losses. In this paper, a GIS-based open source software entitle...

Drought prediction is very important in the planning and management of natural resources and water resources. Karkheh river basin is one of the considerable water resources field in Iran and it is located in west parts of Iran. In this paper using precipitation data of 7 meteorological stations from year 1987 to 2014 and also applying SPI index in...

Landslides are the most frequent phenomenon in the northern part of Iran, which cause considerable financial and life damages every year. One of the most widely used approaches to reduce these damages is preparing Landslide Susceptibility Map (LSM) using suitable methods and selecting the proper conditioning factors. The current study is aimed at c...

Floods are some of the most dangerous and most frequent natural disasters occurring in the northern region of Iran. Flooding in this area frequently leads to major urban, financial, anthropogenic, and environmental impacts. Therefore, the development of flood susceptibility maps used to identify flood zones in the catchment is necessary for improve...

This study investigated the characteristics of rainfall-triggered landslides during the Typhoon Bilis in the Dongjiang Reservoir Watershed, China. The comparative shallow landslide susceptibility mappings (LSMs) were produced by the ensemble data-driven statistical models in a GIS environment. At first, the landslide inventory for the study area wa...

Momentum exchange in the mixing region between the floodplain and the main channel is an essential hydraulic process particularly for the estimation of discharge. The current study investigated various data mining models to estimate apparent shear stress in a symmetric compound channel with smooth and rough floodplains. The applied predictive model...

The main goal of this study is to optimize an adaptive neuro-fuzzy inference system (ANFIS) using three meta-heuristic optimization algorithms including genetic algorithm (GA), biogeography-based optimization (BBO) and simulated annealing (SA) to prepare groundwater potential maps. The methodology was applied to the Booshehr plain, Iran. The result...

Landslides represent a part of the cascade of geological hazards in a wide range of geo-environments. In this study, we aim to investigate and compare the performance of two state-of-the-art machine learning models, i.e., decision tree (DT) and random forest (RF) approaches to model the massive rainfall-triggered landslide occurrences in the Izu-Os...