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In the recent years, science and technology in urban green space have largely focused on technologies that facilitate infiltration and reduce runoff (such as rain gardens and permeable sidewalks). Trees in urban green space reduce the net rainfall by interception, and on the other hand, their extensive root systems allow them to store and direct significant amounts of water into the soil. The present study investigates the effect of rainfall amount and tree species on rainfall interception in Hashtgerd city of Alborz province during two seasons of winter 2017 and spring 2018. For this purpose, during seven rainfall events, the amount of throughfall was measured by the number of five rain gauges installed under each tree. In order to record rainfall events, a rain gage container was installed in a location that was sufficiently distant from buildings and trees, and rainfall events ranging from 2.1 to 6.8 mm were recorded. The results showed that the percentages of rainfall interception for spruce, apricot, fig, willow, walnut, and oak species were 44.6, 42.6, 36.4, 35.1, 33.6 and 30.4 percent, respectively. The results of statistical analysis showed that there is a significant difference among the values of rainfall interception in different tree species (P <0.01). Also, there is a significant difference among the rainfall interception in the rainfall classes (low (lower than 4 mm), medium (4-6 mm), and high (higher than 6 mm)) (P <0.01). Among the studied species, sparrow and apricot species have the highest rainfall interception, which it is possible to make more use of these two types in the control of runoff with urban planning. [ABSTRACT FROM AUTHOR]

Determining areas of high groundwater potential is important for exploitation, management, and protection of water resources. This study assesses the spatial distribution of groundwater potential in the Zarrinehroud watershed of Kurdistan Province, Iran using combinations of five statistical and machine learning algorithms – frequency ratio (FR), radial basis function (RBF), index of entropy (IOE), evidential belief function (EBF) and fuzzy art map (FAM). To accomplish this, 1448 well locations in the study area were randomly divided into two data sets for training (70%= 1013 locations) and validation (30%= 435 locations) based on the holdout method. Fourteen factors that can affect the presence or absence of groundwater were identified, measured, and mapped using ArcGIS and SAGA-GIS software. The models were used to predict the locations of groundwater based on suitable combinations of the conditioning factors to produce groundwater potential maps. The probability of groundwater at any location was classified as low, moderate, high, or very high based on natural breaks in the data spectrum. The model predictions were tested for validity and their success was determined using receiver operating characteristic (ROC) curves, standard errors (SE), positive predictive value (PPV), negative predictive value (NPV), sensitivity (SST), specificity (SPF) and accuracy (ACC), and the Friedman test. The performance assessments of groundwater potential predictions using the area under the curve (AUC) and accuracy (ACC) showed that the FR-RBF model had very good performance (AUC= 0.889, ACC= 87.51). FR-FAM (AUC= 0.869, ACC= 84.67), EBF-FAM (AUC= 0.864, ACC= 84.42), EBF-RBF (AUC= 0.854, ACC= 83.94), FR-IOE (AUC= 0.836, ACC= 83.62), and EBF-IOE (AUC= 0.833, ACC= 80.42) also had acceptable performance. The results of the Friedman test also show that there are significant differences between the models and the highest mean rank was generated by the FR-FAM model (3.642). Therefore, the hybrid models can be used to increase the accuracy of groundwater-prediction models in the study region and perhaps in similar settings. The groundwater potential was studied in the area of Zarrinehroud watershed A combination of methods including FR, RBF, IOE, EBF and FAM Very high and high groundwater potential areas were located in the northern half The development of hybrid models can increase the accuracy of the results [ABSTRACT FROM AUTHOR]

Floods are a common natural disaster that can endanger urban and rural infrastructure. The purpose of this study was to prepare and analyze the flood risk map of Khancheh and Baronagh villages along a part of Suk Cham river. First, the topographic map was created using Google Earth and Global mapper. Then, using a topographic map made in AutoCAD Civil 3D software, the surface was created for the study interval. In the next step, using the constructed surface of cross sections, longitudinal profiles, river channel, right, and left flood banks of Suk Cham river were constructed in AutoCAD Civil 3D software environment. To simulate floods at return periods of 25, 50, 100, and 200 years, the cross sections were transferred to HEC RAS software. The Phil-Steiner method was used to convert the maximum daily flow rate to the maximum instantaneous flow rate according to the P-value of the Kolmogorov-Smirnov test (0.9). The results of flood frequency analysis showed that Pearson type 3 log distribution is the most suitable distribution. Based on the results, the flooding level for the return periods of 25, 50, 100, 200 years was 0.77, 0.9, 1.15, 1.47 km2, respectively. According to the flood risk map in the 100-year return period (high risk area), about 41.5% and 4.5% of the two villages of Khancheh and Baronagh had at moderate to very high risk of flood, respectively. Establishing coastal walls and organizing the river along the river route through these two villages is essential, which can mitigate the risks of floods. The use of prepared plans for flood control measures, flood forecasting and warning systems, flood insurance and in general for structural and non-structural flood control measures in the region is recommended. [ABSTRACT FROM AUTHOR]

Although fractal mathematics has been commonly used to quantify particle size distribution (PSD), limited attention has been paid to the fractal dimension of the infiltration curve (DI). Therefore, it is necessary to determine whether the DI and fractal dimensions of PSD (Dm, Dclay, Dsilt and Dsand) can reflect the infiltration process, and soil erodibility and degradation. Accordingly, this study aimed to provide new information on the quantification of infiltration processes, and soil erodibility and degradation, using DI and Dm in the Soorak basin in the northern part of Iran. The soil and infiltration properties were calculated by field and laboratory practices on 46 topsoil samples and 34 infiltrometer double‐ring tests. Correlation, regression and redundancy analysis (RDA) were used to identify relationships between the infiltration curve and PSD coordinates with soil and infiltration properties. Results indicated that the DI was significantly linearly correlated with other infiltration curve characteristics, with the correlation coefficient ranging from −0.737 to −0.403 (sig. = 0.000–0.037). However, there was no significant correlation between DI and Dm, Dclay, Dsilt and Dsand. Different erodibility factors, especially KUSLE, had significant relations with Dm, Dclay, Dsilt, Dsand and DI (p <.01), suggesting that these fractal dimensions are indicators of soil degradation. Other findings indicated that the land use had no significant effect on DI and infiltration properties, whereas KUSLE, Dm, Dclay, DSilt and DSand were significantly affected by land‐use patterns (p <.01). These findings demonstrate that fractal dimensions of PSD and infiltration can be practicable indices to analyse soil degradation under different land‐use types. Highlights: How fractal geometry adds to understanding of the infiltration process and soil degradation.Fractal dimensions of infiltration (DI) can describe the infiltration process.Fractal dimensions of soil (Dm) and DI can be used as indicators of the soil erodibility.Erodibility and fractal properties of soil were significantly affected by land use. [ABSTRACT FROM AUTHOR]

We proposed an innovative hybrid intelligent approach, namely, the multiboost based naïve bayes trees (MBNBT) method for the spatial prediction of landslides in the Mu Cang Chai District of Yen Bai Province, Vietnam. The MBNBT, which is an ensemble of the multiboost (MB) and naïve bayes trees (NBT) base classifier, has rarely been applied for landslide susceptibility mapping around the world. For the modeling, we selected 248 landslide locations in the hilly terrain of the study area. Fifteen landslide conditioning factors were selected for the construction of the database based on the one-R attribute evaluation (ORAE) technique. Model validation was done using statistical metrics, namely, sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and the area under the receiver operating characteristics curve (AUC). Performance of the hybrid model was evaluated and compared with popular soft computing benchmark models, namely, multiple perceptron neural network (MLPN), Support Vector Machines (SVM), and single NBT. Results indicated that the proposed MBNBT (AUC = 0.824) model outperformed the popular models, namely, the MLPN (AUC = 0.804), SVM (AUC = 0.804), and NBT (AUC = 0.800) models. Analysis of the model results also suggested that the MB meta classifier ensemble model could enhance the prediction power of the NBT model. Therefore, the MBNBT is a suitable method for the assessment of landslide susceptibility in landslide prone areas. [ABSTRACT FROM AUTHOR]

The main objective of this study is to propose a novel hybrid model of a sequential minimal optimization and support vector machine (SMOSVM) for accurate landslide susceptibility mapping. For this task, one of the landslide prone areas of Vietnam, the Mu Cang Chai District located in Yen Bai Province was selected. In total, 248 landslide locations and 15 landslide-affecting factors were selected for landslide modeling and analysis. Predictive capability of SMOSVM was evaluated and compared with other landslide models, namely a hybrid model of the cascade generalization optimization-based support vector machine (CGSVM), individual models, such as support vector machines (SVM) and naïve Bayes trees (NBT). For validation, different quantitative criteria such as statistical based methods and area under the receiver operating characteristic curve (AUC) technique were used. Results of the study show that the SMOSVM model (AUC = 0.824) has the highest performance for landslide susceptibility mapping, followed by CGSVM (AUC = 0.815), SVM (AUC = 0.804), and NBT (AUC = 0.800) models, respectively. Thus, the proposed novel SMOSVM model is a promising method for better landslide susceptibility mapping and prediction, which can be applied also in other landslide prone areas. [ABSTRACT FROM AUTHOR]

Landslides have multidimensional effects on the socioeconomic as well as environmental conditions of the impacted areas. The aim of this study is the spatial prediction of landslide using hybrid machine learning models including bagging (BA), random subspace (RS) and rotation forest (RF) with alternating decision tree (ADTree) as base classifier in the northern part of the Pithoragarh district, Uttarakhand, Himalaya, India. To construct the database, ten conditioning factors and a total of 103 landslide locations with a ratio of 70/30 were used. The significant factors were determined by chi-square attribute evaluation (CSEA) technique. The validity of the hybrid models was assessed by true positive rate (TP Rate), false positive rate (FP Rate), recall (sensitivity), precision, F-measure and area under the receiver operatic characteristic curve (AUC). Results concluded that land cover was the most important factor while curvature had no effect on landslide occurrence in the study area and it was removed from the modelling process. Additionally, results indicated that although all ensemble models enhanced the power prediction of the ADTree classifier (AUCtraining = 0.859; AUCvalidation = 0.813); however, the RS ensemble model (AUCtraining = 0.883; AUCvalidation = 0.842) outperformed and outclassed the RF (AUCtraining = 0.871; AUCvalidation = 0.840), and the BA (AUCtraining = 0.865; AUCvalidation = 0.836) ensemble model. The obtained results would be helpful for recognizing the landslide prone areas in future to better manage and decrease the damage and negative impacts on the environment. [ABSTRACT FROM AUTHOR]