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[EN] Floods are a consequence of extreme rainfall events. Although surface runoff generation is the origin of discharge, flood research usually focuses on lowlands where the impact is higher. Runoff and sediment delivery at slope and pedon scale receiving much less attention in the effort to understand flood behaviour in time and space. This is especially relevant in areas where, due to climatic and hydrogeological conditions, streams are ephemeral, so-called dry rivers (¿wadis¿, "ramblas" or ¿barrancos¿) that are widespread throughout the Mediterranean. This paper researches the relationship between water delivery at pedon and slope scale with dry river floods in Macizo del Caroig, Eastern Iberian Peninsula. Plots of 1x1, 1x2, 1x4, and 2x8 m located in the ¿El Teularet¿ Soil Erosion and Degradation Research Station were monitored from 2004 to 2014 to measure soil and water delivery. Rainfall and flow at the dry river Barranco de Benacancil were also monitored. Results show that runoff and sediment discharge were concentrated in few events during the 11 years of research. A single flood event was registered in the channel on September 28, 2009, however, the runoff was registered 160 times at the plots. Runoff discharge was dependent on the size of the plots, with larger plots yielding lower runoff discharge per unit area, suggesting short runoff-travel distance and duration. Three rainfall events contributed with 26% of the whole runoff discharge, and five achieved 56% of the runoff. We conclude that the runoff generated at the plot scale is disconnected from the main channel. From a spatial point of view, there is a decrease in runoff coefficient along the slope. From a temporal point of view, the runoff is concentrated in a few rainfall events. These results show that the runoff generated at plot and slope scale does not contribute to the floods except for rainfall events with more than 100 mm day-1. The disconnection of the runoff and sediment delivery is confirmed by the reduction in the runoff delivery at plot scale due to the control of the length of the plot (slope) on the runoff and sediment delivery., [ES] . Lasinundacionesson consecuencia de lluvias extremas. Aunque la generación de escorrentía superficial es el origen de la descarga, la investigación de inundaciones generalmente se enfoca en las tierras bajas donde el impacto es mayor. La escorrentía y la distribución de sedimentos a escala de pendiente y pedón reciben mucha menos atención en la comprensión del comportamiento de las inundaciones en el tiempo y el espacio. Esto es especialmente relevante en zonas donde, debido a las condiciones climáticas e hidrogeológicas, los cauces son efímeros. Son los llamados ríos secos (¿wadis¿, ¿ramblas¿ o ¿barrancos¿) muy extendidos por todo el Mediterráneo. Este artículo investiga la relación entre el suministro de agua a escala de pedón y ladera con las crecidas de ríos secos en Macizo del Caroig, este de la Península Ibérica. Las parcelas de 1x1, 1x2, 1x4 y 2x8 m localizadas en la Estación de Investigación de Erosión y Degradación de Suelos ¿El Teularet¿ fueron monitoreadas de 2004 a 2014 para medir la producción de suelo y agua. También se monitorearon las precipitaciones y el caudal en el río seco Barranco de Benacancil. Los resultados muestran que la escorrentía y la descarga de sedimentos se concentraron en pocos eventos durante los 11 años de investigación. Se registró un solo evento de inundación en el canal el 28 de septiembre de 2009, sin embargo, la escorrentía se registró 160 veces en las parcelas. La descarga de escorrentía dependió del tamaño de las parcelas. Las parcelas más grandes produjeron una menor descarga de escorrentía por unidad de área, lo que sugiere una corta distancia y duración del recorrido de escorrentía. Tres eventos de lluvia contribuyeron con el 26% de la descarga total de la escorrentía y cinco lograron el 56% de la escorrentía. Se concluye que la escorrentía generada a escala de la parcela está desconectada del canal principal. Desde un punto de vista espacial, hay una disminución en el coeficiente de escorrentía a lo largo de la pendiente. Desde un punto de vista temporal, la escorrentía se concentra en unos pocos eventos de lluvia. Estos resultados muestran que la escorrentía generada a escala de parcela y pendiente no contribuyen a las inundaciones excepto para eventos de lluvia con más de 100 mm día-1 . La desconexión de la escorrentía y la entrega de sedimentos se confirma por la reducción de la escorrentía a escala de parcela debido al control de la longitud (pendiente) sobre la escorrentía y la entrega de sedimentos., Artemi Cerda thanks the Co-operative Research program from the OECD (Biological Resource Management for Sustainable Agricultural Systems) for its support with the 2016 CRP fellowship (OCDE TAD/CRP JA00088807), POSTFIRE Project (CGL2013-47862-C2-1 and 2-R), and POSTFIRE_CARE Project (CGL2016-75178-C2-2-R) sponsored by the Spanish Ministry of Economy and Competitiveness and AEI/FEDER, UE. This paper was written as a result of the collaboration that was initiated due to the COST ActionES1306: Connecting European Connectivity research and COST CA18135 FIRElinks: Fire in the Earth System. Science and Society. We wish to thank the Department of Geography secretariat team (Nieves Gomez, Nieves Dominguez, and Susana Tomas) for their support for three decades to our research at the Soil Erosion and Degradation Research team (SEDER), with special thanks to the scientific researchers that as visitors from other research teams contributed to the SEDER research. And we also thank the Laboratory for Geomorphology technicians (Leon Navarro) for the key contribution to our research. The collaboration of the Geography and Environmental Sciences students was fruitful and enjoyable.

Delineation of the groundwater’s potential zones is a growing phenomenon worldwide due to the high demand for fresh groundwater. Therefore, the identification of potential groundwater zones is an i...

Piping erosion is one of the water erosions that cause significant changes in the landscape, leading to environmental degradation. To prevent losses resulting from tube growth and enable sustainabl...

Development of zoning and flood-forecasting models is essential for making optimal management decisions before and after floods. The Komijan watershed of Markazi Province, Iran is often affected by floods that have caused great material damage and loss of life. The main objective of this study is to use a new machine-learning method to create three models: best-first decision tree (BFT), a bagging best-first decision tree (BBFT) ensemble and a dagging best-first decision tree (DBFT) ensemble to spatially predict flood probability. Twelve conditioning-factor measures for 272 locations of past floods were used to train and test three models. Receiver operating characteristic (ROC), positive predictive value (PPV), negative predictive value (NPV), sensitivity (SST), specificity (SPF), accuracy (ACC), kappa (K), and root mean square error (RMSE) are applied to compare and validate the models. The results are that all three models performed well in mapping, flood probabilities (AUC > 0.904). The BBFT model was best, however, with an AUC = 0.96. Based on the results of the Relief-F attribute evaluation method, two soil and slope factors were weighted highest among the parameters, indicating that they are the most important flood-conditioning factors. These models may improve identification of zones that are most susceptible to flooding, improving the capacity for risk management and providing more detailed information for managers and decision-makers.

Gully erosion is the most active hydro-geomorphological phenomenon in the continental areas due to the high erosion rates triggered by the gully system. Monitoring and modelling gully development and gully distribution will contribute to understand landforms evolution and risk assessment. The purpose of the current research is to model head-cut gully erosion susceptibility (HCGES) using support vector machine (SVM), random forest (RF) and novel ensemble model of random forest with four Oblique methods (Logistic Regression, Ridge Regression, Partial least squares (PLS) and Support vector machine (SVM)) (hereafter called ensemble ORF) data mining models in Fareghan watershed, Hormozghan province, Iran. For this purpose, 14 variables influencing the gully development, were prepared and 145 head-cut gully erosion locations were identified in the study area. The efficiency of SVM, RF, ensemble ORF were evaluated based on receiver operating characteristic (ROC), the results have shown that all these three models are highly accurate and robust in predicting the head-cut gully erosion susceptibility zones. The results of the models were evaluated based on the area under the receiver operatic characteristic curve (AUC) in the validation stage presented that the efficiency of these models are 0.91, 0.94, and 0.96, respectively. Altitude and distance from the road in all three models were more important than other variables. The findings of this research will contribute to develop gully control strategies and to prevent the gully initiation where gully erosion is more susceptible.

Large damages and losses resulting from floods are widely reported across the globe. Thus, the identification of the flood-prone zones on a flood susceptibility map is very essential. To do so, 13 conditioning factors influencing the flood occurrence in Brisbane river catchment in Australia (i.e., topographic, water-related, geological, and land use factors) were acquired for further processing and modeling. In this study, artificial neural networks (ANN), deep learning neural networks (DLNN), and optimized DLNN using particle swarm optimization (PSO) were exploited to predict and estimate the susceptible areas to the future floods. The significance of the conditioning factors analysis for the region highlighted that altitude, distance from river, sediment transport index (STI), and slope played the most important roles, whereas stream power index (SPI) did not contribute to the hazardous situation. The performance of the models was evaluated against the statistical tests such as sensitivity, specificity, the area under curve (AUC), and true skill statistic (TSS). DLNN and PSO-DLNN models obtained the highest values of sensitivity (0.99) for the training stage to compare with ANN. Moreover, the validations of specificity and TSS for PSO-DLNN recorded the highest values of 0.98 and 0.90, respectively, compared with those obtained by ANN and DLNN. The best accuracies by AUC were evaluated in PSO-DLNN (0.99 in training and 0.98 in testing datasets), followed by DLNN and ANN. Therefore, the optimized PSO-DLNN proved its robustness to compare with other methods.

Different limitations such as the lack of enough hydrometric stations, difficulty in collecting hydrometric data with costly data collection are caused to create hydrologic models for estimating the flood hydrograph. Based on the easy and more access to rainfall statistics, preparing the hydrologic model based on rainfall characteristics and data seems to be the very applicable and logical method. Data-driven models have increasingly been used to describe the behavior of hydrological systems, which can be used to complement or even replace physical-based models. In this study, the efficiency of two data mining models including artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) was evaluated in order to model flood hydrograph characteristics based on rainfall components in Kasilian Watershed, northern Iran. For this purpose, fifteen characteristics of rainfall (hyetograph) and eight characteristics of flood hydrograph were respectively considered independent and dependent variables for 60 rainfall-runoff events from 1975 to 2009. ANN with two functions of hyperbolic tangent and sigmoid and ANFIS with grid partitioning and subtractive clustering were used to estimate flood hydrograph. Variance inflation factor (VIF) (for selecting variables that are minimal multicollinearity) were used to select the input variables. ANFIS model with the grid partitioning method performs better than the ANFIS model with the subtractive clustering method. ANFIS with Nash-Sutcliff efficiency (NSE) of 0.87, root mean squared error (RMSE) of 0.38 m3/s, and deviation of peak time of observed and estimated hydrographs (DPOT) of 4.33 h was found to be superior to ANN with NSE of 0.40, RMSE of 0.88 m3/s, and DPOT of 1.14 h accurately and efficiently for modeling flood hydrograph. Therefore, ANFIS model is proposed for modeling the flood hydrograph based on rainfall characteristics.

This study predicts forest fire susceptibility in Chaloos Rood watershed in Iran using three machine learning (ML) models—multivariate adaptive regression splines (MARS), support vector machine (SVM), and boosted regression tree (BRT). The study utilizes 14 set of fire predictors derived from vegetation indices, climatic variables, environmental factors, and topographical features. To assess the suitability of the models and estimating the variance and bias of estimation, the training dataset obtained from the Natural Resources Directorate of Mazandaran province was subjected to resampling using cross validation (CV), bootstrap, and optimism bootstrap techniques. Using variance inflation factor (VIF), weight indicating the strength of the spatial relationship of the predictors to fire occurrence was assigned to each contributing variable. Subsequently, the models were trained and validated using the receiver operating characteristics (ROC) area under the curve (AUC) curve. Results of the model validation based on the resampling techniques (non, 5- and 10-fold CV, bootstrap and optimism bootstrap) produced AUC values of 0.78, 0.88, 0.90, 0.86 and 0.83 for the MARS model; 0.82, 0.82, 0.89, 0.87, 0.84 for the SVM and 0.87, 0.90, 0.90, 0.90, 0.91 for the BRT model. Across the individual model, the 10-fold CV performed best in MARS and SVM with AUC values of 0.90 and 0.89. Overall, the BRT outperformed the other models in all ramification with highest AUC value of 0.91 using optimism bootstrap resampling algorithm. Generally, the resampling process enhanced the prediction performance of all the models.

The estimation and mapping of forest stand characteristics are vital because this information is necessary for sustainable forest management. The present study considers the use of a Bayesian additive regression trees (BART) algorithm as a non-parametric classifier using Sentinel-2A data and topographic variables to estimate the forest stand characteristics, namely the basal area (m2/ha), stem volume (m3/ha), and stem density (number/ha). These results were compared with those of three other popular machine learning (ML) algorithms, such as generalised linear model (GLM), K-nearest neighbours (KNN), and support vector machine (SVM). A feature selection was done on 28 variables including the multi-spectral bands on Sentinel-2 satellite, related vegetation indices, and ancillary data (elevation, slope, and topographic solar-radiation index derived from digital elevation model (DEM)) and then the most insignificant variables were removed from the datasets by recursive feature elimination (RFE). The study area was a mountainous forest with high biodiversity and an elevation gradient from 26 to 1636 m. An inventory dataset of 1200 sample plots was provided for training and testing the algorithms, and the predictors were fed into the ML models to compute and predict the forest stand characteristics. The accuracies and certainties of the ML models were assessed by their root mean square error (RMSE), mean absolute error (MAE), and R-squared (R2) values. The results demonstrated that BART generated the best basal area and stem volume predictions, followed by GLM, SVM, and KNN. The best RMSE values for both basal area (8.12 m2/ha) and stem volume (29.28 m3/ha) estimation were obtained by BART. Thus, the ability of the BART model for forestry application was established. On the other hand, KNN exhibited the highest RMSE values for all stand variable predictions, thereby exhibiting the least accuracy for this specific application. Moreover, the effectiveness of the narrow Sentinel-2 bands around the red edge and elevation was highlighted for predicting the forest stand characteristics. Therefore, we concluded that the combination of the Sentinel-2 products and topographic variables derived from the PALSAR data used in this study improved the estimation of the forest attributes in temperate forests.

The objective of this study is to assess the gully head-cut erosion susceptibility and identify gully erosion prone areas in the Meimand watershed, Iran. In recent years, this study area has been greatly influenced by several head-cut gullies due to unusual climatic factors and human induced activity. The present study is therefore intended to address this issue by developing head-cut gully erosion prediction maps using boosting ensemble machine learning algorithms, namely Boosted Tree (BT), Boosted Generalized Linear Models (BGLM), Boosted Regression Tree (BRT), Extreme Gradient Boosting (XGB), and Deep Boost (DB). Initially, we produced a gully erosion inventory map using a variety of resources, including published reports, Google Earth images, and field records of the Global Positioning System (GPS). Subsequently, we distributed this information randomly and choose 70% (102) of the test gullies and the remaining 30% (43) for validation. The methodology was designed using morphometric and thematic determinants, including 14 head-cut gully erosion conditioning features. We have also investigated the following: (a) Multi-collinearity analysis to determine the linearity of the independent variables, (b) Predictive capability of piping models using train and test dataset and (c) Variables importance affecting head-cut gully erosion. The study reveals that altitude, land use, distances from road and soil characteristics influenced the method with the greatest impact on head-cut gully erosion susceptibility. We presented five head-cut gully erosion susceptibility maps and investigated their predictive accuracy through area under curve (AUC). The AUC test reveals that the DB machine learning method demonstrated significantly higher accuracy (AUC = 0.95) than the BT (AUC = 0.93), BGLM (AUC = 0.91), BRT (AUC = 0.94) and XGB (AUC = 0.92) approaches. The predicted head-cut gully erosion susceptibility maps can be used by policy makers and local authorities for soil conservation and to prevent threats to human activities.

In the present study, gully erosion susceptibility was evaluated for the area of the Robat Turk Watershed in Iran. The assessment of gully erosion susceptibility was performed using four state-of-the-art data mining techniques: random forest (RF), credal decision trees (CDTree), kernel logistic regression (KLR), and best-first decision tree (BFTree). To the best of our knowledge, the KLR and CDTree algorithms have been rarely applied to gully erosion modeling. In the first step, from the 242 gully erosion locations that were identified, 70% (170 gullies) were selected as the training dataset, and the other 30% (72 gullies) were considered for the result validation process. In the next step, twelve gully erosion conditioning factors, including topographic, geomorphological, environmental, and hydrologic factors, were selected to estimate gully erosion susceptibility. The area under the ROC curve (AUC) was used to estimate the performance of the models. The results revealed that the RF model had the best performance (AUC = 0.893), followed by the KLR (AUC = 0.825), the CDTree (AUC = 0.808), and the BFTree (AUC = 0.789) models. Overall, the RF model performed significantly better than the others, which may support the application of this method to a transferable susceptibility model in other areas. Therefore, we suggest using the RF, KLR, and CDT models for gully erosion susceptibility mapping in other prone areas to assess their reproducibility.

Soil erosion determines landforms, soil formation and distribution, soil fertility, and land degradation processes. In arid and semiarid ecosystems, soil erosion is a key process to understand, foresee, and prevent desertification. Addressing soil erosion throughout watersheds scales requires basic information to develop soil erosion control strategies and to reduce land degradation. To assess and remediate the non-sustainable soil erosion rates, restoration programs benefit from the knowledge of the spatial distribution of the soil losses to develop maps of soil erosion. This study presents Support Vector Machine (SVM), Random Forest (RF), and adaptive boosting (AdaBoost) data mining models to map soil erosion susceptibility in Kozetopraghi watershed, Iran. A soil erosion inventory map was prepared from field rainfall simulation experiments on 174 randomly selected points along the Kozetopraghi watershed. In previous studies, this map has been prepared using indirect methods such as the Universal Soil Loss Equation to assess soil erosion. Direct field measurements for mapping soil erosion susceptibility have so far not been carried out in our study site in the past. The soil erosion rate data generated by simulated rainfall in 1 m2 plots at rainfall rate of 40 mmh&minus, 1 was used to develop the soil erosion map. Of the available data, 70% and 30% were randomly classified to calibrate and validate the models, respectively. As a result, the RF model with the highest area under the curve (AUC) value in a receiver operating characteristics (ROC) curve (0.91), and the lowest mean square error (MSE) value (0.09), has the most concordance and spatial differentiation. Sensitivity analysis by Jackknife and IncNodePurity methods indicates that the slope angle is the most important factor within the soil erosion susceptibility map. The RF susceptibility map showed that the areas located in the center and near the watershed outlet have the most susceptibility to soil erosion. This information can be used to support the development of sustainable restoration plans with more accuracy. Our methodology has been evaluated and can be also applied in other regions.