Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling

Gully erosion is identified as an important sediment source in a range of environments and plays a conclusive role in redistribution of eroded soils on a slope. Hence, addressing spatial occurrence pattern of this phenomenon is very important. Different ensemble models and their single counterparts, mostly data mining methods, have been used for gully erosion susceptibility mapping; however, their calibration and validation procedures need to be thoroughly addressed. The current study presents a series of individual and ensemble dataminingmethods including artificial neural network (ANN), support vector machine (SVM), maximum entropy (ME), ANN-SVM, ANN-ME, and SVM-ME to map gully erosion susceptibility in Aghemam watershed, Iran. To this aim, a gully inventory map along with sixteen gully conditioning factors was used. A 70:30% randomly partitioned sets were used to assess goodness-of-fit and prediction power of the models. The robustness, as the stability ofmodels' performance in response to changes in the dataset, was assessed through three training/test replicates. As a result, conducted preliminary statistical tests showed that ANN has the highest concordance and spatial differentiation with a chi-square value of 36,656 at 95% confidence level,while theME appeared to have the lowest concordance (1772). The ME model showed an impractical result where 45% of the study area was introduced as highly susceptible to gullying, in contrast, ANN-SVMindicated a practical resultwith focusing only on 34% of the study area. Through all three replicates, the ANN-SVM ensemble showed the highest goodness-of-fit and predictive power with a respective values of 0.897 (area under the success rate curve) and 0.879 (area under the prediction rate curve), on average, and correspondingly the highest robustness. This attests the important role of ensemble modeling in congruently building accurate and generalized models which emphasizes the necessity to examine different models integrations. The result of this study can prepare an outline for further biophysical designs on gullies scattered in the study area

Landslide susceptibility assessment using three bivariate models considering the new topo-hydrological factor: HAND

The main objective of this study is to assess the relative contribution of the state-of-the-art topo-hydrological factor, known as height above the nearest drainage (HAND), to landslide susceptibility modellling using three novel statistical models: weights-of-evidence (WofE), index of entropy and certainty factor. In total, 12 landslide conditioning factors that affect the landslide incidence were used as input to the models in the Ziarat Watershed, Golestan Province, Iran. Landslide inventory was randomly divided into a ratio of 70:30 for training and validating the results of the models. The optimum combination of conditioning factors was identified using the principal components analysis (PCA) method. The results demonstrated that HAND is the defining factor among hydrological and topographical factors in the study area. Additionally, the WofE model had the highest prediction capability (AUPRC = 74.31%). Therefore, HAND was found to be a promising factor for landslide susceptibility mapping

Investigating the effects of different landslide positioning techniques, landslide partitioning approaches, and presence-absence balances on landslide susceptibility mapping

The Ziarat Watershed, located in the south of the Golestan Province, Iran, has witnessed several destructive landslide episodes, prompting a number of researchers to aspire to improve landslide susceptibility modeling (LSM) techniques. We constructed three scenarios focusing on landslide positioning techniques (pixel-based, centroid, crown, and toe), training/test sampling strategies (Mahalanobis distance (MD), and random sampling (RS)), with alternative landslide/non-landslide data balances (1:1, 1:2, and 1:3). The data mining boosted regression trees (BRT) model was used for the landslide susceptibility modeling, using landslide data and 13 landslide controlling factors for the Ziarat Watershed. The performance of the scenarios was assessed using the areas under the success and prediction rate curves (AUSRC and AUPRC). A combination of pixel-based–MD–1:2 showed the highest learning capability and goodness-of-fit with an AUSRC value of 0.87, and the highest predictive power and generalization capacity with an AUPRC value of 0.79. Conversely, centroid-based–1:3–RS, crown-based–1:3–RS, and toe-based–1:3–RS performed less well. Comparatively, the pixel-based, MD, and 1:2 data balance scenarios surpassed their counterparts and outperformed the other models. The results indicated a high spatial differentiation with a significant chi-square value of 4549.46 at 95% confidence level. Moreover, 15.21% of the study area, containing almost 50% of the landslides, was found to have a high susceptibility to landslides. According to the premier scenario (pixel-based–MD–1:2), lithological formation, distance from roads, and NDVI, with respective contributions of 31.4%, 12.9%, and 12%, are the main spatial controlling factors leading to landslide occurrences in the study area

Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms

Flood is one of the most destructive natural disasters which cause great financial and life losses per year. Therefore, producing susceptibility maps for flood management are necessary in order to reduce its harmful effects. The aim of the present study is to map flood hazard over the Jahrom Township in Fars Province using a combination of adaptive neuro-fuzzy inference systems (ANFIS) with different metaheuristics algorithms such as ant colony optimization (ACO), genetic algorithm (GA), and particle swarm optimization (PSO) and comparing their accuracy. A total number of 53 flood locations areas were identified, 35 locations of which were randomly selected in order to model flood susceptibility and the remaining 16 locations were used to validate the models. Learning vector quantization (LVQ), as one of the supervised neural network methods, was employed in order to estimate factors' importance. Nine flood conditioning factors namely: slope degree, plan curvature, altitude, topographic wetness index (TWI), stream power index (SPI), distance from river, land use/land cover, rainfall, and lithology were selected and the corresponding maps were prepared in ArcGIS. The frequency ratio (FR) model was used to assign weights to each class within particular controlling factor, then the weights was transferred into MATLAB software for further analyses and to combine with metaheuristic models. The ANFIS-PSO was found to be the most practical model in term of producing the highly focused flood susceptibility map with lesser spatial distribution related to highly susceptible classes. The chi-square result attests the same, where the ANFIS-PSO had the highest spatial differentiation within flood susceptibility classes over the study area. The area under the curve (AUC) obtained from ROC curve indicated the accuracy of 91.4%, 91.8%, 92.6% and 94.5% for the respective models of FR, ANFIS-ACO, ANFIS-GA, and ANFIS-PSO ensembles. So, the ensemble of ANFIS-PSO was introduced as the premier model in the study area. Furthermore, LVQ results revealed that slope degree, rainfall, and altitude were the most effective factors. As regards the premier model, a total area of 44.74% was recognized as highly susceptible to flooding. The results of this study can be used as a platform for better land use planning in order to manage the highly susceptible zones to flooding and reduce the anticipated losses.</p

Urban Flood Hazard Modeling Using Self-Organizing Map Neural Network

Floods are the most common natural disaster globally and lead to severe damage, especially in urban environments. This study evaluated the efficiency of a self-organizing map neural network (SOMN) algorithm for urban flood hazard mapping in the case of Amol city, Iran. First, a flood inventory database was prepared using field survey data covering 118 flooded points. A 70:30 data ratio was applied for training and validation purposes. Six factors (elevation, slope percent, distance from river, distance from channel, curve number, and precipitation) were selected as predictor variables. After building the model, the odds ratio skill score (ORSS), efficiency (E), true skill statistic (TSS), and the area under the receiver operating characteristic curve (AUC-ROC) were used as evaluation metrics to scrutinize the goodness-of-fit and predictive performance of the model. The results indicated that the SOMN model performed excellently in modeling flood hazard in both the training (AUC = 0.946, E = 0.849, TSS = 0.716, ORSS = 0.954) and validation (AUC = 0.924, E = 0.857, TSS = 0.714, ORSS = 0.945) steps. The model identified around 23% of the Amol city area as being in high or very high flood risk classes that need to be carefully managed. Overall, the results demonstrate that the SOMN model can be used for flood hazard mapping in urban environments and can provide valuable insights about flood risk management

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

Urban flood hazard modeling using self-organizing map neural network

Abstract Floods are the most common natural disaster globally and lead to severe damage, especially in urban environments. This study evaluated the efficiency of a self-organizing map neural network (SOMN) algorithm for urban flood hazard mapping in the case of Amol city, Iran. First, a flood inventory database was prepared using field survey data covering 118 flooded points. A 70:30 data ratio was applied for training and validation purposes. Six factors (elevation, slope percent, distance from river, distance from channel, curve number, and precipitation) were selected as predictor variables. After building the model, the odds ratio skill score (ORSS), efficiency (E), true skill statistic (TSS), and the area under the receiver operating characteristic curve (AUC-ROC) were used as evaluation metrics to scrutinize the goodness-of-fit and predictive performance of the model. The results indicated that the SOMN model performed excellently in modeling flood hazard in both the training (AUC = 0.946, E = 0.849, TSS = 0.716, ORSS = 0.954) and validation (AUC = 0.924, E = 0.857, TSS = 0.714, ORSS = 0.945) steps. The model identified around 23% of the Amol city area as being in high or very high flood risk classes that need to be carefully managed. Overall, the results demonstrate that the SOMN model can be used for flood hazard mapping in urban environments and can provide valuable insights about flood risk management

Doing more with less: A comparative assessment between morphometric indices and machine learning models for automated gully pattern extraction (A case study: Dashtiari region, Sistan and Baluchestan Province)

Deep gullies in the Dashtiari Region prompted us to couple different morphometric indices obtained from a UAV-derived DEM to automatically extract gully signatures. The extraction of gully signatures is commonly undertaken via pattern recognition techniques, whose recent advancements seem to require more data and rather cumbersome modeling processes, making it even more difficult for those who are not well-versed in such contexts. Among these methods, object-based image analysis (OBIA), machine learning, and deep learning techniques are the most common. Conversely, here we took advantage of simple morphometric indices and their combinations for gully extraction, including valley depth (VD), topographic position index (TPI), positive openness (PO), red relief image map (RRIM), elevation, slope degree, and the coupled PO-DEM. Furthermore, we compared the automatically derived gully patterns to the manually extracted ones (treated as the ground truth), and their spatial autocorrelation was investigated. Additionally, the application of the classification tree (CT) as a powerful machine learning model was comparatively assessed for morphometric indices. The performance of the adopted pattern extraction techniques was estimated using four different metrics: precision index, true skill statistics (TSS), Cohen's kappa, and Matthews correlation coefficient (MCC). The results revealed that the single use of PO, TPI, and RRIM indices failed to reliably capture the gullies’ pattern, leading to partial success. Notably, combinations of indices showed that the coupled PO-DEM could successfully classify the gully presence locations from the absences and outperform the CT model in terms of both goodness-of-fit and generalization capacity (prediction power), considering all four-performance metrics. Hence, comparing the amount of time spent for manual delineation of gullies, the application of simple morphometric indices, and machine learning models is beyond comparison

PMT: new analytical framework for automated evaluation of geo-environmental modelling approaches

Geospatial computation, data transformation to a relevant statistical software, and step-wise quantitative performance assessment can be cumbersome, especially when considering that the entire modelling procedure is repeatedly interrupted by several input/output steps, and the self-consistency and self-adaptive response to the modelled data and the features therein are lost while handling the data from different kinds of working environments. To date, an automated and a comprehensive validation system,which includes both the cutoff-dependent and –independent evaluation criteria for spatial modelling approaches, has not yet been developed for GIS based methodologies. This study, for the first time, aims to fill this gap by designing and evaluating a user-friendly model validation approach, denoted as Performance Measure Tool (PMT), and developed using freely available Python programming platform. The considered cutoff-dependent criteria include receiver operating characteristic (ROC) curve, success-rate curve (SRC) and prediction-rate curve (PRC),whereas cutoff-independent consist of twenty-one performance metrics such as efficiency, misclassification rate, false omission rate, F-score, threat score, odds ratio, etc. To test the robustness of the developed tool, we applied it to a wide variety of geoenvironmental modelling approaches, especially in different countries, data, and spatial contexts around the world including, the USA (soil digital modelling), Australia (drought risk evaluation), Vietnam (landslide studies), Iran (flood studies), and Italy (gully erosion studies). The newly proposed PMT is demonstrated to be capable of analyzing a wide range of environmental modelling results, and provides inclusive performance evaluation metrics in a relatively short time and user-convenient framework whilst each of the metrics is used to address a particular aspect of the predictive model. Drawing on the inferences, a scenario-based protocol for model performance evaluation is suggeste

Development of novel hybridized models for urban flood susceptibility mapping

Floods in urban environments often result in loss of life and destruction of property, with many negative socio-economic effects. However, the application of most flood prediction models still remains challenging due to data scarcity. This creates a need to develop novel hybridized models based on historical urban flood events, using, e.g., metaheuristic optimization algorithms and wavelet analysis. The hybridized models examined in this study (Wavelet-SVR-Bat and Wavelet-SVR-GWO), designed as intelligent systems, consist of a support vector regression (SVR), integrated with a combination of wavelet transform and metaheuristic optimization algorithms, including the grey wolf optimizer (GWO), and the bat optimizer (Bat). The efficiency of the novel hybridized and standalone SVR models for spatial modeling of urban flood inundation was evaluated using different cutoff-dependent and cutoff-independent evaluation criteria, including area under the receiver operating characteristic curve (AUC), Accuracy (A), Matthews Correlation Coefficient (MCC), Misclassification Rate (MR), and F-score. The results demonstrated that both hybridized models had very high performance (Wavelet-SVR-GWO: AUC = 0.981, A = 0.92, MCC = 0.86, MR = 0.07; Wavelet-SVR-Bat: AUC = 0.972, A = 0.88, MCC = 0.76, MR = 0.11) compared with the standalone SVR (AUC = 0.917, A = 0.85, MCC = 0.7, MR = 0.15). Therefore, these hybridized models are a promising, cost-effective method for spatial modeling of urban flood susceptibility and for providing in-depth insights to guide flood preparedness and emergency response services