Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution

216

Flood susceptibility mapping and assessment is an important element of flood prevention and mitigation strategies because it identifies the most vulnerable areas based on physical characteristics that determine the propensity for flooding. This study aims to define the flood susceptibility zones for the territory of Slovakia using a multi-criteria approach, particularly the analytical hierarchy process (AHP) technique, and geographic information systems (GIS). Seven flood conditioning factors were chosen: hydrography—distance from rivers, river network density; hydrology—flow accumulation; morphometry—elevation, slope; and permeability—curve numbers, lithology. All factors were defined as raster datasets with the resolution of 50 x 50 m. The AHP technique was used to calculate the factor weights. The relative importance of the selected factors prioritized slope degree as the most important factor followed by river network density, distance from rivers, flow accumulation, elevation, curve number, and lithology. It was found that 33.1% of the territory of Slovakia is characterized by very high to high flood susceptibility. The flood susceptibility map was validated against 1513 flood historical points showing very good agreement between the computed susceptibility zones and historical flood events of which 70.9% were coincident with high and very high susceptibility levels, thus confirming the effectiveness of the methodology adopted.

Section: Weight Linear Combination Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Flood Susceptibility Mapping on a National Scale in Slovakia Using the Analytical Hierarchy Process

Vojtek

Vojteková

2019

216

“…These methods are based on effective and objective mathematical algorithms for analysis and prediction [18][19][20][21]. Some popular ML methods used for flood susceptibility assessment are Artificial Neural Networks (ANN) [22,23], Logistic Model Trees (LMT) [24], Support Vector Machines (SVM), Logistic Regression (LR) [25,26], Adaptive Neuro-Fuzzy Inference Systems (ANFIS) [27], and Neural-Fuzzy (NF) approach [28,29]. So far, there is no existing model that can be applied in all regions for flood susceptibility assessment and mapping accurately [30].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping

Pham

Phong

Nguyen

et al. 2020

109

Risk of flash floods is currently an important problem in many parts of Vietnam. In this study, we used four machine-learning methods, namely Kernel Logistic Regression (KLR), Radial Basis Function Classifier (RBFC), Multinomial Naïve Bayes (NBM), and Logistic Model Tree (LMT) to generate flash flood susceptibility maps at the minor part of Nghe An province of the Center region (Vietnam) where recurrent flood problems are being experienced. Performance of these four methods was evaluated to select the best method for flash flood susceptibility mapping. In the model studies, ten flash flood conditioning factors, namely soil, slope, curvature, river density, flow direction, distance from rivers, elevation, aspect, land use, and geology, were chosen based on topography and geo-environmental conditions of the site. For the validation of models, the area under Receiver Operating Characteristic (ROC), Area Under Curve (AUC), and various statistical indices were used. The results indicated that performance of all the models is good for generating flash flood susceptibility maps (AUC = 0.983–0.988). However, performance of LMT model is the best among the four methods (LMT: AUC = 0.988; KLR: AUC = 0.985; RBFC: AUC = 0.984; and NBM: AUC = 0.983). The present study would be useful for the construction of accurate flash flood susceptibility maps with the objectives of identifying flood-susceptible areas/zones for proper flash flood risk management.

“…Recently, machine-learning (ML) techniques have become popular for the spatial prediction of natural hazards like wildfires [22], sinkholes [23], groundwater depletion and flooding [24][25][26][27][28][29][30][31][32][33][34][35][36][37][38], droughts [39], earthquakes [40], land subsidence [41], and landslides [42][43][44][45][46][47][48]. ML is a type of artificial intelligence (AI) that uses computer algorithms to analyze and forecast information by learning from training data.…”

Section: Introductionmentioning

confidence: 99%

Gully Head-Cut Distribution Modeling Using Machine Learning Methods—A Case Study of N.W. Iran

Arabameri

Chen

Blaschke

et al. 2019

Self Cite

To more effectively prevent and manage the scourge of gully erosion in arid and semi-arid regions, we present a novel-ensemble intelligence approach-bagging-based alternating decision-tree classifier (bagging-ADTree)-and use it to model a landscape's susceptibility to gully erosion based on 18 gully-erosion conditioning factors. The model's goodness-of-fit and prediction performance are compared to three other machine learning algorithms (single alternating decision tree, rotational-forest-based alternating decision tree (RF-ADTree), and benchmark logistic regression). To achieve this, a gully-erosion inventory was created for the study area, the Chah Mousi watershed, Iran by combining archival records containing reports of gully erosion, remotely sensed data from Google Earth, and geolocated sites of gully head-cuts gathered in a field survey. A total of 119 gully head-cuts were identified and mapped. To train the models' analysis and prediction capabilities, 83 head-cuts (70% of the total) and the corresponding measures of the conditioning factors were input into each model. The results from the models were validated using the data pertaining to the remaining 36 gully locations (30%). Next, the frequency ratio is used to identify which conditioning-factor classes have the strongest correlation with gully erosion. Using random-forest modeling, the relative importance of each of the conditioning factors was determined. Based on the random-forest results, the top eight factors in this study area are distance-to-road, drainage density, distance-to-stream, LU/LC, annual precipitation, topographic wetness index, NDVI, and elevation. Finally, based on goodness-of-fit and AUROC of the success rate curve (SRC) and prediction rate curve (PRC), the results indicate that the bagging-ADTree ensemble model had the best performance, with SRC (0.964) and PRC (0.978). RF-ADTree (SRC = 0.952 and PRC = 0.971), ADTree (SRC = 0.926 and PRC = 0.965), and LR (SRC = 0.867 and PRC = 0.870) were the subsequent best performers. The results also indicate that bagging and RF, as meta-classifiers, improved the performance of the ADTree model as a base classifier. The bagging-ADTree model's results indicate that 24.28% of the study area is classified as having high and very high susceptibility to gully erosion. The new ensemble model accurately Water 2020, 12, 16 2 of 26 identified the areas that are susceptible to gully erosion based on the past patterns of formation, but it also provides highly accurate predictions of future gully development. The novel ensemble method introduced in this research is recommended for use to evaluate the patterns of gullying in arid and semi-arid environments and can effectively identify the most salient conditioning factors that promote the development and expansion of gullies in erosion-susceptible environments.