scholarly journals Urban flood risk mapping using data-driven geospatial techniques for a flood-prone case area in Iran

2019 ◽  
Vol 51 (1) ◽  
pp. 127-142 ◽  
Author(s):  
Hamid Darabi ◽  
Ali Torabi Haghighi ◽  
Mohamad Ayob Mohamadi ◽  
Mostafa Rashidpour ◽  
Alan D. Ziegler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Population Density ◽  
Flood Risk ◽  
Flood Hazard ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Runoff Generation ◽  
Boosted Regression Tree ◽  
Building Density

Abstract In an effort to improve tools for effective flood risk assessment, we applied machine learning algorithms to predict flood-prone areas in Amol city (Iran), a site with recent floods (2017–2018). An ensemble approach was then implemented to predict hazard probabilities using the best machine learning algorithms (boosted regression tree, multivariate adaptive regression spline, generalized linear model, and generalized additive model) based on a receiver operator characteristic-area under the curve (ROC-AUC) assessment. The algorithms were all trained and tested on 92 randomly selected points, information from a flood inundation survey, and geospatial predictor variables (precipitation, land use, elevation, slope percent, curve number, distance to river, distance to channel, and depth to groundwater). The ensemble model had 0.925 and 0.892 accuracy for training and testing data, respectively. We then created a vulnerability map from data on building density, building age, population density, and socio-economic conditions and assessed risk as a product of hazard and vulnerability. The results indicated that distance to channel, land use, and runoff generation were the most important factors associated with flood hazard, while population density and building density were the most important factors determining vulnerability. Areas of highest and lowest flood risks were identified, leading to recommendations on where to implement flood risk reduction measures to guide flood governance in Amol city.

Machine-learning algorithms for land use dynamics in Lake Haramaya Watershed, Ethiopia

2021 ◽  
Author(s):  
Gezahegn Weldu Woldemariam ◽  
Degefie Tibebe ◽  
Tesfamariam Engida Mengesha ◽  
Tadele Bedo Gelete
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Land Use Dynamics

scholarly journals Flash Flood Susceptibility Modeling Using New Approaches of Hybrid and Ensemble Tree-Based Machine Learning Algorithms

2020 ◽  
Vol 12 (21) ◽  
pp. 3568
Author(s):  
Shahab S. Band ◽  
Saeid Janizadeh ◽  
Subodh Chandra Pal ◽  
Asish Saha ◽  
Rabin Chakrabortty ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Flash Flood ◽  
Flood Hazard ◽  
Machine Learning Algorithms ◽  
Mitigation Strategies ◽  
Boosted Regression Tree ◽  
Flash Flooding ◽  
Flood Susceptibility ◽  
Susceptibility Modeling

Flash flooding is considered one of the most dynamic natural disasters for which measures need to be taken to minimize economic damages, adverse effects, and consequences by mapping flood susceptibility. Identifying areas prone to flash flooding is a crucial step in flash flood hazard management. In the present study, the Kalvan watershed in Markazi Province, Iran, was chosen to evaluate the flash flood susceptibility modeling. Thus, to detect flash flood-prone zones in this study area, five machine learning (ML) algorithms were tested. These included boosted regression tree (BRT), random forest (RF), parallel random forest (PRF), regularized random forest (RRF), and extremely randomized trees (ERT). Fifteen climatic and geo-environmental variables were used as inputs of the flash flood susceptibility models. The results showed that ERT was the most optimal model with an area under curve (AUC) value of 0.82. The rest of the models’ AUC values, i.e., RRF, PRF, RF, and BRT, were 0.80, 0.79, 0.78, and 0.75, respectively. In the ERT model, the areal coverage for very high to moderate flash flood susceptible area was 582.56 km2 (28.33%), and the rest of the portion was associated with very low to low susceptibility zones. It is concluded that topographical and hydrological parameters, e.g., altitude, slope, rainfall, and the river’s distance, were the most effective parameters. The results of this study will play a vital role in the planning and implementation of flood mitigation strategies in the region.

scholarly journals Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review

2020 ◽  
Vol 12 (7) ◽  
pp. 1135 ◽  
Author(s):  
Swapan Talukdar ◽  
Pankaj Singha ◽  
Susanta Mahato ◽  
Shahfahad ◽  
Swades Pal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Land Cover ◽  
Learning Algorithms ◽  
Kappa Coefficient ◽  
Machine Learning Algorithms ◽  
The Other ◽  
Land Use Land Cover ◽  
Accuracy Level ◽  
Parametric Classifier

Rapid and uncontrolled population growth along with economic and industrial development, especially in developing countries during the late twentieth and early twenty-first centuries, have increased the rate of land-use/land-cover (LULC) change many times. Since quantitative assessment of changes in LULC is one of the most efficient means to understand and manage the land transformation, there is a need to examine the accuracy of different algorithms for LULC mapping in order to identify the best classifier for further applications of earth observations. In this article, six machine-learning algorithms, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), fuzzy adaptive resonance theory-supervised predictive mapping (Fuzzy ARTMAP), spectral angle mapper (SAM) and Mahalanobis distance (MD) were examined. Accuracy assessment was performed by using Kappa coefficient, receiver operational curve (RoC), index-based validation and root mean square error (RMSE). Results of Kappa coefficient show that all the classifiers have a similar accuracy level with minor variation, but the RF algorithm has the highest accuracy of 0.89 and the MD algorithm (parametric classifier) has the least accuracy of 0.82. In addition, the index-based LULC and visual cross-validation show that the RF algorithm (correlations between RF and normalised differentiation water index, normalised differentiation vegetation index and normalised differentiation built-up index are 0.96, 0.99 and 1, respectively, at 0.05 level of significance) has the highest accuracy level in comparison to the other classifiers adopted. Findings from the literature also proved that ANN and RF algorithms are the best LULC classifiers, although a non-parametric classifier like SAM (Kappa coefficient 0.84; area under curve (AUC) 0.85) has a better and consistent accuracy level than the other machine-learning algorithms. Finally, this review concludes that the RF algorithm is the best machine-learning LULC classifier, among the six examined algorithms although it is necessary to further test the RF algorithm in different morphoclimatic conditions in the future.

Assessment of machine learning algorithms in land use classification

2022 ◽  
pp. 97-104
Author(s):  
Hassan Khavarian Nehzak ◽  
Maryam Aghaei ◽  
Raoof Mostafazadeh ◽  
Hamidreza Rabiei-Dastjerdi
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Land Use Classification
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