Multi-hazard exposure mapping using machine learning techniques: A case study from Iran
Rahmati, Omid ; Yousefi, Saleh ; Kalantari, Zahra ; Uuemaa, Evelyn ; Teimurian, Teimur ; Keesstra, Saskia ; Pham, Tien Dat ; Bui, Dieu Tien - \ 2019
Remote Sensing 11 (2019)16. - ISSN 2072-4292
Artificial intelligence - Asara watershed - Hazard - Natural disasters - Sentinel-1
Mountainous areas are highly prone to a variety of nature-triggered disasters, which often cause disabling harm, death, destruction, and damage. In this work, an attempt was made to develop an accurate multi-hazard exposure map for a mountainous area (Asara watershed, Iran), based on state-of-the art machine learning techniques. Hazard modeling for avalanches, rockfalls, and floods was performed using three state-of-the-art models-support vector machine (SVM), boosted regression tree (BRT), and generalized additive model (GAM). Topo-hydrological and geo-environmental factors were used as predictors in the models. A flood dataset (n = 133 flood events) was applied, which had been prepared using Sentinel-1-based processing and ground-based information. In addition, snow avalanche (n = 58) and rockfall (n = 101) data sets were used. The data set of each hazard type was randomly divided to two groups: Training (70%) and validation (30%). Model performance was evaluated by the true skill score (TSS) and the area under receiver operating characteristic curve (AUC) criteria. Using an exposure map, the multi-hazard map was converted into a multi-hazard exposure map. According to both validation methods, the SVM model showed the highest accuracy for avalanches (AUC = 92.4%, TSS = 0.72) and rockfalls (AUC = 93.7%, TSS = 0.81), while BRT demonstrated the best performance for flood hazards (AUC = 94.2%, TSS = 0.80). Overall, multi-hazard exposure modeling revealed that valleys and areas close to the Chalous Road, one of the most important roads in Iran, were associated with high and very high levels of risk. The proposed multi-hazard exposure framework can be helpful in supporting decision making on mountain social-ecological systems facing multiple hazards.
Land subsidence hazard modeling : Machine learning to identify predictors and the role of human activities
Rahmati, Omid ; Golkarian, Ali ; Biggs, Trent ; Keesstra, Saskia ; Mohammadi, Farnoush ; Daliakopoulos, Ioannis N. - \ 2019
Journal of Environmental Management 236 (2019). - ISSN 0301-4797 - p. 466 - 480.
Groundwater overexploitation - Iran - Land use change - Subsidence - Sustainability
Land subsidence caused by land use change and overexploitation of groundwater is an example of mismanagement of natural resources, yet subsidence remains difficult to predict. In this study, the relationship between land subsidence features and geo-environmental factors is investigated by comparing two machine learning algorithms (MLA): maximum entropy (MaxEnt) and genetic algorithm rule-set production (GARP) algorithms in the Kashmar Region, Iran. Land subsidence features (N = 79) were mapped using field surveys. Land use, lithology, the distance from traditional groundwater abstraction systems (Qanats), from afforestation projects, from neighboring faults, and the drawdown of groundwater level (DGL) (1991–2016) were used as predictive variables. Jackknife resampling showed that DGL, distance from afforestation projects, and distance from Qanat systems are major factors influencing land subsidence, with geology and faults being less important. The GARP algorithm outperformed the MaxEnt algorithm for all performance metrics. The performance of both models, as measured by the area under the receiver-operator characteristic curve (AUROC), decreased from 88.9–94.4% to 82.5–90.3% when DGL was excluded as a predictor, though the performance of GARP was still good to excellent even without DGL. MLAs produced maps of subsidence risk with acceptable accuracy, both with and without data on groundwater drawdown, suggesting that MLAs can usefully inform efforts to manage subsidence in data-scarce regions, though the highest accuracy requires data on changes in groundwater level.
Evaluation of watershed health using Fuzzy-ANP approach considering geo-environmental and topo-hydrological criteria
Alilou, Hossein ; Rahmati, Omid ; Singh, Vijay P. ; Choubin, Bahram ; Pradhan, Biswajeet ; Keesstra, Saskia ; Ghiasi, Seid Saeid ; Sadeghi, Seyed Hamidreza - \ 2019
Journal of Environmental Management 232 (2019). - ISSN 0301-4797 - p. 22 - 36.
Analytical network process - Fuzzy theory - Multi-criteria decision analysis (MCDA) - Soil erosion - Watershed health
Assessment of watershed health and prioritization of sub-watersheds are needed to allocate natural resources and efficiently manage watersheds. Characterization of health and spatial prioritization of sub-watersheds in data scarce regions helps better comprehend real watershed conditions and design and implement management strategies. Previous studies on the assessment of health and prioritization of sub-watersheds in ungauged regions have not considered environmental factors and their inter-relationship. In this regard, fuzzy logic theory can be employed to improve the assessment of watershed health. The present study considered a combination of climate vulnerability (Climate Water Balance), relative erosion rate of surficial rocks, slope weighted K-factor, topographic indices, thirteen morphometric characteristics (linear, areal, and relief aspects), and potential non-point source pollution to assess watershed health, using a new framework which considers the complex linkage between human activities and natural resources. The new framework, focusing on watershed health score (WHS), was employed for the spatial prioritization of 31 sub-watersheds in the Khoy watershed, West Azerbaijan Province, Iran. In this framework, an analytical network process (ANP) and fuzzy theory were used to investigate the inter-relationships between the above mentioned geo-environmental factors and to classify and rank the health of each sub-watershed in four classes. Results demonstrated that only one sub-watershed (C15) fell into the class that was defined as ‘a potentially critical zone’. This article provides a new framework and practical recommendations for watershed management agencies with a high level of assurance when there is a lack of reliable hydrometric gauge data.
How can statistical and artificial intelligence approaches predict piping erosion susceptibility?
Hosseinalizadeh, Mohsen ; Kariminejad, Narges ; Rahmati, Omid ; Keesstra, Saskia ; Alinejad, Mohammad ; Mohammadian Behbahani, Ali - \ 2019
Science of the Total Environment 646 (2019). - ISSN 0048-9697 - p. 1554 - 1566.
Loess plateau - Machine learning algorithms - Piping collapse - Susceptibility map - Unmanned aerial vehicle (UAV)
It is of fundamental importance to model the relationship between geo-environmental factors and piping erosion because of the environmental degradation attributed to soil loss. Methods that identify areas prone to piping erosion at the regional scale are limited. The main objective of this research is to develop a novel modeling approach by using three machine learning algorithms—mixture discriminant analysis (MDA), flexible discriminant analysis (FDA), and support vector machine (SVM) in addition to an unmanned aerial vehicle (UAV) images to map susceptibility to piping erosion in the loess-covered hilly region of Golestan Province, Northeast Iran. In this research, we have used 22 geo-environmental indices/factors and 345 identified pipes as predictors and dependent variables. The piping susceptibility maps were assessed by the area under the ROC curve (AUC). Validation of the results showed that the AUC for the three mentioned algorithms varied from 90.32% to 92.45%. We concluded that the proposed approach could efficiently produce a piping susceptibility map.
Development and analysis of the Soil Water Infiltration Global database
Rahmati, Mehdi ; Weihermüller, Lutz ; Vanderborght, Jan ; Pachepsky, Yakov A. ; Mao, Lili ; Sadeghi, Seyed Hamidreza ; Moosavi, Niloofar ; Kheirfam, Hossein ; Montzka, Carsten ; Looy, Kris Van; Toth, Brigitta ; Hazbavi, Zeinab ; Yamani, Wafa Al; Albalasmeh, Ammar A. ; Alghzawi, M.Z. ; Angulo-Jaramillo, Rafael ; Antonino, Antônio Celso Dantas ; Arampatzis, George ; Armindo, Robson André ; Asadi, Hossein ; Bamutaze, Yazidhi ; Batlle-Aguilar, Jordi ; Béchet, Béatrice ; Becker, Fabian ; Blöschl, Günter ; Bohne, Klaus ; Braud, Isabelle ; Castellano, Clara ; Cerdà, Artemi ; Chalhoub, Maha ; Cichota, Rogerio ; Císlerová, Milena ; Clothier, Brent ; Coquet, Yves ; Cornelis, Wim ; Corradini, Corrado ; Coutinho, Artur Paiva ; Oliveira, Muriel Bastista De; Macedo, José Ronaldo De; Durães, Matheus Fonseca ; Emami, Hojat ; Eskandari, Iraj ; Farajnia, Asghar ; Flammini, Alessia ; Fodor, Nándor ; Gharaibeh, Mamoun ; Ghavimipanah, Mohamad Hossein ; Ghezzehei, Teamrat A. ; Giertz, Simone ; Hatzigiannakis, Evangelos G. ; Horn, Rainer ; Jiménez, Juan José ; Jacques, Diederik ; Keesstra, Saskia Deborah ; Kelishadi, Hamid ; Kiani-Harchegani, Mahboobeh ; Kouselou, Mehdi ; Jha, Madan Kumar ; Lassabatere, Laurent ; Li, Xiaoyan ; Liebig, Mark A. ; Lichner, Lubomír ; López, María Victoria ; Machiwal, Deepesh ; Mallants, Dirk ; Mallmann, Micael Stolben ; Oliveira Marques, Jean Dalmo De; Marshall, Miles R. ; Mertens, Jan ; Meunier, Félicien ; Mohammadi, Mohammad Hossein ; Mohanty, Binayak P. ; Pulido-Moncada, Mansonia ; Montenegro, Suzana ; Morbidelli, Renato ; Moret-Fernández, David ; Moosavi, Ali Akbar ; Mosaddeghi, Mohammad Reza ; Mousavi, Seyed Bahman ; Mozaffari, Hasan ; Nabiollahi, Kamal ; Neyshabouri, Mohammad Reza ; Ottoni, Marta Vasconcelos ; Ottoni Filho, Theophilo Benedicto ; Pahlavan-Rad, Mohammad Reza ; Panagopoulos, Andreas ; Peth, Stephan ; Peyneau, Pierre Emmanuel ; Picciafuoco, Tommaso ; Poesen, Jean ; Pulido, Manuel ; Reinert, Dalvan José ; Reinsch, Sabine ; Rezaei, Meisam ; Roberts, Francis Parry ; Robinson, David ; Rodrigo-Comino, Jesüs ; Rotunno Filho, Otto Corrêa ; Saito, Tadaomi ; Suganuma, Hideki ; Saltalippi, Carla ; Sándor, Renáta ; Schütt, Brigitta ; Seeger, Manuel ; Sepehrnia, Nasrollah ; Sharifi Moghaddam, Ehsan ; Shukla, Manoj ; Shutaro, Shiraki ; Sorando, Ricardo ; Stanley, Ajayi Asishana ; Strauss, Peter ; Su, Zhongbo ; Taghizadeh-Mehrjardi, Ruhollah ; Taguas, Encarnación ; Teixeira, Wenceslau Geraldes ; Vaezi, Ali Reza ; Vafakhah, Mehdi ; Vogel, Tomas ; Vogeler, Iris ; Votrubova, Jana ; Werner, Steffen ; Winarski, Thierry ; Yilmaz, Deniz ; Young, Michael H. ; Zacharias, Steffen ; Zeng, Yijian ; Zhao, Ying ; Zhao, Hong ; Vereecken, Harry - \ 2018
Earth System Science Data 10 (2018)3. - ISSN 1866-3508 - p. 1237 - 1263.
In this paper, we present and analyze a novel global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists who performed the experiments or they were digitized from published articles. Data from 54 different countries were included in the database with major contributions from Iran, China, and the USA. In addition to its extensive geographical coverage, the collected infiltration curves cover research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use was gathered along with the infiltration data, making the database valuable for the development of pedotransfer functions (PTFs) for estimating soil hydraulic properties, for the evaluation of infiltration measurement methods, and for developing and validating infiltration models. Soil textural information (clay, silt, and sand content) is available for 3842 out of 5023 infiltration measurements (∼76%) covering nearly all soil USDA textural classes except for the sandy clay and silt classes. Information on land use is available for 76ĝ€% of the experimental sites with agricultural land use as the dominant type (∼40%). We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models. All collected data and related soil characteristics are provided online in ∗.xlsx and ∗.csv formats for reference, and we add a disclaimer that the database is for public domain use only and can be copied freely by referencing it. Supplementary data are available at https://doi.org/10.1594/PANGAEA.885492 (Rahmati et al., 2018). Data quality assessment is strongly advised prior to any use of this database. Finally, we would like to encourage scientists to extend and update the SWIG database by uploading new data to it.