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A Comparative Analysis of Analytical Hierarchy Process and Fuzzy Logic Modeling in Flood Susceptibility Mapping in the Assaka Watershed, Morocco

Khaddari Achraf, Jari Abdessamad, Chakiri Saïd, El Hadi Hassan, Labriki Allal, Hajaj Soufiane, Goumghar Lahcen, El Harti Abderrazak, Abioui Mohamed

Journal of Ecological Engineering

2023

Vol. 24, nr 8

62--83

The Assaka watershed is one of the largest watersheds in the Guelmim region in southern Morocco. It is frequently exposed to the many flooding events that can be responsible for many costly human and material damages. This work illustrates a decision-making methodology based on Analytical Hierarchy Process (AHP) and Fuzzy Logic Modelling (FLM), in the order to perform a useful flood susceptibility mapping in the study area. Seven decisive factors were introduced, namely, flow accumulation, distance to the hydrographic network, elevation, slope, LULC, lithology, and rainfall. The susceptibility maps were obtained after normalization and weighting using the AHP, while after Fuzzification as well as the application of fuzzy operators (OR, SUM, PRODUCT, AND, GAMMA 0.9) for the fuzzy logic methods. Thereafter, the flood susceptibility zones were distributed into five flood intensity classes with very high, high, medium, low, and, very low susceptibility. Then validated by field observations, an inventory of flood-prone sites identified by the Draa Oued Noun Hydraulic Watershed Agency (DONHBA) with 71 carefully selected flood-prone sites and GeoEye-1 satellite images. The assessment of the mapping results using the ROC curve shows that the best results are derived from applying the fuzzy SUM (AUC = 0.901) and fuzzy OR (AUC = 0.896) operators. On the other hand, the AHP method (AUC = 0.893) shows considerable mapping results. Then, a comparison of the two methods of SUM fuzzy logic and AHP allowed considering the two techniques as complementary to each other. They can accurately model the flood susceptibility of the Assaka watershed. Specifically, this area is characterized by a high to very high risk of flooding, which was estimated at 67% and 30% of the total study area coverage using the fuzzy logic (SUM operator) and the AHP methods, respectively. Highly susceptible flood areas require immediate action in terms of planning, development, and land use management to avoid any dramatic disaster.

Flood susceptibility mapping using qualitative and statistical methods in a semi-arid basin: case of the Manouba–Sijoumi watershed, Northeastern Tunisia

Khadraoui Nahed, Dahri Noura, Bouamrane Ali, Pham Quoc Bao, Abida Habib

Acta Geophysica

2023

Vol. 71, no. 5

2307--2323

Floods are considered among the gravest natural disasters worldwide and have resulted in enormous human and material damage. The Manouba–Sijoumi basin (Northeast of Tunisia) is often flooded due to urban expansion, population growth and unplanned land use. This study aims to identify and to define the flood-prone areas of this basin for the 2003 and 2018 extreme events based on a Geographic Information System, a qualitative method (analytic network process-ANP) and a statistical model (frequency ratio-FR). The flood risk maps obtained by both models were validated using the receiver operating characteristic, the area under the curve (AUC) and inventory map. Areas of high and very high flood sensitivity are located mainly in urban settings, with an increase in risk between 2003 and 2018. The AUC values for both models were of the same significance (98%) for the year 2003 while those for the year 2018 were 94% and 98% for the ANP and FR models, respectively. This would imply that both models yielded reasonable results. However, the FR model showed an ability to reduce the uncertainty associated with expert judgements. The results indicate that the most influential factor on flooding in this area was land use/cover. Indeed, populations were largely settled in unsuitable sites for urbanization and in potentially flood-prone areas located mainly around the Sijoumi Sebkha, especially to the west and south of it. The findings of the study are of great value for policy makers and state authorities to achieve greater awareness and adopt strategies for environmental preparedness and management.

Flood susceptibility mapping using advanced hybrid machine learning and CyGNSS: a case study of Nghe An province, Vietnam

Nguyen Huu Duy, Vu Phương Lan, Ha Minh Cuong, Dinh Thi Bao Hoa, Nguyen Thuy Hang, Hoang Tich Phuc, Doan Quang Cuong, Pham Van Manh, Dang Dinh Kha

Acta Geophysica

2022

Vol. 70, no 6

2785--2803

Flooding is currently the most dangerous natural hazard. It can have heavy human and material impacts and, in recent years, flooding has tended to occur more frequently, due to changes our species has made to hydrological regimes, and due to climate change. It is of the utmost importance that new models are developed to predict and map food susceptibility with high accuracy, to support decision-makers and planners in designing more effective food management strategies. The objective of this study is the development of a new method based on state-of-the-art machine learning and remote sensing, namely random forest (RF), dingo optimization algorithm, a weighted chimp optimization algorithm (WChOA), and particle swarm optimization to build food susceptibility maps in the Nghe An province of Vietnam. The CyGNSS system was used to collect soil moisture data to integrate into the susceptibility model. A total of 1650 food locations and 14 conditioning factors were used to construct the model. These data were divided at a ratio of 60/20/20 to train, validate, and test the model, respectively. In addition, various statistical indices, namely root-mean-square error, receiver operation characteristic, mean absolute error, and the coefficient of determination (R2 ), were used to assess the performance of the model. The results for all the models were good, with an AUC value of+0.9. The RF-WChOA model performed best, with an AUC value of 0.99. The proposed models can predict and map food susceptibility with high accuracy.