Enhancing the Performance of Machine Learning and Deep Learning-Based Flood Susceptibility Models by Integrating Grey Wolf Optimizer (GWO) Algorithm
Abstract
:1. Introduction
2. Materials and Methods
2.1. Methodology
2.2. Study Area
2.3. Flood Inventory Map
2.4. Flood Conditioning Factors
2.5. Multicollinearity Assessment
2.6. Models
2.6.1. Frequency Ratio (FR) Model
2.6.2. Recurrent Neural Networks (RNNs)
2.6.3. Support Vector Regression (SVR)
2.6.4. XGBoost (eXtreme Gradient Boosting)
2.6.5. Grey Wolf Optimizer (GWO)
2.7. Performance Measures
2.7.1. Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE)
2.7.2. R-Squared (R2)
2.7.3. Area under the Receiver Operating Characteristic (ROC) Curve (AUC)
2.7.4. Chi-Squared Test
2.7.5. Taylor Diagram
2.7.6. Model Implementation
3. Results
3.1. Result of Multicollinearity Test
3.2. Frequency Ratio (FR) Result
3.3. Result of the Modelling
3.4. Assessment of Contributing Factors
3.5. Creation of Flood Susceptibility Maps
3.6. Validation of Flood Susceptibility Maps
4. Discussion
4.1. Influence of the Conditioning Factors on Flooding
4.2. Performance of the Models
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Talbot, C.J.; Bennett, E.M.; Cassell, K.; Hanes, D.M.; Minor, E.C.; Paerl, H.; Raymond, P.A.; Vargas, R.; Vidon, P.G.; Wollheim, W.; et al. The Impact of Flooding on Aquatic Ecosystem Services. Biogeochemistry 2018, 141, 439–461. [Google Scholar] [CrossRef] [PubMed]
- Zhong, S.; Yang, L.; Toloo, S.; Wang, Z.; Tong, S.; Sun, X.; Crompton, D.; FitzGerald, G.; Huang, C. The Long-Term Physical and Psychological Health Impacts of Flooding: A Systematic Mapping. Sci. Total Environ. 2018, 626, 165–194. [Google Scholar] [CrossRef] [PubMed]
- Khayyam, U.; Noureen, S. Assessing the Adverse Effects of Flooding for the Livelihood of the Poor and the Level of External Response: A Case Study of Hazara Division, Pakistan. Environ. Sci. Pollut. Res. 2020, 27, 19638–19649. [Google Scholar] [CrossRef] [PubMed]
- Jonkman, S.N.; Curran, A.; Bouwer, L.M. Floods Have Become Less Deadly: An Analysis of Global Flood Fatalities 1975–2022. Nat. Hazards 2024, 120, 6327–6342. [Google Scholar] [CrossRef]
- Hirabayashi, Y.; Tanoue, M.; Sasaki, O.; Zhou, X.; Yamazaki, D. Global Exposure to Flooding from the New CMIP6 Climate Model Projections. Sci. Rep. 2021, 11, 3740. [Google Scholar] [CrossRef] [PubMed]
- Zhang, J.; Liao, X.; Xu, W. Mapping Global Risk of GDP Loss to River Floods. In Atlas of Global Change Risk of Population and Economic Systems; Springer: Singapore, 2022; pp. 203–210. [Google Scholar] [CrossRef]
- Liu, T.; Shi, P.; Fang, J. Spatiotemporal Variation in Global Floods with Different Affected Areas and the Contribution of Influencing Factors to Flood-Induced Mortality (1985–2019). Nat. Hazards 2022, 111, 2601–2625. [Google Scholar] [CrossRef]
- Imamura, Y. Development of a Method for Assessing Country-Based Flood Risk at the Global Scale. Int. J. Disaster Risk Sci. 2022, 13, 87–99. [Google Scholar] [CrossRef]
- Dhar, O.N.; Nandargi, S. Hydrometeorological Aspects of Floods in India. In Flood Problem and Management in South Asia; Springer: Dordrecht, The Netherlands, 2003; pp. 1–33. [Google Scholar] [CrossRef]
- Gupta, S.; Javed, A.; Datt, D. Economics of Flood Protection in India. In Flood Problem and Management in South Asia; Springer: Dordrecht, The Netherlands, 2003; pp. 199–210. [Google Scholar] [CrossRef]
- Mishra, V.; Shah, H.L. Hydroclimatological Perspective of the Kerala Flood of 2018. J. Geol. Soc. India 2018, 92, 645–650. [Google Scholar] [CrossRef]
- Senan, C.P.C.; Ajin, R.S.; Danumah, J.H.; Costache, R.; Arabameri, A.; Rajaneesh, A.; Sajinkumar, K.S.; Kuriakose, S.L. Flood Vulnerability of a Few Areas in the Foothills of the Western Ghats: A Comparison of AHP and F-AHP Models. Stoch. Environ. Res. Risk Assess. 2022, 37, 527–556. [Google Scholar] [CrossRef] [PubMed]
- Vishnu, C.L.; Sajinkumar, K.S.; Oommen, T.; Coffman, R.A.; Thrivikramji, K.P.; Rani, V.R.; Keerthy, S. Satellite-Based Assessment of the August 2018 Flood in Parts of Kerala, India. Geomat. Nat. Hazards Risk 2019, 10, 758–767. [Google Scholar] [CrossRef]
- Razavi-Termeh, S.V.; Sadeghi-Niaraki, A.; Seo, M.; Choi, S.-M. Application of Genetic Algorithm in Optimization Parallel Ensemble-Based Machine Learning Algorithms to Flood Susceptibility Mapping Using Radar Satellite Imagery. Sci. Total Environ. 2023, 873, 162285. [Google Scholar] [CrossRef] [PubMed]
- Negese, A.; Worku, D.; Shitaye, A.; Getnet, H. Potential Flood-Prone Area Identification and Mapping Using GIS-Based Multi-Criteria Decision-Making and Analytical Hierarchy Process in Dega Damot District, Northwestern Ethiopia. Appl. Water Sci. 2022, 12, 255. [Google Scholar] [CrossRef]
- Liu, B.; Lu, W. Surrogate models in machine learning for computational stochastic multi-scale modelling in composite materials design. Int. J. Hydromechatron. 2022, 5, 336–365. [Google Scholar] [CrossRef]
- Ley, C.; Martin, R.K.; Pareek, A.; Groll, A.; Seil, R.; Tischer, T. Machine Learning and Conventional Statistics: Making Sense of the Differences. Knee Surg. Sports Traumatol. Arthrosc. 2022, 30, 753–757. [Google Scholar] [CrossRef]
- Taherdoost, H.; Madanchian, M. Analytic Network Process (ANP) Method: A Comprehensive Review of Applications, Advantages, and Limitations. J. Data Sci. Intell. Syst. 2023, 1, 1–7. [Google Scholar] [CrossRef]
- Yalcin, A.; Reis, S.; Aydinoglu, A.C.; Yomralioglu, T. A GIS-Based Comparative Study of Frequency Ratio, Analytical Hierarchy Process, Bivariate Statistics and Logistics Regression Methods for Landslide Susceptibility Mapping in Trabzon, NE Turkey. Catena 2011, 85, 274–287. [Google Scholar] [CrossRef]
- Chinthamu, N.; Karukuri, M. Data science and applications. J. Data Sci. Intell. Syst. 2023, 1, 83–91. [Google Scholar] [CrossRef]
- Wang, H.; Duentsch, I.; Guo, G.; Khan, S.A. Special Issue on Small Data Analytics. Int. J. Mach. Learn. Cybern. 2022, 14, 1–2. [Google Scholar] [CrossRef]
- Onyango, A.; Okelo, B.; Omollo, R. Topological data analysis of COVID-19 using artificial intelligence and machine learning techniques in big datasets of hausdorff spaces. J. Data Sci. Intell. Syst. 2023, 1, 55–64. [Google Scholar] [CrossRef]
- Aldoseri, A.; Al-Khalifa, K.N.; Hamouda, A.M. Re-Thinking Data Strategy and Integration for Artificial Intelligence: Concepts, Opportunities, and Challenges. Appl. Sci. 2023, 13, 7082. [Google Scholar] [CrossRef]
- Hasanuzzaman, M.; Islam, A.; Bera, B.; Shit, P.K. A Comparison of Performance Measures of Three Machine Learning Algorithms for Flood Susceptibility Mapping of River Silabati (Tropical River, India). Phys. Chem. Earth Parts A/B/C 2022, 127, 103198. [Google Scholar] [CrossRef]
- Yin, L.; Li, B.; Li, P.; Zhang, R. Research on stock trend prediction method based on optimized random forest. CAAI Trans. Intell. Technol. 2023, 8, 274–284. [Google Scholar] [CrossRef]
- Tehrany, M.S.; Pradhan, B.; Mansor, S.; Ahmad, N. Flood Susceptibility Assessment Using GIS-Based Support Vector Machine Model with Different Kernel Types. Catena 2015, 125, 91–101. [Google Scholar] [CrossRef]
- Rezaie, F.; Panahi, M.; Bateni, S.M.; Jun, C.; Neale, C.M.U.; Lee, S. Novel Hybrid Models by Coupling Support Vector Regression (SVR) with Meta-Heuristic Algorithms (WOA and GWO) for Flood Susceptibility Mapping. Nat. Hazards 2022, 114, 1247–1283. [Google Scholar] [CrossRef]
- Chao, Q.; Xu, Z.; Shao, Y.; Tao, J.; Liu, C.; Ding, S. Hybrid model-driven and data-driven approach for the health assessment of axial piston pumps. Int. J. Hydromechatron. 2023, 6, 76–92. [Google Scholar] [CrossRef]
- Priscillia, S.; Schillaci, C.; Lipani, A. Flood Susceptibility Assessment Using Artificial Neural Networks in Indonesia. Artif. Intell. Geosci. 2021, 2, 215–222. [Google Scholar] [CrossRef]
- Razavi-Termeh, S.V.; Sadeghi-Niaraki, A.; Razavi, S.; Choi, S.-M. Enhancing Flood-Prone Area Mapping: Fine-Tuning the K-Nearest Neighbors (KNN) Algorithm for Spatial Modelling. Int. J. Digit. Earth 2024, 17, 2311325. [Google Scholar] [CrossRef]
- Khosravi, K.; Pham, B.T.; Chapi, K.; Shirzadi, A.; Shahabi, H.; Revhaug, I.; Prakash, I.; Tien Bui, D. A Comparative Assessment of Decision Trees Algorithms for Flash Flood Susceptibility Modeling at Haraz Watershed, Northern Iran. Sci. Total Environ. 2018, 627, 744–755. [Google Scholar] [CrossRef]
- Lyu, H.-M.; Yin, Z.-Y. Flood Susceptibility Prediction Using Tree-Based Machine Learning Models in the GBA. Sustain. Cities Soc. 2023, 97, 104744. [Google Scholar] [CrossRef]
- Zhang, Q.; Xiao, J.; Tian, C.; Lin, J.C.-W.; Zhang, S. A robust deformed convolutional neural network (CNN) for image denoising. CAAI Trans. Intell. Technol. 2023, 8, 331–342. [Google Scholar] [CrossRef]
- Wang, Y.; Fang, Z.; Hong, H.; Peng, L. Flood Susceptibility Mapping Using Convolutional Neural Network Frameworks. J. Hydrol. 2020, 582, 124482. [Google Scholar] [CrossRef]
- Panahi, M.; Jaafari, A.; Shirzadi, A.; Shahabi, H.; Rahmati, O.; Omidvar, E.; Lee, S.; Bui, D.T. Deep Learning Neural Networks for Spatially Explicit Prediction of Flash Flood Probability. Geosci. Front. 2021, 12, 101076. [Google Scholar] [CrossRef]
- Li, Z.; Li, S. Recursive recurrent neural network: A novel model for manipulator control with different levels of physical constraints. CAAI Trans. Intell. Technol. 2023, 8, 622–634. [Google Scholar] [CrossRef]
- Adnan, M.S.G.; Siam, Z.S.; Kabir, I.; Kabir, Z.; Ahmed, M.R.; Hassan, Q.K.; Rahman, R.M.; Dewan, A. A Novel Framework for Addressing Uncertainties in Machine Learning-Based Geospatial Approaches for Flood Prediction. J. Environ. Manag. 2023, 326, 116813. [Google Scholar] [CrossRef] [PubMed]
- Awad, M.; Khanna, R. Support Vector Regression. In Efficient Learning Machines; Apress: Berkeley, CA, USA, 2015; pp. 67–80. [Google Scholar] [CrossRef]
- Mesut, B.; Başkor, A.; Buket Aksu, N. Role of Artificial Intelligence in Quality Profiling and Optimization of Drug Products. In A Handbook of Artificial Intelligence in Drug Delivery; Academic Press: Cambridge, MA, USA, 2023; pp. 35–54. [Google Scholar] [CrossRef]
- Tarwidi, D.; Pudjaprasetya, S.R.; Adytia, D.; Apri, M. An Optimized XGBoost-Based Machine Learning Method for Predicting Wave Run-up on a Sloping Beach. MethodsX 2023, 10, 102119. [Google Scholar] [CrossRef] [PubMed]
- Razavi-Termeh, S.V.; Seo, M.; Sadeghi-Niaraki, A.; Choi, S.-M. Flash Flood Detection and Susceptibility Mapping in the Monsoon Period by Integration of Optical and Radar Satellite Imagery Using an Improvement of a Sequential Ensemble Algorithm. Weather Clim. Extrem. 2023, 41, 100595. [Google Scholar] [CrossRef]
- Belyadi, H.; Haghighat, A. Supervised Learning. In Machine Learning Guide for Oil and Gas Using Python; Gulf Professional Publishing: Houston, TX, USA, 2021; pp. 169–295. [Google Scholar] [CrossRef]
- Pilcevic, D.; Djuric Jovicic, M.; Antonijevic, M.; Bacanin, N.; Jovanovic, L.; Zivkovic, M.; Dragovic, M.; Bisevac, P. Performance Evaluation of Metaheuristics-Tuned Recurrent Neural Networks for Electroencephalography Anomaly Detection. Front. Physiol. 2023, 14, 1267011. [Google Scholar] [CrossRef] [PubMed]
- Yang, L.; Shami, A. On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice. Neurocomputing 2020, 415, 295–316. [Google Scholar] [CrossRef]
- HajimohamadzadehTorkambour, S.; Nejad, M.J.; Pazoki, F.; Karimi, F.; Heydari, A. Synthesis and characterization of a green and recyclable arginine-based palladium/CoFe2O4 nanomagnetic catalyst for efficient cyanation of aryl halides. RSC Adv. 2024, 14, 14139–14151. [Google Scholar] [CrossRef] [PubMed]
- Yan, F.; Xu, J.; Yun, K. Dynamically Dimensioned Search Grey Wolf Optimizer Based on Positional Interaction Information. Complexity 2019, 2019, 7189653. [Google Scholar] [CrossRef]
- Razavi-Termeh, S.V.; Sadeghi-Niaraki, A.; Choi, S.-M. A New Approach Based on Biology-Inspired Metaheuristic Algorithms in Combination with Random Forest to Enhance the Flood Susceptibility Mapping. J. Environ. Manag. 2023, 345, 118790. [Google Scholar] [CrossRef] [PubMed]
- Yetkin, M.; Bilginer, O. On the Application of Nature-Inspired Grey Wolf Optimizer Algorithm in Geodesy. J. Geod. Sci. 2020, 10, 48–52. [Google Scholar] [CrossRef]
- Wang, J.-S.; Li, S.-X. An Improved Grey Wolf Optimizer Based on Differential Evolution and Elimination Mechanism. Sci. Rep. 2019, 9, 7181. [Google Scholar] [CrossRef] [PubMed]
- Saad, A.; Dong, Z.; Karimi, M. A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design. Algorithms 2017, 10, 120. [Google Scholar] [CrossRef]
- Amrutha, A.S.; Varghese, A.; Prakash, S.; Baiju, K.R. Hydrometeorological Landslides on the Windward Side of Western Ghats—A Case Study of Kootickal, Kerala, India. J. Geospat. Surv. 2023, 3, 2. [Google Scholar] [CrossRef]
- Ajin, R.S.; Saha, S.; Saha, A.; Biju, A.; Costache, R.; Kuriakose, S.L. Enhancing the Accuracy of the REPTree by Integrating the Hybrid Ensemble Meta-Classifiers for Modelling the Landslide Susceptibility of Idukki District, South-Western India. J. Indian Soc. Remote Sens. 2022, 50, 2245–2265. [Google Scholar] [CrossRef]
- Anchima, S.J.; Gokul, A.; Senan, C.P.C.; Danumah, J.H.; Saha, S.; Sajinkumar, K.S.; Rajaneesh, A.; Johny, A.; Mammen, P.C.; Ajin, R.S. Vulnerability Evaluation Utilizing AHP and an Ensemble Model in a Few Landslide-Prone Areas of the Western Ghats, India. Environ. Dev. Sustain. 2023, 1–44. [Google Scholar] [CrossRef]
- Saleem, N.; Huq, M.E.; Twumasi, N.Y.D.; Javed, A.; Sajjad, A. Parameters Derived from and/or Used with Digital Elevation Models (DEMs) for Landslide Susceptibility Mapping and Landslide Risk Assessment: A Review. ISPRS Int. J. Geo-Inf. 2019, 8, 545. [Google Scholar] [CrossRef]
- Beven, K.J.; Kirkby, M.J. A Physically Based, Variable Contributing Area Model of Basin Hydrology/Un Modèle à Base Physique de Zone d’appel Variable de l’hydrologie Du Bassin Versant. Hydrol. Sci. Bull. 1979, 24, 43–69. [Google Scholar] [CrossRef]
- Mojaddadi, H.; Pradhan, B.; Nampak, H.; Ahmad, N.; Ghazali, A.H. bin Ensemble Machine-Learning-Based Geospatial Approach for Flood Risk Assessment Using Multi-Sensor Remote-Sensing Data and GIS. Geomat. Nat. Hazards Risk 2017, 8, 1080–1102. [Google Scholar] [CrossRef]
- Chowdhury, M.S. Modelling Hydrological Factors from DEM Using GIS. MethodsX 2023, 10, 102062. [Google Scholar] [CrossRef] [PubMed]
- As-Syakur, A.R.; Adnyana, I.W.; Arthana, I.W.; Nuarsa, I.W. Enhanced Built-Up and Bareness Index (EBBI) for Mapping Built-Up and Bare Land in an Urban Area. Remote Sens. 2012, 4, 2957–2970. [Google Scholar] [CrossRef]
- Nuraini, L.; Nugraha, A.S.A.; Yanti, R.A.; Janah, L. Comparison Normalized Dryness Built-Up Index (NDBI) with Enhanced Built-Up and Bareness Index (EBBI) for Identification Urban in Buleleng Sub-District. Media Komun. FPIPS 2022, 21, 74–82. [Google Scholar] [CrossRef]
- Salma; Nikhil, S.; Danumah, J.H.; Prasad, M.K.; Nazar, N.; Saha, S.; Mammen, P.C.; Ajin, R.S. Prediction Capability of the MCDA-AHP Model in Wildfire Risk Zonation of a Protected Area in the Southern Western Ghats. Environ. Sustain. 2023, 6, 59–72. [Google Scholar] [CrossRef]
- Bhagya, S.B.; Sumi, A.S.; Balaji, S.; Danumah, J.H.; Costache, R.; Rajaneesh, A.; Gokul, A.; Chandrasenan, C.P.; Quevedo, R.P.; Johny, A.; et al. Landslide Susceptibility Assessment of a Part of the Western Ghats (India) Employing the AHP and F-AHP Models and Comparison with Existing Susceptibility Maps. Land 2023, 12, 468. [Google Scholar] [CrossRef]
- Færgestad, E.M.; Langsrud, Ø.; Høy, M.; Hollung, K.; Sæbø, S.; Liland, K.H.; Kohler, A.; Gidskehaug, L.; Almergren, J.; Anderssen, E.; et al. Analysis of Megavariate Data in Functional Genomics. In Comprehensive Chemometrics; Elsevier: Amsterdam, The Netherlands, 2009; pp. 221–278. [Google Scholar] [CrossRef]
- Siegel, A.F.; Wagner, M.R. Multiple Regression. In Practical Business Statistics; Academic Press: Cambridge, MA, USA, 2022; pp. 371–431. [Google Scholar] [CrossRef]
- Sinha, A.; Nikhil, S.; Ajin, R.S.; Danumah, J.H.; Saha, S.; Costache, R.; Rajaneesh, A.; Sajinkumar, K.S.; Amrutha, K.; Johny, A.; et al. Wildfire Risk Zone Mapping in Contrasting Climatic Conditions: An Approach Employing AHP and F-AHP Models. Fire 2023, 6, 44. [Google Scholar] [CrossRef]
- Eskandari, E.; Alimoradi, H.; Pourbagian, M.; Shams, M. Numerical investigation and deep learning-based prediction of heat transfer characteristics and bubble dynamics of subcooled flow boiling in a vertical tube. Korean J. Chem. Eng. 2022, 39, 3227–3245. [Google Scholar] [CrossRef]
- Pradeep, G.S.; Patel, N.; Kuriakose, S.L.; Ajin, R.S.; Oniga, V.-E.; Rajaneesh, A.; Mammen, P.C.; Prasad, M.K.; Nikhil, S.; Danumah, J.H. Forest Fire Risk Zone Mapping of Eravikulam National Park in India. Croat. J. For. Eng. 2021, 43, 199–217. [Google Scholar] [CrossRef]
- Thomas, A.V.; Saha, S.; Danumah, J.H.; Raveendran, S.; Prasad, M.K.; Ajin, R.S.; Kuriakose, S.L. Landslide Susceptibility Zonation of Idukki District Using GIS in the Aftermath of 2018 Kerala Floods and Landslides: A Comparison of AHP and Frequency Ratio Methods. J. Geovis. Spat. Anal. 2021, 5, 21. [Google Scholar] [CrossRef]
- Jena, R.; Pradhan, B.; Alamri, A.M. Susceptibility to Seismic Amplification and Earthquake Probability Estimation Using Recurrent Neural Network (RNN) Model in Odisha, India. Appl. Sci. 2020, 10, 5355. [Google Scholar] [CrossRef]
- Wang, Y.; Fang, Z.; Wang, M.; Peng, L.; Hong, H. Comparative Study of Landslide Susceptibility Mapping with Different Recurrent Neural Networks. Comput. Geosci. 2020, 138, 104445. [Google Scholar] [CrossRef]
- Khosravi, K.; Rezaie, F.; Cooper, J.R.; Kalantari, Z.; Abolfathi, S.; Hatamiafkoueieh, J. Soil Water Erosion Susceptibility Assessment Using Deep Learning Algorithms. J. Hydrol. 2023, 618, 129229. [Google Scholar] [CrossRef]
- Saha, A.; Pal, S.; Arabameri, A.; Blaschke, T.; Panahi, S.; Chowdhuri, I.; Chakrabortty, R.; Costache, R.; Arora, A. Flood Susceptibility Assessment Using Novel Ensemble of Hyperpipes and Support Vector Regression Algorithms. Water 2021, 13, 241. [Google Scholar] [CrossRef]
- Ji, C.; Ma, F.; Wang, J.; Sun, W. Early Identification of Abnormal Deviations in Nonstationary Processes by Removing Non-Stationarity. Comput. Aided Chem. Eng. 2022, 49, 1393–1398. [Google Scholar] [CrossRef]
- Zhang, L.; Arabameri, A.; Santosh, M.; Pal, S.C. Land Subsidence Susceptibility Mapping: Comparative Assessment of the Efficacy of the Five Models. Environ. Sci. Pollut. Res. 2023, 30, 77830–77849. [Google Scholar] [CrossRef] [PubMed]
- Subasi, A.; Panigrahi, S.S.; Patil, B.S.; Canbaz, M.A.; Klén, R. Advanced Pattern Recognition Tools for Disease Diagnosis. In 5G IoT and Edge Computing for Smart Healthcare; Academic Press: Cambridge, MA, USA, 2022; pp. 195–229. [Google Scholar] [CrossRef]
- Shams, M.Y.; Elshewey, A.M.; El-kenawy, E.-S.M.; Ibrahim, A.; Talaat, F.M.; Tarek, Z. Water Quality Prediction Using Machine Learning Models Based on Grid Search Method. Multimed. Tools Appl. 2023, 83, 35307–35334. [Google Scholar] [CrossRef]
- Mirjalili, S.; Mirjalili, S.M.; Lewis, A. Grey Wolf Optimizer. Adv. Eng. Softw. 2014, 69, 46–61. [Google Scholar] [CrossRef]
- Xu, X. Revolutionizing Education: Advanced Machine Learning Techniques for Precision Recommendation of Top-Quality Instructional Materials. Int. J. Comput. Intell. Syst. 2023, 16, 179. [Google Scholar] [CrossRef]
- Jierula, A.; Wang, S.; OH, T.-M.; Wang, P. Study on Accuracy Metrics for Evaluating the Predictions of Damage Locations in Deep Piles Using Artificial Neural Networks with Acoustic Emission Data. Appl. Sci. 2021, 11, 2314. [Google Scholar] [CrossRef]
- Melo, F. Area under the ROC Curve. In Encyclopedia of Systems Biology; Springer: New York, NY, USA, 2013; pp. 38–39. [Google Scholar] [CrossRef]
- Hosmer, D.W.; Lemeshow, S. Applied Logistic Regression; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2000. [Google Scholar] [CrossRef]
- Tanyu, B.F.; Abbaspour, A.; Alimohammadlou, Y.; Tecuci, G. Landslide Susceptibility Analyses Using Random Forest, C4.5, and C5.0 with Balanced and Unbalanced Datasets. Catena 2021, 203, 105355. [Google Scholar] [CrossRef]
- Izzaddin, A.; Langousis, A.; Totaro, V.; Yaseen, M.; Iacobellis, V. A New Diagram for Performance Evaluation of Complex Models. Stoch. Environ. Res. Risk Assess. 2024, 38, 2261–2281. [Google Scholar] [CrossRef]
- Paul, A.; Afroosa, M.; Baduru, B.; Paul, B. Showcasing Model Performance across Space and Time Using Single Diagrams. Ocean Model. 2023, 181, 102150. [Google Scholar] [CrossRef]
- Anžel, A.; Heider, D.; Hattab, G. Interactive Polar Diagrams for Model Comparison. Comput. Methods Programs Biomed. 2023, 242, 107843. [Google Scholar] [CrossRef] [PubMed]
- Akshaya, M.; Danumah, J.H.; Saha, S.; Ajin, R.S.; Kuriakose, S.L. Landslide Susceptibility Zonation of the Western Ghats Region in Thiruvananthapuram District (Kerala) Using Geospatial Tools: A Comparison of the AHP and Fuzzy-AHP Methods. Saf. Extrem. Environ. 2021, 3, 181–202. [Google Scholar] [CrossRef]
- Razavi Termeh, S.V.; Kornejady, A.; Pourghasemi, H.R.; Keesstra, S. Flood Susceptibility Mapping Using Novel Ensembles of Adaptive Neuro Fuzzy Inference System and Metaheuristic Algorithms. Sci. Total Environ. 2018, 615, 438–451. [Google Scholar] [CrossRef] [PubMed]
- Vilasan, R.T.; Kapse, V.S. Evaluation of the Prediction Capability of AHP and F-AHP Methods in Flood Susceptibility Mapping of Ernakulam District (India). Nat. Hazards 2022, 112, 1767–1793. [Google Scholar] [CrossRef]
- Winsemius, H.C.; Aerts, J.C.J.H.; van Beek, L.P.H.; Bierkens, M.F.P.; Bouwman, A.; Jongman, B.; Kwadijk, J.C.J.; Ligtvoet, W.; Lucas, P.L.; van Vuuren, D.P.; et al. Global Drivers of Future River Flood Risk. Nat. Clim. Chang. 2015, 6, 381–385. [Google Scholar] [CrossRef]
- Hatcho, N.; Yamasaki, K.; Hirofumi, O.; Kimura, M.; Matsuno, Y. Estimation of the Function of a Paddy Field for Reduction of Flood Risk. In Sustainability of Water Resources; Springer: Cham, Switzerland, 2022; pp. 159–177. [Google Scholar] [CrossRef]
- Taherizadeh, M.; Niknam, A.; Nguyen-Huy, T.; Mezősi, G.; Sarli, R. Flash Flood-Risk Areas Zoning Using Integration of Decision-Making Trial and Evaluation Laboratory, GIS-Based Analytic Network Process and Satellite-Derived Information. Nat. Hazards 2023, 118, 2309–2335. [Google Scholar] [CrossRef]
- Taloor, A.K.; Manhas, D.S.; Kothyari, G.C. Retrieval of Land Surface Temperature, Normalized Difference Moisture Index, Normalized Difference Water Index of the Ravi Basin Using Landsat Data. Appl. Comput. Geosci. 2021, 9, 100051. [Google Scholar] [CrossRef]
- Guha, S.; Govil, H.; Besoya, M. An Investigation on Seasonal Variability between LST and NDWI in an Urban Environment Using Landsat Satellite Data. Geomat. Nat. Hazards Risk 2020, 11, 1319–1345. [Google Scholar] [CrossRef]
- McFeeters, S.K. The Use of the Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features. Int. J. Remote Sens. 1996, 17, 1425–1432. [Google Scholar] [CrossRef]
- Różycka, M.; Migoń, P.; Michniewicz, A. Topographic Wetness Index and Terrain Ruggedness Index in Geomorphic Characterisation of Landslide Terrains, on Examples from the Sudetes, SW Poland. Z. Geomorphol. 2017, 61 (Suppl. S2), 61–80. [Google Scholar] [CrossRef]
- Martinez, A.J.; Meddens, A.J.H.; Kolden, C.A.; Strand, E.K.; Hudak, A.T. Characterizing Persistent Unburned Islands within the Inland Northwest USA. Fire Ecol. 2019, 15, 20. [Google Scholar] [CrossRef]
- Tariq, A.; Yan, J.; Ghaffar, B.; Qin, S.; Mousa, B.G.; Sharifi, A.; Huq, M.E.; Aslam, M. Flash Flood Susceptibility Assessment and Zonation by Integrating Analytic Hierarchy Process and Frequency Ratio Model with Diverse Spatial Data. Water 2022, 14, 3069. [Google Scholar] [CrossRef]
- Lee, J.-Y.; Kim, J.-S. Detecting Areas Vulnerable to Flooding Using Hydrological-Topographic Factors and Logistic Regression. Appl. Sci. 2021, 11, 5652. [Google Scholar] [CrossRef]
- Bashar, A.; Haque, M.I.; Parvin, M.A.; Hossain, M.A. WATER AND VEGETATION COVER CHANGE DETECTION USING MULTISPECTRAL SATELLITE IMAGERY: A CASE STUDY ON JHENAIDAH DISTRICT OF BANGLADESH. Bangladesh J. Multidiscip. Sci. Res. 2023, 7, 22–34. [Google Scholar] [CrossRef]
- Candiago, S.; Remondino, F.; De Giglio, M.; Dubbini, M.; Gattelli, M. Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images. Remote Sens. 2015, 7, 4026–4047. [Google Scholar] [CrossRef]
- Kareem, H.; Attaee, M.; Omran, Z. Evaluation the Soil-Adjusted Vegetation Indices SAVI and MSAVI for Bristol City, United Kingdom Using Landsat 8-OLI through Geospatial Technology. Ecol. Eng. Environ. Technol. 2023, 24, 89–97. [Google Scholar] [CrossRef]
- Sarker, I.H. Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions. SN Comput. Sci. 2021, 2, 420. [Google Scholar] [CrossRef] [PubMed]
- Ahmed, S.F.; Alam, M.S.B.; Hassan, M.; Rozbu, M.R.; Ishtiak, T.; Rafa, N.; Mofijur, M.; Shawkat Ali, A.B.M.; Gandomi, A.H. Deep Learning Modelling Techniques: Current Progress, Applications, Advantages, and Challenges. Artif. Intell. Rev. 2023, 56, 13521–13617. [Google Scholar] [CrossRef]
- Alimoradi, H.; Eskandari, E.; Pourbagian, M.; Shams, M. A parametric study of subcooled flow boiling of Al2O3/water nanofluid using numerical simulation and artificial neural networks. Nanoscale Microscale Thermophys. Eng. 2022, 26, 129–159. [Google Scholar] [CrossRef]
- Lau, S.L.; Lim, J.; Chong, E.K.; Wang, X. Single-pixel image reconstruction based on block compressive sensing and convolutional neural network. Int. J. Hydromechatron. 2023, 6, 258–273. [Google Scholar] [CrossRef]
- Chen, C.-H.; Tanaka, K.; Kotera, M.; Funatsu, K. Comparison and Improvement of the Predictability and Interpretability with Ensemble Learning Models in QSPR Applications. J. Cheminform. 2020, 12, 19. [Google Scholar] [CrossRef] [PubMed]
- Kotu, V.; Deshpande, B. Data Mining Process. In Predictive Analytics and Data Mining; Morgan Kaufmann: Cambridge, MA, USA, 2015; pp. 17–36. [Google Scholar] [CrossRef]
- Hou, W.; Yin, G.; Gu, J.; Ma, N. Estimation of Spring Maize Evapotranspiration in Semi-Arid Regions of Northeast China Using Machine Learning: An Improved SVR Model Based on PSO and RF Algorithms. Water 2023, 15, 1503. [Google Scholar] [CrossRef]
- Gayathri, R.; Rani, S.U.; Čepová, L.; Rajesh, M.; Kalita, K. A Comparative Analysis of Machine Learning Models in Prediction of Mortar Compressive Strength. Processes 2022, 10, 1387. [Google Scholar] [CrossRef]
- Poguluri, S.K.; Bae, Y.H. Enhancing Wave Energy Conversion Efficiency through Supervised Regression Machine Learning Models. J. Mar. Sci. Eng. 2024, 12, 153. [Google Scholar] [CrossRef]
- Xu, J.; Jiang, Y.; Yang, C. Landslide Displacement Prediction during the Sliding Process Using XGBoost, SVR and RNNs. Appl. Sci. 2022, 12, 6056. [Google Scholar] [CrossRef]
- Yue, W.; Ren, C.; Liang, Y.; Liang, J.; Lin, X.; Yin, A.; Wei, Z. Assessment of Wildfire Susceptibility and Wildfire Threats to Ecological Environment and Urban Development Based on GIS and Multi-Source Data: A Case Study of Guilin, China. Remote Sens. 2023, 15, 2659. [Google Scholar] [CrossRef]
Data | Source | Conditioning Factors | Spatial Resolution/Scale |
---|---|---|---|
ASTER GDEM | https://gdemdl.aster.jspacesystems.or.jp/index_en.html | Slope Elevation Aspect Plan curvature Profile curvature Stream power index (SPI) Terrain ruggedness index (TRI) Topographic wetness index (TWI) | 30 m |
Landsat-8 OLI image | https://earthexplorer.usgs.gov | Land use/land cover (LULC) types Geomorphic units Enhanced built-up and bareness index (EBBI) Normalized difference water index (NDWI) Soil adjusted vegetation index (SAVI) | 30 m |
Topographic map | Survey of India (SoI) | Stream density | 1:50,000 |
Soil map | National Bureau of Soil Survey & Land Use Planning (NBSS & LUP) | Soil texture | 1:250,000 |
Conditioning Factors | VIF |
---|---|
Aspect | 2.06 |
EBBI | 5.57 |
Elevation | 2.87 |
Geomorphology | 5.77 |
Land cover | 4.46 |
NDWI | 8.51 |
Plan curvature | 1.28 |
Profile curvature | 1.68 |
SAVI | 8.69 |
Slope | 8.82 |
Soil texture | 1.97 |
SPI | 5.46 |
Stream density | 7.26 |
TRI | 9.37 |
TWI | 9.49 |
Class | FR | Class | FR |
---|---|---|---|
Elevation (m) | NDWI | ||
0–64 | 2.01 | −0.52–−0.37 | 3.98 |
64–209 | 0.00 | −0.37–−0.31 | 0.39 |
209–467 | 0.00 | −0.31–−0.23 | 0.38 |
467–827 | 0.00 | −0.23–−0.06 | 1.38 |
>827 | 0.00 | −0.05–0.28 | 0.46 |
SAVI | EBBI | ||
−0.29–0.18 | 0.72 | −6.17–−3.49 | 6.15 |
0.18–0.41 | 0.94 | −3.49–−2.37 | 0.83 |
0.41–0.53 | 0.32 | −2.37–−1.5 | 0.29 |
0.53–0.64 | 0.32 | −1.5–−0.46 | 0.39 |
0.64–0.89 | 4.32 | >−0.46 | 0.00 |
Slope (°) | TWI | ||
0–5 | 2.10 | <4 | 0.00 |
5–10 | 0.00 | 4–5 | 0.00 |
10–18 | 0.00 | 5–6 | 0.06 |
18–28 | 0.00 | 6–8 | 3.36 |
>28 | 0.00 | >8 | 4.10 |
Stream density (Km/Km2) | TRI | ||
0–1.32 | 3.70 | 0–2 | 2.24 |
1.32–3.24 | 0.00 | 2–4.5 | 0.02 |
3.24–4.82 | 0.00 | 4.5–8.5 | 0.00 |
4.82–6.49 | 0.00 | 8.5–14 | 0.00 |
>6.49 | 0.00 | >14 | 0.00 |
SPI | Profile curvature | ||
−13.81–−7.20 | 2.84 | −0.02–−0.004 | 0.00 |
−7.20–−3.30 | 0.20 | −0.004–−0.001 | 0.33 |
−3.30–−0.70 | 0.09 | −0.001–0.0008 | 1.80 |
−0.70–2.00 | 0.00 | 0.0008–0.003 | 0.53 |
2.00–11.71 | 0.00 | >0.003 | 0.00 |
Soil types Sand Gravelly loam Gravelly clay Loam Clay | 4.14 0.00 0.13 0.63 3.50 | Plan curvature Concave Flat Convex | 0.52 1.22 0.64 |
Geomorphic units Denudational hills Plateau Coastal plain Water body | 0.00 0.00 5.50 3.35 | LULC Forest Grass land Mixed vegetation Plantation Barren land Built-up area Marshy land Fallow land Water body Paddy field | 0.00 0.00 0.00 0.00 0.00 0.00 6.66 3.72 0.56 5.47 |
Aspect Flat N NE E SE S SW W NW | 4.03 0.55 0.43 0.36 0.75 0.85 0.38 0.94 0.85 |
Models | SVR | XGBoost | RNN |
---|---|---|---|
Optimized hyper parameters | Kernel:rbf Degree:10 Tolerance:1 C:3 Epsilon:0.0 Shrinking: True Cache size:350 Best cost = 0.14 | Learning rate: 0.1 Number of estimators: 200 Maximum depth: 5 Minimum child weight: 0.0001 Gamma: 0.001 Subsample: 0.1 Colsample bytree: 1 Best cost = 0.11 | Number of neurons 1: 275 Number of neurons 2: 394 Epoch: 66 Batch size: 959 Learning rate: 0.009 Best cost = 0.091 |
Models | MAE | RMSE | R2 | |||
---|---|---|---|---|---|---|
Train | Test | Train | Test | Train | Test | |
SVR | 0.15 | 0.17 | 0.20 | 0.22 | 0.83 | 0.79 |
XGBoost | 0.13 | 0.14 | 0.18 | 0.21 | 0.86 | 0.80 |
RNN | 0.07 | 0.09 | 0.12 | 0.19 | 0.93 | 0.84 |
SVR-GWO | 0.14 | 0.14 | 0.17 | 0.19 | 0.87 | 0.84 |
XGBoost-GWO | 0.09 | 0.11 | 0.13 | 0.18 | 0.92 | 0.86 |
RNN-GWO | 0.03 | 0.09 | 0.06 | 0.18 | 0.98 | 0.88 |
Models | Very Low (%) | Low (%) | Moderate (%) | High (%) | Very High (%) |
---|---|---|---|---|---|
SVR | 57.35 | 18.33 | 8.93 | 7.85 | 7.54 |
XGBoost | 19.17 | 30.94 | 18.62 | 15.85 | 15.42 |
RNN | 50.35 | 22.25 | 11.41 | 10.17 | 5.82 |
SVR-GWO | 23.17 | 56.65 | 7.32 | 7.58 | 5.28 |
XGBoost-GWO | 19.70 | 20.06 | 20.32 | 20.97 | 18.95 |
RNN-GWO | 54.30 | 20.09 | 9.23 | 8.33 | 8.05 |
Models | AUC | Standard Error | 95% Confidential Interval |
---|---|---|---|
SVR | 0.948 | 0.0225 | 0.891 to 0.980 |
XGBoost | 0.953 | 0.0197 | 0.898 to 0.983 |
RNN | 0.956 | 0.0210 | 0.902 to 0.985 |
SVR-GWO | 0.960 | 0.0173 | 0.908 to 0.987 |
XGBoost-GWO | 0.961 | 0.0183 | 0.909 to 0.988 |
RNN-GWO | 0.968 | 0.0167 | 0.919 to 0.992 |
Models | Chi-Squared | Significance Level (p < 0.0001) |
---|---|---|
SVR | 73.97 | Yes |
XGBoost | 77.82 | Yes |
RNN | 76.95 | Yes |
SVR-GWO | 76.57 | Yes |
XGBoost-GWO | 75.47 | Yes |
RNN-GWO | 77.30 | Yes |
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Mabdeh, A.N.; Ajin, R.S.; Razavi-Termeh, S.V.; Ahmadlou, M.; Al-Fugara, A. Enhancing the Performance of Machine Learning and Deep Learning-Based Flood Susceptibility Models by Integrating Grey Wolf Optimizer (GWO) Algorithm. Remote Sens. 2024, 16, 2595. https://doi.org/10.3390/rs16142595
Mabdeh AN, Ajin RS, Razavi-Termeh SV, Ahmadlou M, Al-Fugara A. Enhancing the Performance of Machine Learning and Deep Learning-Based Flood Susceptibility Models by Integrating Grey Wolf Optimizer (GWO) Algorithm. Remote Sensing. 2024; 16(14):2595. https://doi.org/10.3390/rs16142595
Chicago/Turabian StyleMabdeh, Ali Nouh, Rajendran Shobha Ajin, Seyed Vahid Razavi-Termeh, Mohammad Ahmadlou, and A’kif Al-Fugara. 2024. "Enhancing the Performance of Machine Learning and Deep Learning-Based Flood Susceptibility Models by Integrating Grey Wolf Optimizer (GWO) Algorithm" Remote Sensing 16, no. 14: 2595. https://doi.org/10.3390/rs16142595
APA StyleMabdeh, A. N., Ajin, R. S., Razavi-Termeh, S. V., Ahmadlou, M., & Al-Fugara, A. (2024). Enhancing the Performance of Machine Learning and Deep Learning-Based Flood Susceptibility Models by Integrating Grey Wolf Optimizer (GWO) Algorithm. Remote Sensing, 16(14), 2595. https://doi.org/10.3390/rs16142595