Novel hybrid intelligence models for flood-susceptibility prediction: Meta optimization of the GMDH and SVR models with the genetic algorithm and harmony search

Dodangeh, Esmaeel; Panahi, Mahdi; Rezaie, Fatemeh; Lee, Saro; Bui, Dieu Tien; Lee, Chang-Wook; Pradhan, Biswajeet

doi:10.1016/j.jhydrol.2020.125423

Cited by 114 publications

(48 citation statements)

References 79 publications

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“…In the current research, a hybrid of SVR and Harmony Search has been innovated to cope with the setback of a possible imprecise prediction system. Harmony search has been confirmed by some researchers to optimize the critical parameters of ε-SVR in engineering problems (Dodangeha 2020)- (Fattahi 2016).…”

Section: ε -Svr Modelmentioning

confidence: 91%

Analysis of permeability based on petrophysical logs: comparison between heuristic numerical and analytical methods

Gholanlo¹

2021

J Petrol Explor Prod Technol

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A series of novel heuristic numerical tools were adopted to tackle the setback of permeability estimation in carbonate reservoirs compared to the classical methods. To that end, a comprehensive data set of petrophysical data including core and log in two wells was situated in Marun Oil Field. Both wells, Well#1 and Well#2, were completed in the Bangestan reservoir, having a broad diversity of carbonate facies. In the light of high Lorenz coefficients, 0.762 and 0.75 in Well#1 and Well#2, respectively, an extensive heterogeneity has been expected in reservoir properties, namely permeability. Despite Well#1, Well#2 was used as a blinded well, which had no influence on model learning and just contributed to assess the validation of the proposed model. An HFU model with the aim of discerning the sophistication of permeability and net porosity interrelation has been developed in the framework of Amaefule’s technique which has been modified by newly introduced classification and clustering conceptions. Eventually, seven distinct pore geometrical units have been distinguished through implementing the hybridized genetic algorithm and k-means algorithm. Furthermore, a K-nearest neighbors (KNN) algorithm has been carried out to divide log data into the flow units and assigns them to the pre-identified FZI values. Besides, a cross between the ε-SVR model, a supervised learning machine, and the Harmony Search algorithm has been used to estimate directly permeability. To select the optimum combination of the involved logging parameters in the ε-SVR model and reduce the dimensionality problem, a principle component analysis (PCA) has been implemented on Well#1 data set. The result of PCA illustrates parameters, such as permeability, the transit time of sonic wave, resistivity of the unflashed zone, neutron porosity, photoelectric index, spectral gamma-ray, and bulk density, which possess the highest correlation coefficient with first derived PC. In line with previous studies, the findings will be compared with empirical methods, Coates–Dumanior, and Timur methods, which both have been launched into these wells. Overall, it is obvious to conclude that the ε -SVR model is undeniably the superior method with the lowest mean square error, nearly 4.91, and the highest R-squared of approximately 0.721. On the contrary, the transform relationship of porosity and permeability has remarkably the worst results in comparison with other models in error (MSE) and accuracy (R2) of 128.73 and 0.116, respectively.

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Section: ε -Svr Modelmentioning

confidence: 91%

Analysis of permeability based on petrophysical logs: comparison between heuristic numerical and analytical methods

Gholanlo¹

2021

J Petrol Explor Prod Technol

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“…For comprehensive landslide modeling, we determined the most important landslide conditioning factors. The following nine conditioning factors were identified via a literature review, and according to the characteristics of the study area [4]: altitude [30], slope degree [36], slope aspect [5], topographic wetness index (TWI) [40], length of slope (LS) [41], valley depth [28], distance from roads [42], distance from rivers [30], and geology [43].…”

Section: Conditioning Factorsmentioning

confidence: 99%

“…This factor was calculated from our DEM using SAGA-GIS software (Figure 3f). According to the literature, most of the landslides occurred near the road network system [40]. Road construction can cause a discontinuity in the soil on a hillslope, making it more prone to landslides.…”

Section: Conditioning Factorsmentioning

confidence: 99%

Landslide Susceptibility Assessment Using an Optimized Group Method of Data Handling Model

Kadirhodjaev¹,

Rezaie

Lee

et al. 2020

IJGI

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Landslides can cause considerable loss of life and damage to property, and are among the most frequent natural hazards worldwide. One of the most fundamental and simple approaches to reduce damage is to prepare a landslide hazard map. Accurate prediction of areas highly prone to future landslides is important for decision-making. In the present study, for the first time, the group method of data handling (GMDH) was used to generate landslide susceptibility map for a specific region in Uzbekistan. First, 210 landslide locations were identified by field survey and then divided randomly into model training and model validation datasets (70% and 30%, respectively). Data on nine conditioning factors, i.e., altitude, slope, aspect, topographic wetness index (TWI), length of slope (LS), valley depth, distance from roads, distance from rivers, and geology, were collected. Finally, the maps were validated using the testing dataset and receiver operating characteristic (ROC) curve analysis. The findings showed that the “optimized” GMDH model (i.e., using the gray wolf optimizer [GWO]) performed better than the standalone GMDH model, during both the training and testing phase. The accuracy of the GMDH–GWO model in the training and testing phases was 94% and 90%, compared to 85% and 82%, respectively, for the standard GMDH model. According to the GMDH–GWO model, the study area included very low, low, moderate, high, and very high landslide susceptibility areas, with proportions of 14.89%, 10.57%, 15.00%, 35.12%, and 24.43%, respectively.

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“…Both proposed methods show relative improvements in benchmark problems and real-world case studies. Yet evolutionary optimization algorithms significantly contribute to flood control and susceptibility assessment [13,29] and uncertainty assessment methods [4,5].…”

Section: Introductionmentioning

confidence: 99%

A hybrid constrained coral reefs optimization algorithm with machine learning for optimizing multi-reservoir systems operation

Emami

Nazif

Mousavi

et al. 2021

Journal of Environmental Management

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The continuous growing demand for water, prolonged periods of drought, and climatic uncertainties attributed mainly to climate change mean surface water reservoirs more than ever need to be managed efficiently. Several optimization algorithms have been developed to optimize multi-reservoir systems operation, mostly during severe dry/wet seasons, to mitigate extreme-events consequences. Yet, convergence speed, presence of local optimums, and calculation-cost efficiency are challenging while looking for the global optimum. In this paper, the problem of finding an efficient optimal operation policy in multi-reservoir systems is discussed. The complexity of the long-term operating rules and the reservoirs' upstream and downstream joint-demands projected in recursive constraints make this problem formidable. The original Coral Reefs Optimization (CRO) algorithm, which is a meta-heuristic evolutionary algorithm, and two modified versions have been used to solve this problem. Proposed modifications reduce the calculation cost by narrowing the search space called a constrained-CCRO and adjusting reproduction operators with a reinforcement learning approach, namely the Q-Learning method (i.e., the CCRO-QL algorithm). The modified versions search for the optimum solution in the feasible region instead of the entire problem domain. The models' performance has been evaluated by solving five mathematical benchmark problems and a well-known continuous four-reservoir system (CFr) problem. Obtained results have been compared with those in the literature and the global optimum, which Linear Programming (LP) achieves. The CCRO-QL is shown to be very calculation-cost-effective in locating the global optimum or near-optimal solutions and efficient in terms of convergence, accuracy, and robustness.

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Novel hybrid intelligence models for flood-susceptibility prediction: Meta optimization of the GMDH and SVR models with the genetic algorithm and harmony search

Cited by 114 publications

References 79 publications

Analysis of permeability based on petrophysical logs: comparison between heuristic numerical and analytical methods

Analysis of permeability based on petrophysical logs: comparison between heuristic numerical and analytical methods

Landslide Susceptibility Assessment Using an Optimized Group Method of Data Handling Model

A hybrid constrained coral reefs optimization algorithm with machine learning for optimizing multi-reservoir systems operation

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