A hybridized model based on neural network and swarm intelligence-grey wolf algorithm for spatial prediction of urban flood-inundation

Darabi, Hamid; Haghighi, Ali Torabi; Rahmati, Omid; Shahrood, Abolfazl Jalali; Rouzbeh, Sajad; Pradhan, Biswajeet; Bui, Dieu Tien

doi:10.1016/j.jhydrol.2021.126854

Cited by 39 publications

(5 citation statements)

References 91 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among them, the rain flood inundation range refers to the area of the ground surface that was inundated when the city flooding disaster occurs. The rain flood inundation depth refers to the height from the surface of the underlying land surface to the surface of the stagnant water at a certain location, that is, the depth of the stagnant water [ 35 ]. The loss degree of rain flood disaster is closely related to the rain flood inundation status, including factors such as rain flood inundation depth, inundation range, inundation duration, and flow velocity during flooding [ 36 ].…”

Section: Methodsmentioning

confidence: 99%

Simulation and Optimization Strategy of Storm Flood Safety Pattern Based on SCS-CN Model

Cai

2022

IJERPH

View full text Add to dashboard Cite

The contradiction between rapid urbanization’s demand for land resources and the ecological environment is increasing, which has led to large-scale hardening of the underlying surface of the city and reduction of land for storage. In addition, construction land occupies rainwater confluence land, resulting in a significant decline in urban stormwater control capabilities. The increasingly frequent flood disasters in recent years have exposed the contradiction between urban construction and stormwater safety that cannot be ignored. Therefore, this article takes the central city of Harbin as the research object, uses ArcGIS for spatial analysis and SCS (Soil Conservation Service) hydrological model simulation to construct the rain and flood safety pattern in the research area, and proposes targeted optimization suggestions and strategies based on the evaluation results to achieve the purpose of coordinating the water ecosystem service function with social and economic development. The research shows that protecting the original stormwater corridor and strengthening the connection between the stormwater control patches can effectively guarantee the connectivity of the stormwater corridor, build the natural stormwater regulation and storage system, and then increase the ability of the city to resist the risk of rainstorm, reduce the disaster caused by urban waterlogging, and achieve the goal of sponge city construction.

show abstract

Section: Methodsmentioning

confidence: 99%

Simulation and Optimization Strategy of Storm Flood Safety Pattern Based on SCS-CN Model

Cai

2022

IJERPH

View full text Add to dashboard Cite

show abstract

“…A spatial flood modelling and mapping serves as an alternative method to anticipate the spatial distribution and the intensity of flooding in regions that lack hydrological and hydraulic data. To evaluate the accuracy of flood inundation, a novel hybridized model, neural network and swarm intelligence-grey wolf algorithm (ANN-SGW) [56] was developed and assessed using statistical evaluation metrics. In addition, the performance of this model was compared to four benchmark machine learning models: random forest (RF), logistic model tree (LMT), classification and regression trees (CART), and J48 decision tree (J48DT).…”

Section: Machine Learning Models On Groundwater Level Crop Yield Pred...mentioning

confidence: 99%

The Impact of Climate Change on Groundwater and Crop Yield in Asia: A Comprehensive Review

Santhosh,

Prabhakaran,

Veeraswamy

et al. 2023

Preprint

View full text Add to dashboard Cite

The effect of climate change plays a significant role on groundwater level variations and crop yield. The change in climate leading to increased temperatures, decreased rainfalls and extreme drought conditions ultimately cause low groundwater level and crop yield. Judicious groundwater management technology viz., water recharge options, need based irrigation, crop selection, must be adapted for further increasing the water table level in soil ecosystem. Additionally, climate change impacts combined with changes in agricultural water use can affect groundwater dynamics. Increased irrigation demand and decreased summer precipitation can lower groundwater levels, impacting crop production. Overall, climate change affects both groundwater resources and crop yield highlighting the need for sustainable water management practices and consideration of soil properties in agricultural modelling. The authors carefully selected relevant research articles addressing the impact of climate change on ground water level in Asian countries. The review highlights the use of machine learning method to ground water level and crop yield modelling. According to the study, machine learning techniques have made significant contributions to predicting groundwater level changes and crop yield with higher accuracies, high performance and less running time.

show abstract

“…It is based on the predatory nature and social hierarchy of grey wolves. Grey wolves live in packs of 5-12 and are divided into four groups: alpha (α, the most dominant), beta (β), delta (δ), and omega (ω, the least dominant; Darabi et al 2021). GWO works by dividing a set of solutions into four groups.…”

Section: Grey Wolf Optimizationmentioning

confidence: 99%

Spatial modeling of flood hazard using machine learning and GIS in Ha Tinh province, Vietnam

Nguyen

2022

Journal of Water and Climate Change

View full text Add to dashboard Cite

The objective of this study was the development of an approach based on machine learning and GIS, namely Adaptive Neuro-Fuzzy Inference System (ANFIS), Gradient-Based Optimizer (GBO), Chaos Game Optimization (CGO), Sine Cosine Algorithm (SCA), Grey Wolf Optimization (GWO), and Differential Evolution (DE) to construct flood susceptibility maps in the Ha Tinh province of Vietnam. The database includes 13 conditioning factors and 1,843 flood locations, which were split by a ratio of 70/30 between those used to build and those used to validate the model, respectively. Various statistical indices, namely root mean square error (RMSE), area under curve (AUC), mean absolute error (MAE), accuracy, and R1 score, were applied to validate the models. The results show that all the proposed models performed well, with an AUC value of more than 0.95. Of the proposed models, ANFIS-GBO was the most accurate, with an AUC value of 0.96. Analysis of the flood susceptibility maps shows that approximately 32–38% of the study area is located in the high and very high flood susceptibility zone. The successful performance of the proposed models over a large-scale area can support local authorities and decision-makers develop policies and strategies to reduce the threats related to flooding in the future.

show abstract

A hybridized model based on neural network and swarm intelligence-grey wolf algorithm for spatial prediction of urban flood-inundation

Cited by 39 publications

References 91 publications

Simulation and Optimization Strategy of Storm Flood Safety Pattern Based on SCS-CN Model

Simulation and Optimization Strategy of Storm Flood Safety Pattern Based on SCS-CN Model

The Impact of Climate Change on Groundwater and Crop Yield in Asia: A Comprehensive Review

Spatial modeling of flood hazard using machine learning and GIS in Ha Tinh province, Vietnam

Contact Info

Product

Resources

About