An Improved Flood Susceptibility Assessment in Jeddah, Saudi Arabia, Using Advanced Machine Learning Techniques

Ghanim, Abdulnour Ali Jazem; Shaf, Ahmad; Ali, Tariq; Zafar, Maryam; Al-Areeq, Ahmed M.; Alyami, Saleh H.; Irfan, Muhammad; Rahman, Saifur

doi:10.3390/w15142511

Cited by 19 publications

(5 citation statements)

References 29 publications

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“…Thanks to their ability to model complex relationships and patterns in data, ANNs can help predict the occurrence of flash floods based on many different input variables. The use of ANNs allows for a better understanding and forecasting of flood dynamics and, therefore, also for the effective planning of preventive actions [58,59].…”

Section: Multilayer Perceptron (Mlp) Neural Networkmentioning

confidence: 99%

Evaluating the Utility of Selected Machine Learning Models for Predicting Stormwater Levels in Small Streams

Starzec,

Kordana-Obuch

2024

Sustainability

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The consequences of climate change include extreme weather events, such as heavy rainfall. As a result, many places around the world are experiencing an increase in flood risk. The aim of this research was to assess the usefulness of selected machine learning models, including artificial neural networks (ANNs) and eXtreme Gradient Boosting (XGBoost) v2.0.3., for predicting peak stormwater levels in a small stream. The innovation of the research results from the combination of the specificity of small watersheds with machine learning techniques and the use of SHapley Additive exPlanations (SHAP) analysis, which enabled the identification of key factors, such as rainfall depth and meteorological data, significantly affect the accuracy of forecasts. The analysis showed the superiority of ANN models (R2 = 0.803–0.980, RMSE = 1.547–4.596) over XGBoost v2.0.3. (R2 = 0.796–0.951, RMSE = 2.304–4.872) in terms of forecasting effectiveness for the analyzed small stream. In addition, conducting the SHAP analysis allowed for the identification of the most crucial factors influencing forecast accuracy. The key parameters affecting the predictions included rainfall depth, stormwater level, and meteorological data such as air temperature and dew point temperature for the last day. Although the study focused on a specific stream, the methodology can be adapted for other watersheds. The results could significantly contribute to improving real-time flood warning systems, enabling local authorities and emergency management agencies to plan responses to flood threats more accurately and in a timelier manner. Additionally, the use of these models can help protect infrastructure such as roads and bridges by better predicting potential threats and enabling the implementation of appropriate preventive measures. Finally, these results can be used to inform local communities about flood risk and recommended precautions, thereby increasing awareness and preparedness for flash floods.

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Section: Multilayer Perceptron (Mlp) Neural Networkmentioning

confidence: 99%

Evaluating the Utility of Selected Machine Learning Models for Predicting Stormwater Levels in Small Streams

Starzec,

Kordana-Obuch

2024

Sustainability

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“…Similarly, the number of ice days also significantly increases, with daily maximum temperatures of 0.0°C. By 2050, there will be an increase of four to ten hot days (a maximum daily temperature of more than 30°C) in Saudi Arabia ( 25 , 26 ). There will be significant changes, especially at altitudes below 1,000 m. Concerning precipitation, there are currently no concrete statements.…”

Section: Challenges and Developments In The Forestrymentioning

confidence: 99%

Impact of forestry on environment and human health: an evidence-based investigation

Addas

2023

Front. Public Health

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There is an increasing interest in the health effects of the forest. Without active participation in forestry, conflicts between the various stakeholders are foreseeable. The impact of forests on human life is unforgettable, and everyone gets enormous benefits from trees and greenery. COVID-19 has caused many changes in human behavior, which needs much attention. Environmental change's impact is considered a better solution and influences human behavior. Scientists around the globe are conducting research experiments on trees and the effect of forestry on human health, which is increasing in terms of social, ecological, and economic services. Trees provide full support to enhance the quality of life and minimize air pollution. Forests must be noticed to get benefits (e.g., carbon storage, fruits, human health considerations, economic benefits, and biodiversity). This research aims to explain the area of forest and health from the perspective of Saudi Arabian forestry and develop strategic measures for the proactive design of this topic. The research entailed expert interviews with forestry representatives and a quantitative survey of medical students. Our findings show that implementing strategic measures, such as establishing a forest and health, improves health and eliminates air pollution. It shows many other establishment and planning strategies, such as the use of professional visitor monitoring, the development of product innovations, the use of digitalization, and the development of integrated forest management.

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“…LGBM is appropriate for big datasets with multiple instances. The capacity of LGBM to effectively handle massive datasets makes it a powerful tool in a variety of sectors, including online advertising and recommendation systems [55,56]. Yandex's CatBoost is yet another significant version of gradient boosting.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Machine Learning Models for Predicting Rainfall in Urban Metropolitan Cities

Kumar,

Kedam,

Sharma

et al. 2023

Sustainability

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Current research studies offer an investigation of machine learning methods used for forecasting rainfall in urban metropolitan cities. Time series data, distinguished by their temporal complexities, are exploited using a unique data segmentation approach, providing discrete training, validation, and testing sets. Two unique models are created: Model-1, which is based on daily data, and Model-2, which is based on weekly data. A variety of performance criteria are used to rigorously analyze these models. CatBoost, XGBoost, Lasso, Ridge, Linear Regression, and LGBM are among the algorithms under consideration. This research study provides insights into their predictive abilities, revealing significant trends across the training, validation, and testing phases. The results show that ensemble-based algorithms, particularly CatBoost and XGBoost, outperform in both models. CatBoost emerged as the model of choice throughout all assessment stages, including training, validation, and testing. The MAE was 0.00077, the RMSE was 0.0010, the RMSPE was 0.49, and the R2 was 0.99, confirming CatBoost’s unrivaled ability to identify deep temporal intricacies within daily rainfall patterns. Both models had an R2 of 0.99, indicating their remarkable ability to predict weekly rainfall trends. Significant results for XGBoost included an MAE of 0.02 and an RMSE of 0.10, indicating their ability to handle longer time intervals. The predictive performance of Lasso, Ridge, and Linear Regression varies. Scatter plots demonstrate the robustness of CatBoost and XGBoost by demonstrating their capacity to sustain consistently low prediction errors across the dataset. This study emphasizes the potential to transform urban meteorology and planning, improve decision-making through precise rainfall forecasts, and contribute to disaster preparedness measures.

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An Improved Flood Susceptibility Assessment in Jeddah, Saudi Arabia, Using Advanced Machine Learning Techniques

Cited by 19 publications

References 29 publications

Evaluating the Utility of Selected Machine Learning Models for Predicting Stormwater Levels in Small Streams

Evaluating the Utility of Selected Machine Learning Models for Predicting Stormwater Levels in Small Streams

Impact of forestry on environment and human health: an evidence-based investigation

A Comparison of Machine Learning Models for Predicting Rainfall in Urban Metropolitan Cities

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