Application of GIS and Machine Learning to Predict Flood Areas in Nigeria

Ighile, Eseosa Halima; Shirakawa, Hiroaki; Tanikawa, Hiroki

doi:10.3390/su14095039

Cited by 27 publications

(28 citation statements)

References 84 publications

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“…However, their study lacks extension to other environments and classification of the possible hazard during wet periods or floods. In the study of Ighile et al in 2022, they predicted the flood susceptible areas in Nigeria based on historical flood records from 1985-2020 and some factors [43]. They used artificial neural network (ANN) and logistic regression (LR) models to develop a flood susceptibility map and evaluated the link between flood events and fifteen explanatory variables.…”

Section: Discussion On K-mean Clustering Of the Hybrid Methodsmentioning

confidence: 99%

Application of topological data analysis to flood disaster management in Nigeria

Ohanuba

Ismail

Ali

2022

Environmental Engineering Research

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The importance of this research to the literature lies in the ability to develop a hybrid method of Topological Data Analysis and Unsupervised Machine Learning (TDA-uML) for flood detection. The working method in TDA-uML entails collection and loading of datasets, feature representation, data preprocessing (i.e., training, testing, computation, and validation), and data classification. Three properties make TDA distinct from traditional methods: coordinate-invariance, deformative-invariance, and compressed-representative. Formerly utilized hydrologic, hydraulic, and statistical models for flood control were frequently erroneous in their forecasts, lacked the use of hybrid models, and were not validated. The main research objective is to develop a hybrid method for predicting floods. The motivation is to fill research gaps by using TDA-uML methods for persistent homology (PH) and synthetic k-means clustering. The results will be used to compare and categorize the features that are produced. Seven states were selected based on Nigeria's flood history and affected population. The 7 states are located within the 8 hydrological areas of Nigeria. The efficiency of the resultant validity was 91%. The findings contributed to the development of a model for flood prediction and management; topological features were extracted from the data to predict and categorize the risk.

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Section: Discussion On K-mean Clustering Of the Hybrid Methodsmentioning

confidence: 99%

Application of topological data analysis to flood disaster management in Nigeria

Ohanuba

Ismail

Ali

2022

Environmental Engineering Research

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“…As a results of climate change, several climate models predict increase in flood occurrences in Nigeria e.g. [53]- [54]- [50]- [55]. As highlighted by Kundzewicz et al [2] changes in precipitation and temperature regimes as a result of changing climate are responsible flood occurrences.…”

Section: How Climate Change Exacerbate Flood In Nigeria?mentioning

confidence: 99%

Flood Policy and Governance: A Pathway for Policy Coherence in Nigeria

Danhassan¹,

Ahmed²,

Zangina³

et al. 2022

Preprint

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In recent years, Nigeria is witnessing increasing frequency of flood occurrence with devastating impact translating into significant loss of lives (in Nigeria, over 300 people died in September 2022) and properties. Addressing flood disaster requires holistic approach from policy and governance perspectives, integration of policies and programs and synergies between institutions. Using synergies and eliminating trade-offs, flood governance and policy coherence integrate all relevant policy fields and institutions to achieve common policy outcomes. The objective of this study is to examine and understand how flood governance and policy coherence are approached, as well as institutional design and implementation for coherence in Nigeria. The findings revealed that there is no single flood policy in Nigeria. Due to this, there is no focus and no defined objectives for flood governance, prevention, control, and management, and no imperative for the government to seek both short-term and long-term flood solutions. There is no synergy and coordination among institutions for flood governance in the country. Since the country established the federal ministry of environment in 1999, the environment, floods, and climate-related hazards were given less priority. State and local governments handle most flood disasters and emergencies. Federal assistance is provided, however, when flood disasters exceed the capabilities of local and state governments. This study recommends that across the country, flood policy needs to be designed, formulated, and implemented while assigning governance responsibility and decentralizing policy to state and local governments.

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“…The uncertainties of coupled flood forecasting approach based on the GXM model and WRF forecasts remain unestablished. The dataset used in research considered old data, dating back to 1990 to 2002 [35] Proving that machine learning techniques can accurately map and predict flood-prone areas and can be used to develop flood mitigation policies and plans…”

Section: Weather Research and Forecast Model Grid-xinanjiang Modelmentioning

confidence: 99%

A Smart Framework for Managing Natural Disasters Based on the IoT and ML

et al. 2023

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Natural disasters greatly threaten our lives in addition to adversely affecting all activities. Unfortunately, most solutions currently used in flood management are suffering from many drawbacks related to latency and accuracy. Moreover, the previous solutions consider that the whole city has the same level of vulnerability to damage, while each area in the city may have different topologies and conditions. This study presents a new framework that collects data in real-time about bad weather, which may cause floods, where the framework has a proposed classification algorithm to process sensed data to determine the level of danger in each area of the city. In case of a threat, the framework will send early alerts to users and rescue teams. The framework depends on the Internet of Things (IoT) and fog computing coupled with multiple models of machine learning (Rain Forest, Decision Tree, K-Nearest Neighbor, Support Vector Machine, Logistic Regression, and Deep Learning) to enhance performance and reliability. In addition, the research suggests some assistant services. To prove the efficiency of the framework, we applied the proposed algorithm to real data for the city of Jeddah, Saudi Arabia, for the years 2009 to 2013 and for the years 2018 to 2022. Then, we depended on standard metrics (accuracy, precision, recall, F1-score, and ROC curve). The Rain Forest and Decision Tree achieved the highest accuracy, exceeding 99 percent, followed by the K-Nearest Neighbor. The framework will provide flood detection systems that can predict floods early, send a multi-level warning, and reduce financial, human, and infrastructural damage.

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Application of GIS and Machine Learning to Predict Flood Areas in Nigeria

Cited by 27 publications

References 84 publications

Application of topological data analysis to flood disaster management in Nigeria

Application of topological data analysis to flood disaster management in Nigeria

Flood Policy and Governance: A Pathway for Policy Coherence in Nigeria

A Smart Framework for Managing Natural Disasters Based on the IoT and ML

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