Advance flood detection and notification system based on sensor technology and machine learning algorithm

Khalaf, Mohammed; Hussain, Abir Jaafar; Al‐Jumeily, Dhiya; Fergus, Paul; Idowu, Ibrahim Olatunji

doi:10.1109/iwssip.2015.7314188

Cited by 22 publications

(6 citation statements)

References 4 publications

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“…This can be accomplished by combining the outcomes of the ML models with weights obtained, for instance, from the log-log scores. Another alternative that is becoming popular is the construction of hybrid models as a combination of ML algorithms for more accurate and efficient models [24,35,36]. Moreover, as stated by Solomatine and Xue [36], inaccuracies in forecasting floods are mainly due to data-related problems.…”

Section: Discussionmentioning

confidence: 99%

“…Bayesian algorithms based on Bayes' theorem on conditional probability (e.g., naive Bayes, Bayesian network, Gaussian naïve Bayes, etc.) [18,31,35]. v.…”

Section: Machine Learning (Ml) Methods For Classification Of Flood Alert Levelsmentioning

confidence: 99%

“…Different ML strategies for flood forecasting are implemented, generating either quantitative or qualitative runoff forecasts [18,[28][29][30][31][32][33][34][35][36][37][38]. Qualitative forecasting consists of classifying floods into distinct categories or river states according to their severity (i.e., runoff magnitude), and use this as a base for flood class prediction [30,37,39].…”

Section: Introductionmentioning

confidence: 99%

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Flood Early Warning Systems Using Machine Learning Techniques: The Case of the Tomebamba Catchment at the Southern Andes of Ecuador

et al. 2021

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Worldwide, machine learning (ML) is increasingly being used for developing flood early warning systems (FEWSs). However, previous studies have not focused on establishing a methodology for determining the most efficient ML technique. We assessed FEWSs with three river states, No-alert, Pre-alert and Alert for flooding, for lead times between 1 to 12 h using the most common ML techniques, such as multi-layer perceptron (MLP), logistic regression (LR), K-nearest neighbors (KNN), naive Bayes (NB), and random forest (RF). The Tomebamba catchment in the tropical Andes of Ecuador was selected as a case study. For all lead times, MLP models achieve the highest performance followed by LR, with f1-macro (log-loss) scores of 0.82 (0.09) and 0.46 (0.20) for the 1 h and 12 h cases, respectively. The ranking was highly variable for the remaining ML techniques. According to the g-mean, LR models correctly forecast and show more stability at all states, while the MLP models perform better in the Pre-alert and Alert states. The proposed methodology for selecting the optimal ML technique for a FEWS can be extrapolated to other case studies. Future efforts are recommended to enhance the input data representation and develop communication applications to boost the awareness of society of floods.

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Section: Discussionmentioning

confidence: 99%

“…Bayesian algorithms based on Bayes' theorem on conditional probability (e.g., naive Bayes, Bayesian network, Gaussian naïve Bayes, etc.) [18,31,35]. v.…”

Section: Machine Learning (Ml) Methods For Classification Of Flood Alert Levelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Flood Early Warning Systems Using Machine Learning Techniques: The Case of the Tomebamba Catchment at the Southern Andes of Ecuador

et al. 2021

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“…In this work, the roads were monitored by sensors to send out alerts to drivers in case the flooding is detected. As for flood detection, Khalaf et al [23] employed sensors and ML methods in their work. This study merely used a sensor network to assess the water level and sent an SMS alert in case flooding is detected.…”

Section: Literature Reviewmentioning

confidence: 99%

An Intelligent Early Flood Forecasting and Prediction Leveraging Machine and Deep Learning Algorithms with Advanced Alert System

et al. 2023

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Flood disasters are a natural occurrence around the world, resulting in numerous casualties. It is vital to develop an accurate flood forecasting and prediction model in order to curb damages and limit the number of victims. Water resource allocation, management, planning, flood warning and forecasting, and flood damage mitigation all benefit from rain forecasting. Prior to recent decades’ worth of research, this domain demonstrated to be promising prospects in time series prediction tasks. Therefore, the main aim of this study is to build a forecasting model based on the exponential smoothing-long-short term memory (ES-LSTM) structure and recurrent neural networks (RNNs) for predicting hourly precipitation seasons; and classify the precipitation using an artificial neural network (ANN) model and decision tree (DT) algorithm. We employ the dataset from the Australian commonwealth office of meteorology named Historical Daily Weather dataset to test the effectiveness of the proposed model. The findings showed that the ES-LSTM and RNN had achieved 3.17 and 6.42 in terms of mean absolute percentage error (MAPE), respectively. Meanwhile, the ANN and DT models obtained a prediction accuracy rate of 96.65% and 84.0%, respectively. Finally, the outcomes revealed that ES-LSTM and ANN had achieved the best results compared to other models.

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“…The ML methods are applied in two main categories: (1) supervised method by predicting some output variable associated with each input sample and (2) unsupervised method that does not need any sample data and provides a prediction by considering input feature dataset. The ML methods are widely deployed in many applications based on different sensors and datasets such as quasidistributed smart textile [37], simultaneous assessment of magnetic field intensity [38], paddy rice seed classification [39,40], anime film visualization [41], eggplant seed classification [42], regional digital construction [43], flood mapping [44], and flood prevention [45]. Although the ML methods have provided promising results in many abovementioned applications, they suffer from lower coverage and generalization [1].…”

Section: Introductionmentioning

confidence: 99%

Fusion of the Multisource Datasets for Flood Extent Mapping Based on Ensemble Convolutional Neural Network (CNN) Model

Seydi

Saeidi²,

Kalantar

et al. 2022

Journal of Sensors

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Floods, as one of the natural hazards, can affect the environment, damage the infrastructures, and threaten human lives. Due to climate change and anthropogenic activities, floods occur in high frequency all over the world. Therefore, mapping of the flood areas is of prime importance in disaster management. This research presents a novel framework for flood area mapping based on heterogeneous remote sensing (RS) datasets. The proposed framework fuses the synthetic aperture radar (SAR), optical, and altimetry datasets for mapping flood areas, and it is applied in three main steps: (1) preprocessing, (2) deep feature extraction based on multiscale residual kernel convolution and convolution neural network’s (CNN) parameter optimization by fusing the datasets, and (3) flood detection based on the trained model. This research exploits two large-scale area datasets for mapping the flooded areas in Golestan and Khuzestan provinces, Iran. The results show that the proposed methodology has a high performance in flood area detection. The visual and numerical analyses verify the effectiveness and ability of the proposed method to detect the flood areas with an overall accuracy (OA) higher than 98% in both study areas. Finally, the efficiency of the designed architecture was verified by hybrid-CNN and 3D-CNN methods.

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Advance flood detection and notification system based on sensor technology and machine learning algorithm

Cited by 22 publications

References 4 publications

Flood Early Warning Systems Using Machine Learning Techniques: The Case of the Tomebamba Catchment at the Southern Andes of Ecuador

Flood Early Warning Systems Using Machine Learning Techniques: The Case of the Tomebamba Catchment at the Southern Andes of Ecuador

An Intelligent Early Flood Forecasting and Prediction Leveraging Machine and Deep Learning Algorithms with Advanced Alert System

Fusion of the Multisource Datasets for Flood Extent Mapping Based on Ensemble Convolutional Neural Network (CNN) Model

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