FLOODWALL: A Real-Time Flash Flood Monitoring and Forecasting System Using IoT

Prakash, Chandra; Barthwal, Anurag; Acharya, Debopam

doi:10.1109/jsen.2022.3223671

Cited by 16 publications

(8 citation statements)

References 45 publications

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“…2). There are many forecasting models developed in the literature as the models in [9]- [12]. Moreover, the author in [26] includes a study comparing different preprocessing techniques and shows how to partition the complex forecasting problem into more minor sub-problems to solve.…”

Section: The Blackwater River In Usamentioning

confidence: 99%

Evolutionary Design of a PSO-Tuned Multigene Symbolic Regression Genetic Programming Model for River Flow Forecasting

Sheta¹,

Abdel-Raouf²,

Fraihat³

et al. 2023

IJACSA

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The earth's population is growing at a rapid rate, while the availability of water resources remains limited. Water is required for various purposes, including drinking, agriculture, industry, recreation, and development. Accurate forecasting of river flows can have a significant economic impact, particularly in agricultural water management and planning during water resource scarcity. Developing precise river flow forecasting models can greatly improve the management of water resources in many countries. In this study, we propose a two-phase model for predicting the flow of the Blackwater river located in the South Central United States. In the first phase, we use Multigene Symbolic Regression Genetic Programming (MG-GP) to develop a mathematical model. In the second phase, Particle Swarm Optimization (PSO) is employed to fine-tune the model parameters. Fine-tuning the MG-GP parameters improves the prediction accuracy of the model. The newly fine-tuned model exhibits 96% and 94% accuracy in training and testing cases, respectively.

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Section: The Blackwater River In Usamentioning

confidence: 99%

Evolutionary Design of a PSO-Tuned Multigene Symbolic Regression Genetic Programming Model for River Flow Forecasting

Sheta¹,

Abdel-Raouf²,

Fraihat³

et al. 2023

IJACSA

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“…The number of true-positive (TP) predictions of the intrusion event divided by the total number of positive predictions is called precision [32]. It indicates the fraction of accident categories that are correct in this work: Precision = true-positive observations positively predicted observations : ð6Þ 6.2.…”

Section: Precisionmentioning

confidence: 99%

FSAS: An IoT-Based Security System for Crop Field Storage

Prakash

Barthwal

Avikal

et al. 2023

International Journal of Distributed Sensor Networks

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Internet of Things abstracts the ability to remotely associate and observe things or objects over the Internet. When it comes to agriculture, this idea has been incorporated to make agriculture-related tasks smart, secure, and automated. Agriculture is vital for economic growth and also for the survival of humans. Farmers living in rural areas of India face a common problem of the theft of equipment like induction motors from small storage houses meant for storing commodities in crop fields. In this study, we present a remote security management framework for monitoring the crop field storage house, known as the farm security alert system (FSAS). FSAS is a small, energy efficient, low cost, and accurate security management system that uses microcontroller-based passive infrared (PIR) sensor and global system for mobile communication (GSM) module to generate an alert to the farm owner if there is an intrusion event at the crop field store. The microcontroller board utilized in the proposed model is the Arduino Uno, and PIR motion sensor is used to recognize the intruder. In addition, FSAS also can be used for monitoring of induction motor by utilizing a similar arrangement of sensors. The sensor signal is transmitted to the cloud whenever the intruder is within the sensing range of the sensor node. Naive Bayes’ prediction model is used to identify the level of encroachment as no (green), mild (yellow), or high (red) threat. The status and the alarms can be received by the farm owners, either on their smartphones as application alerts or as a short message/phone call, at any distance, and independent of whether their cell phones are connected to the Internet.

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“…The evolution state and spatial dynamic structure of the convective system are analyzed, and precipitation intensity is predicted 10,11 . Although these extrapolation-based methods predict the short-term linear advection characteristics of storms, it is difficult to predict the generation and evolution of convective storms, especially when the lead time exceeds 30 minutes 12,13 .…”

Section: Introductionmentioning

confidence: 99%

A new strong convective precipitation forecasting method based on attention mechanism and spatio-temporal reasoning

Zhao,

Wang,

Zhao

et al. 2024

Preprint

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Radar observation variables reﬂect the precipitation amount of strong convective precipitation processes, which accurate forecast is an important difﬁculty in weather forecasting. Current forecasting methods are mostly based on radar echo extrapolation, which has the insufﬁciency of input information and the ineffectiveness of model architecture. This paper presents a Bidirectional Long Short-Term Memory forecasting method for strong convective precipitation based on the attention mechanism and residual neural network (ResNet-Attention-BiLSTM). First, this paper uses ResNet to effectively extract the key information of extreme weather and solves the problem of regression to the mean of the prediction model by learning the residuals of the radar observation data. Second, this paper uses the attention mechanism to adaptively weight the fusion of the features to enhance the extraction of the important features of the precipitation image data. On this basis, this paper presents a novel spatio-temporal reasoning method for radar observations and establishes a precipitation forecasting model, which captures the past and future time-order relationship of the sequence data. Finally, this paper conducts experiments based on the real collected data of a strong convective precipitation process and compares its performance with the existing models, the mean absolute percentage error of this model was reduced by 15.94% (1km), 18.72% (3km), and 14.91% (7km), and the prediction accuracy (R2) was increased by 10.89% (1km), 9.61% (3km), and 9.29% (7km), which proves the state of the art and effectiveness of this forecasting model.

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FLOODWALL: A Real-Time Flash Flood Monitoring and Forecasting System Using IoT

Cited by 16 publications

References 45 publications

Evolutionary Design of a PSO-Tuned Multigene Symbolic Regression Genetic Programming Model for River Flow Forecasting

Evolutionary Design of a PSO-Tuned Multigene Symbolic Regression Genetic Programming Model for River Flow Forecasting

FSAS: An IoT-Based Security System for Crop Field Storage

A new strong convective precipitation forecasting method based on attention mechanism and spatio-temporal reasoning

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