RETRACTED: An evaluation of machine learning and deep learning models for drought prediction using weather data

Jiang, Weiwei; Luo, Jiayun

doi:10.3233/jifs-212748

Cited by 13 publications

(6 citation statements)

References 25 publications

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“…The second research direction entails the implementation of distributed learning techniques [25], which would be well-suited for real-world systems, enabling scalable and efficient predictions. The third research direction revolves around the joint forecasting of weather and energy consumption [26], a potentially more effective approach that considers the interplay…”

Section: Discussionmentioning

confidence: 99%

Energy consumption forecasting with deep learning

2024

J. Phys.: Conf. Ser.

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This research endeavors to create an advanced machine learning model designed for the prediction of household electricity consumption. It leverages a multidimensional time-series dataset encompassing energy consumption profiles, customer characteristics, and meteorological information. A comprehensive exploration of diverse deep learning architectures is conducted, encompassing variations of recurrent neural networks (RNNs), temporal convolutional networks (TCNs), and traditional autoregressive moving average models (ARIMA) for reference purposes. The empirical findings underscore the substantial enhancement in forecasting accuracy attributed to the inclusion of meteorological data, with the most favorable outcomes being attained through the application of time-series convolutional networks. Additionally, an in-depth investigation is conducted into the impact of input duration and prediction steps on model performance, emphasizing the pivotal role of selecting an optimal duration and number of steps to augment predictive precision. In summation, this investigation underscores the latent potential of deep learning in the domain of electricity consumption forecasting, presenting pragmatic methodologies and recommendations for household electricity consumption prediction.

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Section: Discussionmentioning

confidence: 99%

Energy consumption forecasting with deep learning

2024

J. Phys.: Conf. Ser.

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“…Drought and weather monitoring of 18 meterological parameters were constantly observed for three months and utilized for proper early flood prediction. Extreme gradient boost algorithm and SVM algorithm were applied over the deep learning architecture for the proper prediction of flood [26]. Unmanned aerial vehicle based captured images are applied for flood detection by Munawar et al, [27].…”

Section: Related Workmentioning

confidence: 99%

IoT-Enabled Flood Monitoring System for Enhanced Dam Surveillance and Risk Mitigation

R.S,

et al. 2024

Int. Res. J. multidiscip. Technovation

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According to the Indian scenario, the majority of reservoirs for holding water are operated independently, which is problematic when there are crises (abnormal inflow, cloudy conditions), which causes the surrounding communities and agricultural areas to be submerged those aquifers. Due to the vast geographic region and depth, it is challenging to manually measure the essential reservoir life metrics. Therefore, this research work suggests a cutting-edge system of reservoir management that includes sensors that are appropriate for measuring variables such as pressure, water level, outflow velocity, inflow velocity, tilt, vibration, etc. The Arduino Uno integrates all of the sensors, and Microsoft Power BI receives the data in real time, where each parameter is shown in an appropriate format for visualization. In case of an emergency water level rise, the alarm is set off. The procedure begins with the collection of data from sensors and concludes with the presentation of that data on a dashboard in a control room situated in a distant place that links to a website where the relevant information can be seen by visitors.

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“…However, it is not certain which model performs better. A detailed study was carried out by using a real-world open Kaggle publicly available dataset [18]. Three months of 18 meteorological indicators were used to predict drought levels which included evaluating around 16 ML and 16 DL models.…”

Section: Applications Of Machine Learning Techniques In the Environme...mentioning

confidence: 99%

Flood Monitoring and Early Warning Systems – An IoT Based Perspective

Ahmed,

Mohammed Siddique,

Mohammad Shahid Husain

2023

EAI Endorsed Trans IoT

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One of the most frequently occurring calamities around the world is the flood. For flood prone areas or countries, an essential part of their governance is flood management. The necessity to continuously review and analyse the adverse or ambient environmental conditions in real-time demands developing a monitoring system so that floods could be detected beforehand. This paper discusses different Internet of Things (IoT) based techniques and applications implemented for efficient flood monitoring and an early warning system and it is observed that in future, the combination of IoT and Synthetic Aperture Radar (SAR) data may be helpful to develop robust and secure flood monitoring and early warning system that provides effective and efficient mapping during natural disasters. The emerging technology in the discipline of computing is IoT, an embedded system that enables devices to gather real-time data to further store it in the computational devices using Wireless Sensor Networks (WSN) for further processing. The IoT based projects that can help collect data from sensors are an added advantage for researchers to explore in providing better services to people. These systems can be integrated with cloud computing and analyzing platforms. Researchers recently have focussed on mathematical modeling based flood prediction schemes rather than physical parametric based flood prediction. The new methodologies explore the algorithmic approaches. There have been many systems proposed based on analog technology to web-based and now using mobile applications. Further, alert systems have been designed using web-based applications that gather processed data by Arduino Uno Microcontroller which is received from ultrasonic and rain sensors. Additionally, the machine learning based embedded systems can measure different atmospheric conditions such as temperature, moisture, and rains to forecast floods by analyzing varying trends in climatic changes.

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RETRACTED: An evaluation of machine learning and deep learning models for drought prediction using weather data

Cited by 13 publications

References 25 publications

Energy consumption forecasting with deep learning

Energy consumption forecasting with deep learning

IoT-Enabled Flood Monitoring System for Enhanced Dam Surveillance and Risk Mitigation

Flood Monitoring and Early Warning Systems – An IoT Based Perspective

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