Building an IoT temperature and humidity forecasting model based on long short-term memory (LSTM) with improved whale optimization algorithm
In particular, predicting the temperature and humidity information plays a crucial role in plantation, estimating rainfalls and climate change, and predicting air quality via specified geographical regions. The temperature and humidity forecasting information is occasionally presented with low accur...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Memories - Materials, Devices, Circuits and Systems |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2773064623000634 |
| _version_ | 1797398405406785536 |
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| author | Mustafa Wassef Hasan |
| author_facet | Mustafa Wassef Hasan |
| author_sort | Mustafa Wassef Hasan |
| collection | DOAJ |
| description | In particular, predicting the temperature and humidity information plays a crucial role in plantation, estimating rainfalls and climate change, and predicting air quality via specified geographical regions. The temperature and humidity forecasting information is occasionally presented with low accuracy due to uncertain techniques and vast methods that employ different sensors and models. For this reason, this work proposes an Internet of Things (IoT) temperature and humidity forecasting model based on an improved whale optimization algorithm with long short-term memory (IWOA-LSTM) technique. To increase the convergence speed processing time and overcome the local optimization problem, the IWOA is introduced. The number of hidden layers, learning rate momentum, and weight decay of the LSTM optimized using the IWOA. The actual temperature and humidity data are collected using DHT11 and ESP8266 NodeMCU practical model and processed using the ThingSpeak platform. The processing data stage depends on filling the missing data gaps using the rolling average technique (RAT). The performance evaluation of the proposed IWOA-LSTM forecasting model is assessed using some statistical functions, namely known as mean square error, mean absolute error, root mean square error, and mean absolute percentage error. The IWOA-LSTM techniques were also assessed using throughput, latency, and power consumption. The developed IWOA-LSTM model shows high accuracy, leading to better forecasting information than other forecasting models. |
| first_indexed | 2024-03-09T01:25:11Z |
| format | Article |
| id | doaj.art-34bd1fdcf3dd4c92b882eea4dc67b36c |
| institution | Directory Open Access Journal |
| issn | 2773-0646 |
| language | English |
| last_indexed | 2024-03-09T01:25:11Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Memories - Materials, Devices, Circuits and Systems |
| spelling | doaj.art-34bd1fdcf3dd4c92b882eea4dc67b36c2023-12-10T06:19:26ZengElsevierMemories - Materials, Devices, Circuits and Systems2773-06462023-12-016100086Building an IoT temperature and humidity forecasting model based on long short-term memory (LSTM) with improved whale optimization algorithmMustafa Wassef Hasan0Department of Electrical Engineering, University of Technology- Iraq, Baghdad, IraqIn particular, predicting the temperature and humidity information plays a crucial role in plantation, estimating rainfalls and climate change, and predicting air quality via specified geographical regions. The temperature and humidity forecasting information is occasionally presented with low accuracy due to uncertain techniques and vast methods that employ different sensors and models. For this reason, this work proposes an Internet of Things (IoT) temperature and humidity forecasting model based on an improved whale optimization algorithm with long short-term memory (IWOA-LSTM) technique. To increase the convergence speed processing time and overcome the local optimization problem, the IWOA is introduced. The number of hidden layers, learning rate momentum, and weight decay of the LSTM optimized using the IWOA. The actual temperature and humidity data are collected using DHT11 and ESP8266 NodeMCU practical model and processed using the ThingSpeak platform. The processing data stage depends on filling the missing data gaps using the rolling average technique (RAT). The performance evaluation of the proposed IWOA-LSTM forecasting model is assessed using some statistical functions, namely known as mean square error, mean absolute error, root mean square error, and mean absolute percentage error. The IWOA-LSTM techniques were also assessed using throughput, latency, and power consumption. The developed IWOA-LSTM model shows high accuracy, leading to better forecasting information than other forecasting models.http://www.sciencedirect.com/science/article/pii/S2773064623000634Internet of things (IoT)Temperature and humidity forecastingLong short-term memory (LSTM)Improved whale optimization algorithm (IWOA)ThingSpeak platform |
| spellingShingle | Mustafa Wassef Hasan Building an IoT temperature and humidity forecasting model based on long short-term memory (LSTM) with improved whale optimization algorithm Memories - Materials, Devices, Circuits and Systems Internet of things (IoT) Temperature and humidity forecasting Long short-term memory (LSTM) Improved whale optimization algorithm (IWOA) ThingSpeak platform |
| title | Building an IoT temperature and humidity forecasting model based on long short-term memory (LSTM) with improved whale optimization algorithm |
| title_full | Building an IoT temperature and humidity forecasting model based on long short-term memory (LSTM) with improved whale optimization algorithm |
| title_fullStr | Building an IoT temperature and humidity forecasting model based on long short-term memory (LSTM) with improved whale optimization algorithm |
| title_full_unstemmed | Building an IoT temperature and humidity forecasting model based on long short-term memory (LSTM) with improved whale optimization algorithm |
| title_short | Building an IoT temperature and humidity forecasting model based on long short-term memory (LSTM) with improved whale optimization algorithm |
| title_sort | building an iot temperature and humidity forecasting model based on long short term memory lstm with improved whale optimization algorithm |
| topic | Internet of things (IoT) Temperature and humidity forecasting Long short-term memory (LSTM) Improved whale optimization algorithm (IWOA) ThingSpeak platform |
| url | http://www.sciencedirect.com/science/article/pii/S2773064623000634 |
| work_keys_str_mv | AT mustafawassefhasan buildinganiottemperatureandhumidityforecastingmodelbasedonlongshorttermmemorylstmwithimprovedwhaleoptimizationalgorithm |