Evapotranspiration (ET) is the fundamental component of efficient water resource management. Accurate forecasting of ET is essential for efficient water utilization in agriculture. ET forecasting is a complex process due to the requirements of large meteorological variables. The recommended approach is based on the Internet of Things (IoT) and an ensemble-learning-based approach for meteorological data collection and ET forecasting with limited meteorological conditions. IoT is part of the recommended approach to collect real-time data on meteorological variables. The daily maximum temperature (T), mean humidity (Hm), and maximum wind speed (Ws) are used to forecast evapotranspiration (ET). Long short-term memory (LSTM) and ensemble LSTM with bagged and boosted approaches are implemented and evaluated for their accuracy in forecasting ET values using meteorological data from 2001 to 2023. The results demonstrate that the bagged LSTM approach accurately forecasts ET with limited meteorological conditions in Riyadh, Saudi Arabia, with the coefficient of determination (R${}^2$) of 0.94 compared to the boosted LSTM and off-the-shelf LSTM with R${}^2$ of 0.91 and 0.77, respectively. The bagged LSTM model is also more efficient with small values of root mean squared error (RMSE) and mean squared error (MSE) of 0.42 and 0.53 compared to the boosted LSTM and off-the-shelf LSTM models.

Sarcasm is a term used to criticize someone's feelings. Sometimes, humans are not able to identify sarcastic comments, and they typically express the reverse of what they mean when they make snarky remarks. Therefore, the detection of sarcasm within a text automatically is a difficult task. Its significance in enhancing sentiment analysis has also made it an important study field. In previous studies, different approaches to deep learning (DL) and machine learning (ML) have been explored. However, previous approaches mainly depend on the lexical and linguistic aspects. Therefore, these techniques could not perform well in the context of sentiment accuracy. In this research, an efficient approach for detecting sarcasm is proposed. A Bidirectional Encoder Representation from a Transformer (BERT) is proposed to improve the sentiment accuracy in this research. This research also aims to compare the two models of deep learning, the BERT and LSTM (Long Short-Term Memory) models. This comparative analysis aims to provide a detailed overview of the pros and cons of each approach for the detection of sarcasm. The primary aim of this study is to examine the different existing ML and DL approaches for the identification of sarcasm. Apart from this, the comparison of BERT and LSTM contributes to the ongoing debate about whether models work best for sarcasm detection in social media. In this study, sentiment analysis's accuracy is improved by making better decisions, especially when it concerns Twitter interactions.