This study evaluates sustainable water management strategies using TOPSIS and Fuzzy TOPSIS (FTOPSIS) to address global water scarcity by comparing rainwater harvesting, water recycling, and desalination across five criteria: water efficiency, cost-effectiveness, environmental impact, social equity, and technological feasibility. The results show Rainwater Harvesting as the most balanced option with a relative closeness value of \({C}_{i}^{+}\)=0.640, excelling in social equity and environmental sustainability. Water Recycling ranks closely behind (\({C}_{i}^{+}\)=0.608), highlighting its adaptability and technological feasibility, while Desalination, though highly efficient, is hindered by lower cost-effectiveness (\({C}_{i}^{+}\)=0.578). By integrating TOPSIS and FTOPSIS, the study addresses uncertainties and subjective criteria, providing a robust multi-dimensional assessment framework for resource management. This methodology aids decision-makers in identifying strategies that align with sustainable development goals and adapt to regional priorities. Future work can expand this framework to include stakeholder engagement and policy factors, enhancing water management strategies for resilient, long-term solutions.

In coastal areas, coconuts are a common crop. Everyone from farmers to lawmakers and businesses would benefit from an accurate forecast of coconut production. Internet of Things (IoT) sensors are strategically positioned to continuously monitor the environment and gather production statistics to obtain accurate agricultural output predictions. To effectively estimate coconut prediction, this study presents an enhanced deep learning classifier called Bi-directional Long Short-Term Memory (BILSTM) with the integrated Lévy Flight and Seagull Optimization Algorithm (LFSOA). LASSO feature selection is applied to eliminate the superfluous characteristics in the yield estimation. To further enhance the coconut yield estimate, the optimal set of hyperparameters for BILSTM is tuned by the LFSOA, which helps to avoid the overfitting issue. For the results, the BILSTM is compared against different classifiers such as Recurrent Neural Network (RNN), Random Forest Classifier (RFC), and LSTM. Similarly, LFSOA-based hyperparameter tuning is contrasted with different optimization algorithms. The outputs show that LFSOA-based hyperparameter tuning in BILSTM achieved accuracy, precision, recall, and f1-score of 98.963%, 99.026%, 99.155%, and 95.758%, respectively, which are higher when compared to existing methods. Similarly, the BILSTM-LFSOA accomplished better results in statistical measures, including the Root Mean Square Error (RMSE) of 0.105, Mean Squared Error (MSE) of 0.011, Mean Absolute Error (MAE) of 0.094, and coefficient of determination (R²) of 0.954, respectively. From the overall analysis, the proposed BILSTM-LFSOA improves coconut yield prediction by achieving better results in all the performance measures when compared with existing models. The results of this study are important to many stakeholders, including but not limited to policymakers, farmers, banks, and insurance companies. As coconuts are an important crop in developing countries, accurate coconut yield forecasting will lead to greater financial and food security in these regions.