Ali Najah Ahmed

TL;DR: A Neuro-Fuzzy Inference System (WDT-ANFIS) based augmented wavelet de-noising technique has been recommended that depends on historical data of the water quality parameter and exhibited a significant improvement in predicting accuracy for all theWater quality parameters and outperformed all the recommended models.

TL;DR: It was found that the PM2.5 concentrations showed a high reduction during the 2020 Malaysia Movement Control Order, but the reduction did not solely depend on MCO, thus the researchers suggest a further study considering the influencing factors that need to be adhered to in the future.

TL;DR: The results show the integrated AI with GWO outperform the standard AI methods and can make better forecasting during training and testing phases for the monthly inflow in all input cases, revealing the superiority of GWO meta-heuristic algorithm in improving the accuracy of the standardAI in forecasting the monthly Inflow.

TL;DR: The proposed Xgboost model outperformed both the Artificial Neural Network and Support Vector Regression models for all different input combinations and serves as a great benchmark for future groundwater levels prediction using Xg Boost algorithm.

TL;DR: In this article, a comparative study was conducted focusing on developing and comparing several Machine Learning (ML) models, evaluating different scenarios and time horizon, and forecasting rainfall using two types of methods.