A novel methodology to predict monthly municipal water demand based on weather variables scenario

This study provides a novel methodology to predict monthly water demand based on several weather variables scenarios by using combined techniques including discrete wavelet transform, principal component analysis, and particle swarm optimisation. To our knowledge, the adopted approach is the first technique to be proposed and applied in the water demand prediction. Compared to traditional methods, the developed methodology is superior in terms of predictive accuracy and runtime. Water consumption coupled with weather variables of the Melbourne City, from 2006 to 2015, were obtained from the South East Water retail company. The results showed that using data pre-processing techniques can significantly improve the quality of data and to select the best model input scenario. Additionally, it was noticed that the particle swarm optimisation algorithm accurately predicts the constants of the suggested model. Furthermore, the results confirmed that the proposed methodology accurately estimated the monthly data of municipal water demand based on a range of statistical criteria

Drought Forecasting: A Review and Assessment of the Hybrid Techniques and Data Pre-processing

Drought is a prolonged period of low precipitation that negatively impacts agriculture, animals, and people. Over the last decades, gradual changes in drought indices have been observed. Therefore, understanding and forecasting drought is essential to avoid its economic impacts and appropriate water resource planning and management. This paper presents a recent literature review, including a brief description of data pre-processing, data-driven modelling strategies (i.e., univariate or multivariate), machine learning algorithms (i.e., advantages and disadvantages), hybrid models, and performance metrics. Combining various prediction methods to create efficient hybrid models has become the most popular use in recent years. Accordingly, hybrid models have been increasingly used for predicting drought. As such, these models will be extensively reviewed, including preprocessing-based hybrid models, parameter optimisation-based hybrid models, and hybridisation of components combination-based with preprocessing-based hybrid models. In addition, using statistical criteria, such as RMSE, MAE, NSE, MPE, SI, BIC, AIC, and AAD, is essential to evaluate the performance of the models

Forecasting of Air Maximum Temperature on Monthly Basis Using Singular Spectrum Analysis and Linear Autoregressive Model

In this research, the singular spectrum analysis technique is combined with a linear autoregressive model for the purpose of prediction and forecasting of monthly maximum air temperature. The temperature time series is decomposed into three components and the trend component is subjected for modelling. The performance of modelling for both prediction and forecasting is evaluated via various model fitness function. The results show that the current method presents an excellent performance in expecting the maximum air temperature in future based on previous recordings

Prediction and Forecasting of Maximum Weather Temperature Using a Linear Autoregressive Model

This paper investigates the autoregressive (AR) model performance in prediction and forecasting the monthly maximum temperature. The temperature recordings are collected over 12 years (i.e., 144 monthly readings). All the data are stationaries, which is converted to be stationary, via obtaining the normal logarithm values. The recordings are then divided into 70% training and 30% testing sample. The training sample is used for determining the structure of the AR model while the testing sample is used for validating the obtained model in forecasting performance. A wide range of model order is selected and the most suitable order is selected in terms of the highest modelling accuracy. The study shows that the monthly maximum temperature can accurately be predicted and forecasted using the AR model