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

A Novel Methodology for Prediction Urban Water Demand by Wavelet Denoising and Adaptive Neuro-Fuzzy Inference System Approach

Accurate and reliable urban water demand prediction is imperative for providing the basis to design, operate, and manage water system, especially under the scarcity of the natural water resources. A new methodology combining discrete wavelet transform (DWT) with an adaptive neuro-fuzzy inference system (ANFIS) is proposed to predict monthly urban water demand based on several intervals of historical water consumption. This ANFIS model is evaluated against a hybrid crow search algorithm and artificial neural network (CSA-ANN), since these methods have been successfully used recently to tackle a range of engineering optimization problems. The study outcomes reveal that 1) data preprocessing is essential for denoising raw time series and choosing the model inputs to render the highest model performance; 2) both methodologies, ANFIS and CSA-ANN, are statistically equivalent and capable of accurately predicting monthly urban water demand with high accuracy based on several statistical metric measures such as coefficient of efficiency (0.974, 0.971, respectively). This study could help policymakers to manage extensions of urban water system in response to the increasing demand with low risk related to a decision

Assessing the Benefits of Nature-Inspired Algorithms for the Parameterisation of ANN in the Prediction of Water Demand

Accurate forecasting techniques for a stochastic pattern of water demand are essential for any city that faces high variability in climate factors and a shortage of water resources. This is the first research that assesses the impact of climatic factors on urban water demand in Iraq, which is one of the hottest countries in the world. We present a novel forecasting methodology that includes data preprocessing and an artificial neural network (ANN) model, which is integrated by a recently nature-inspired metaheuristic algorithm (marine predators algorithm (MPA)). The MPA-ANN algorithm will be compared with four different nature-inspired metaheuristic algorithms. Nine climatic factors were examined with different scenarios to simulate the monthly stochastic urban water demand over eleven years for Baghdad City, Iraq. The results reveal that: 1) precipitation, solar radiation, and dew point temperature are the most relevant factors to develop the models. 2) The ANN model becomes more accurate when it is used in combination with the MPA. 3) This methodology can accurately forecast the water demand considering the variability in climatic factors. These findings are of considerable significance to water utilities to plan, review, and compare the availability of freshwater resources and increase water requests (i.e., adaptation variability of climatic factors)