Seasonal Autoregressive Integrated Moving Research Articles

Electricity Theft Detection in Smart Grids Using Sarimax and OCR

This study proposes a novel approach to detect electricity theft in smart grids by combining SARIMAX (Seasonal Autoregressive Integrated Moving Average with Exogenous Factors) models with Optical Character Recognition (OCR) techniques. Electricity theft remains a significant challenge in smart grid systems, leading to revenue losses and operational inefficiencies. Traditional methods often fall short in accurately identifying and addressing such illicit activities. The integration of SARIMAX models leverages time-series data, capturing patterns and anomalies indicative of electricity theft. These models incorporate exogenous factors such as weather conditions, economic indicators, and historical consumption patterns, enhancing the detection accuracy. Additionally, OCR technology is applied to analyze meter readings and identify discrepancies or irregularities that may signal potential theft instances. The synergy between SARIMAX and OCR offers a comprehensive solution for electricity theft detection in smart grids. By harnessing advanced analytics and machine learning algorithms, this approach enables utility providers to proactively identify suspicious activities, mitigate revenue losses, and ensure the integrity of their grid infrastructure

MODEL PERAMALAN JUMLAH TANGKAPAN IKAN KAKAP YANG DIDARATKAN DI PPI OEBA KUPANG NUSA TENGGARA TIMUR

Snapper is one of the economically important fish targeted by fishers in the island of Timor. Domestic and foreign markets for fillet and fresh snapper is considerably large. The export volume in 2021 was 4,172,056 kg, and the total value reached 12,452,211 USD. Along with the increasing snapper production in Kupang City and NTT, it is necessary to forecast the future snapper production. Thus, it can be a reference for policy makers in the region in designing fisheries development. This study aims to produce forecasting model for snapper catches at PPI Oeba Kupang. This research applied Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA), as a forecasting method. The data of snapper production analyzed in this study consisted of 72 months, started from January 2016 to December 2021. Data were obtained from the UPT of the Department of Marine and Fisheries of NTT Province at PPI Oeba Kupang. Data have a non-stationary pattern to the variance, hence a logarithmic transformation is needed, as well as seasonal differences analysis. However, there is no need for non-seasonal differences since the data are stationary with respect to the mean. The results of the identification of the Autocorrelation function and Partial Autucorrelation function Plots are ARIMA models with seasonal factor period 4, ARIMA (P,D,Q) = (1,0,1), while the SARIMA order (P, D, Q) = (2,1,1)4. Based on parameter testing, verification, examination, and testing of suitable models, the best model obtained was (0,0,1)(2,1,1)4. Keywords: Autoregressive Integrated Moving Average, focecasting model, Seasonal Autoregressive Integrated Moving Average, Snapper.

Agricultural price prediction based on data mining and attention-based gated recurrent unit: a case study on China’s hog

Under the influence of the coronavirus disease and other factors, agricultural product prices show non-stationary and non-linear characteristics, making it increasingly difficult to forecast accurately. This paper proposes an innovative combinatorial model for Chinese hog price forecasting. First, the price is decomposed using the Seasonal and Trend decomposition using the Loess (STL) model. Next, the decomposed data are trained with the Long Short-term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. Finally, the prepared data and the multivariate influence factors after Factor analysis are predicted using the gated recurrent neural network and attention mechanisms (AttGRU) to obtain the final prediction values. Compared with other models, the STL-FA-AttGRU model produced the lowest errors and achieved more accurate forecasts of hog prices. Therefore, the model proposed in this paper has the potential for other price forecasting, contributing to the development of precision and sustainable agriculture.

Predicting Petrol and Diesel Prices in Ghana, A Comparison of ARIMA and SARIMA Models

Predicting prices is of great concern and important in the world of economics and finance. In this paper, a comparative analysis of gasoline and diesel in Ghana were analysed using Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA). Based on their forecasting accuracy, the best model was used for predicting future prices of gasoline and diesel from January 2024 to December 2024. A monthly data for the prices of gasoline and diesel spanning from January 2016 to December 2023 taken from the Bank of Ghana (BoG) and the National Petroleum Authority (NPA) was used for the analysis. ARIMA (0; 1; 2) and ARIMA (2; 1; 3) were identified as the best models for gasoline and diesel respectively, SARIMA(0; 1; 2) x (0; 1; 1)12 and SARIMA (1; 1; 1) x (0; 1; 1)12 were also identified after taking a seasonal difference of the series all based on AIC and BIC. The coefficient of the identified models were tested for its significance using the Z-test. The ARIMA and the SARIMA models were compared using RMSE, MAE, and MAPE. The SARIMA models generally performed better than the ARIMA models for both gasoline and diesel except RMSE for diesel where the ARIMA model was slightly better than the SARIMA models with values of 0:9677988 and 1:011531 respectively. The model evaluation proved that the SARIMA models for both gasoline and diesel were superior to the ARIMA and showed that, the SARIMA model is adequate and appropriate for forecasting of prices of gasoline and diesel prices in Ghana.

Empowering Sustainability: A Consumer-Centric Analysis Based on Advanced Electricity Consumption Predictions

This study addresses the critical challenge of accurately forecasting electricity consumption by utilizing Exponential Smoothing and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. The research aims to enhance the precision of forecasting in the dynamic energy landscape and reveals promising outcomes by employing a robust methodology involving model application to a large amount of consumption data. Exponential Smoothing demonstrates accurate predictions, as evidenced by a low Sum of Squared Errors (SSE) of 0.469. SARIMA, with its seasonal ARIMA structure, outperforms Exponential Smoothing, achieving lower Mean Absolute Percentage Error (MAPE) values on both training (2.21%) and test (2.44%) datasets. This study recommends the adoption of SARIMA models, supported by lower MAPE values, to influence technology adoption and future-proof decision-making. This study highlights the societal implications of informed energy planning, including enhanced sustainability, cost savings, and improved resource allocation for communities and industries. The synthesis of model analysis, technological integration, and consumer-centric approaches marks a significant stride toward a resilient and efficient energy ecosystem. Decision-makers, stakeholders, and researchers may leverage findings for sustainable, adaptive, and consumer-centric energy planning, positioning the sector to address evolving challenges effectively and empowering consumers while maintaining energy efficiency.

Power consumption prediction for electric vehicle charging stations and forecasting income

Electric vehicles (EVs) are the future of the automobile industry, as they produce zero emissions and address environmental and health concerns caused by traditional fuel-poared vehicles. As more people shift towards EVs, the demand for power consumption forecasting is increasing to manage the charging stations effectively. Predicting power consumption can help optimize operations, prevent grid overloading, and power outages, and assist companies in estimating the number of charging stations required to meet demand. The paper uses three time series models to predict the electricity demand for charging stations, and the SARIMA (Seasonal Auto Regressive Integrated Moving Average) model outperforms the ARMA (Auto Regressive Moving Average) and ARIMA (Auto Regressive Integrated Moving Average) models, with the least RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error) scores in forecasting power demand and revenue. The data used for validation consists of charging activities over a four-year period from public charging outlets in Colorado, six months of charging data from ChargeMOD's public charging terminals in Kerala, India. Power usage is also forecasted based on wheels of vehicles, and finally, a plan subscription data from the same source is utilized to anticipate income, that helps companies develop pricing strategies to maximize profits while remaining competitive. Utility firms and charging networks may use accurate power consumption forecasts for a variety of purposes, such as power scheduling and determining the expected energy requirements for charging stations. Ultimately, precise power consumption forecasting can assist in the effective planning and design of EV charging infrastructure. The main aim of this study is to create a good time series model which can estimate the electric vehicle charging stations usage of power and verify if the firm has a good income along with some accuracy measures. The results show that SARIMA model plays a vital role in providing us with accurate information. According to the data and study here, four wheelers use more power than two and three wheelers. Also, DC charging facility uses more electricity than AC charging stations. These results can be used to determine the cost to operate the EVs and its subscriptions.

Predicting Ghana’s Daily Natural Gas Consumption Using Time Series Models

In recent years, natural gas utilisation has seen a considerable increase because, it presents an alternative energy source that is reliable, economical and environmentally friendly for consumers. In Ghana, natural gas consumption has over the years increased due to mainly the rise in industrial and residential demands. Accurate prediction of natural gas consumption will provide stakeholders with vital information needed for planning and making informed policy decisions. This paper explores the Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) to predict Ghana's daily natural gas consumption. The data employed for the study is daily natural gas consumption in Ghana from 2020 to 2022. The results show that both ARIMA and SARIMA models can predict the consumption of natural gas in Ghana with a good degree of accuracy. The SARIMA model slightly outperforms the ARIMA model, with a Root Mean Square Error (RMSE) of 22.25 and a Mean Absolute Percentage Error (MAPE) of 6.96%, compared to an RMSE of 23.27 and a MAPE of 7.29% for the ARIMA model. The model forecast suggests a steady natural gas consumption in Ghana but with some intermittent fluctuations.

Predicting the Number of Forest and Land Fire Hotspot Occurrences Using the ARIMA and SARIMA Methods

Forests are an area and part of the environmental cycle that is very important for survival because forests are areas on Earth that regulate the balance of the ecosystem. Forest fires rank second only to illegal logging in Indonesia's list of forest destruction causes. Forest fires can occur due to two factors, namely natural and human factors. Therefore, the hotspot factor that can cause forest fires is an independent variable. The population of hotspots in the West Kalimantan region in 2020 amounted to 1,416 spots. This study aims to predict the number of hotspot occurrences on land and forests that cause fires before the fires spread and are challenging to overcome or extinguish. The method to indicate the number of hotspot occurrences uses the Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) methods. Modeling ARIMA (0,1,1) and SARIMA (0,1,1) (2,2,1)12 obtained Root Mean Square Error (RMSE) evaluation results for ARIMA of 6.61 while SARIMA of 7.61. The ARIMA's Mean Squared Error (MSE) evaluation value is 43.70, and the SARIMA is 58.05. Based on these results, it can be concluded that the ARIMA model provides excellent and accurate performance in describing the trend of hotspot events that will occur in the future with a smaller RMSE value compared to SARIMA.

Optimizing aeration efficiency and forecasting dissolved oxygen in brackish water aquaculture: Insights from paddle wheel aerator

BackgroundAquaculture relies significantly on effective aeration systems to ensure optimal conditions for aquatic organisms. This 96-day study investigates the dynamic relationship between Oxygen Transfer Rates (OTR) and seasonal variations, with a specific focus on the impact of seasonal dynamics and the placement of paddle wheel aerators. The study recognizes the pivotal role of Total Dissolved Solids (TDS) and Total Suspended Solids (TSS) as key water quality parameters influencing aeration efficiency. MethodologyA series of water circulation experiments were conducted at regular intervals to assess mixing rates, revealing a nuanced trajectory ranging from 27.05 to 14.22 m³/kWh. The study scrutinized the influence of TDS and TSS on these rates. Additionally, water velocity variations, ranging from 0.45 to 1.67 m/s, were examined, highlighting density-dependent changes, particularly evident post four weeks of operation. Oxygen stratification analysis provided insights into deviations in Dissolved Oxygen (DO) concentrations, with particular attention to climatic aberrations. Rigorous statistical analyses, including chi-squared, Pearson correlation, Gaussian distribution checks, and student's t-tests, validated the methodological robustness and data reliability. Significant findingsEmploying a Seasonal Auto Regressive Integrated Moving Average (SARIMA) model, the study achieved a remarkable 97 % accuracy in forecasting DO levels for the subsequent 96 days. Real-time validation, complemented by a Chi-square goodness of fit test, reaffirmed the model's reliability, establishing congruence between observed and forecasted values. This research underscores the critical roles of strategic aerator placement and seasonal considerations in optimizing pond aeration efficiency, providing substantive insights for the sustainable management of aquaculture ecosystems.

Water consumption time series forecasting in urban centers using deep neural networks

The time series analysis and prediction techniques are highly valued in many application fields, such as economy, medicine and biology, environmental sciences or meteorology, among others. In the last years, there is a growing interest in the sustainable and optimal management of a resource as scarce as essential: the water. Forecasting techniques for water management can be used for different time horizons from the planning of constructions that can respond to long-term needs, to the detection of anomalies in the operation of facilities or the optimization of the operation in the short and medium term. In this paper, a deep neural network is specifically designed to predict water consumption in the short-term. Results are reported using the time series of water consumption for a year and a half measured with 10-min frequency in the city of Murcia, the seventh largest city in Spain by number of inhabitants. The results are compared with K Nearest Neighbors, Random Forest, Extreme Gradient Boosting, Seasonal Autoregressive Integrated Moving Average and two persistence models as naive methods, showing the proposed deep learning model the most accurate results.

Multilayer Seasonal Autoregressive Integrated Moving Average Models for Complex Network Traffic Analysis

The ever-increasing amount of network traffic generated by various devices and applications has made it crucial to have efficient methods for analyzing and managing network traffic. Traditional approaches, such as statistical modeling, have yet to be proven enough due to network traffic's complex nature and dynamic characteristics. Recent research has shown the effectiveness of complex network analysis techniques for understanding network traffic patterns. This paper proposes multilayer seasonal autoregressive integrated moving average models for analyzing and predicting network traffic. This approach considers the seasonal patterns and interdependencies between different layers of network traffic, allowing for a more accurate and comprehensive representation of the data. The Multilayer Seasonal Autoregressive Integrated Moving Average (MSARIMA) model consists of multiple layers, each representing a different aspect of network traffic, such as time of day, day of week, or type of traffic. Each layer is modeled separately using SARIMA, a popular time series forecasting technique. The models for different layers are combined to capture the overall behavior of network traffic. The proposed approach has several benefits over traditional statistical approaches. It can capture network traffic's complex and dynamic nature, including short-term and long-term seasonal patterns. It also allows for the detection of anomalies and the prediction of future traffic patterns with high accuracy.

Cryptocurrency Price Prediction Using Machine Learning Techniques

Abstract: The goal of this project is to use machine learning to forecast cryptocurrency values. As a result of their high levels of volatility, cryptocurrencies are notoriously difficult to anticipate in terms of value. SARIMA (Seasonal Auto Regressive Integrated Moving Average) algorithm that we suggest using to capture the intricate dynamics of the bitcoin market. Our machine learning models will be trained using the gathered data, and they will then be utilised to forecast future cryptocurrency values. The project's final product is anticipated to be a useful tool for cryptocurrency traders, analysts, and investors, giving them a more precise way to make data-drive investment decisions.

Forecasting daily COVID-19 cases with gradient boosted regression trees and other methods: evidence from U.S. cities.

There is a vast literature on the performance of different short-term forecasting models for country specific COVID-19 cases, but much less research with respect to city level cases. This paper employs daily case counts for 25 Metropolitan Statistical Areas (MSAs) in the U.S. to evaluate the efficacy of a variety of statistical forecasting models with respect to 7 and 28-day ahead predictions. This study employed Gradient Boosted Regression Trees (GBRT), Linear Mixed Effects (LME), Susceptible, Infectious, or Recovered (SIR), and Seasonal Autoregressive Integrated Moving Average (SARIMA) models to generate daily forecasts of COVID-19 cases from November 2020 to March 2021. Consistent with other research that have employed Machine Learning (ML) based methods, we find that Median Absolute Percentage Error (MAPE) values for both 7-day ahead and 28-day ahead predictions from GBRTs are lower than corresponding values from SIR, Linear Mixed Effects (LME), and Seasonal Autoregressive Integrated Moving Average (SARIMA) specifications for the majority of MSAs during November-December 2020 and January 2021. GBRT and SARIMA models do not offer high-quality predictions for February 2021. However, SARIMA generated MAPE values for 28-day ahead predictions are slightly lower than corresponding GBRT estimates for March 2021. The results of this research demonstrate that basic ML models can lead to relatively accurate forecasts at the local level, which is important for resource allocation decisions and epidemiological surveillance by policymakers.

Traffic Flow Forecast using Time Series Analysis based on Machine Learning Algorithm

Time series forecasting plays a critical role in predicting future trends and making informed decisions across various domains. In this research paper, we present a multi-model approach to improve the accuracy and robustness of time series forecasting. Our methodology involves integrating two powerful models, Long Short-Term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA), to capture complex temporal patterns and seasonality in the data. We leverage real-world traffic data from different junctions and employ a comprehensive pipeline for data preprocessing, including data selection, resampling, and sequence generation. Our study focuses on four junctions, and for each junction, we individually train LSTM models, followed by SARIMA models for comparison. The LSTM models are designed to capture long-term dependencies in the time series data, while the SARIMA models are tailored to handle seasonality and autocorrelation. We implement a custom sequence generation process and split the dataset into training and testing sets for model evaluation. The LSTM models are trained using Pytorch, optimizing them for accurate short-term predictions. Our results show that the combination of LSTM and SARIMA models yields superior forecasting performance compared to using each model individually. We present a detailed analysis of the forecasted results, including Root Mean Square Error (RMSE) calculations and visualizations to demonstrate the effectiveness of our multi-model approach. This research contributes to the field of time series forecasting by showcasing the benefits of combining deep learning and classical statistical methods. The proposed approach provides a flexible and robust framework applicable to various time series prediction tasks, offering improved accuracy and reliability in forecasting future trends.

Leveraging Classical Statistical Methods for Sustainable Maintenance in Automotive Assembly Equipment

Predictive maintenance management plays a crucial role in ensuring the reliable operation of equipment in industry. While continuous monitoring technology is available today, equipment without sensors limits continuous equipment state data recording. Predictive maintenance has been effectively carried out using artificial intelligence algorithms for datasets with sufficient data. However, replicating these results with limited data is challenging. This work proposes the use of time series models to implement predictive maintenance in the equipment of an automotive assembly company with few records available. For this purpose, three models are explored—Holt–Winters Exponential Smoothing (HWES), Autoregressive Integrated Moving Average (ARIMA), and Seasonal Autoregressive Integrated Moving Average (SARIMA)—to determine the most accurate forecasting of future equipment downtime and advocate the use of SAP PM for effective maintenance process management. The data were obtained from five equipment families from January 2020 to December 2022, representing 36 registers for each piece of equipment. After data fitting and forecasting, the results indicate that the SARIMA model best fits seasonal characteristics, and the forecasting offers valuable information to help in decision-making to avoid equipment downtime, despite having the highest error. The results were less favorable when handling datasets with random components, requiring model recalibration for short-term forecasting.

Fast autoregressive tensor decomposition for online real-time traffic flow prediction

Online real-time traffic flow prediction typically offers better real-time performance than offline prediction. However, existing studies rarely discussed online real-time traffic flow prediction and the balance between prediction accuracy and computational costs. Training and predicting traffic flow data with complex patterns by artificial intelligence models is usually time-consuming. Some high-accuracy statistical learning methods also reach a performance bottleneck in terms of computational speed. Therefore, a Fast Autoregressive Tensor Decomposition (FATD) algorithm is proposed for online real-time traffic flow prediction. First, the historical tensor data is decomposed by Tucker Decomposition into factor matrices and easy-to-compute core tensors, and these core tensors are modeled by Tensor Seasonal Autoregressive Integrated Moving Average (Tensor SARIMA). Second, a future core tensor is predicted by Tensor SARIMA. By the Inverse Tucker Decomposition, the traffic flow data to be predicted is recovered. The experimental results show that the FATD algorithm can reduce the computational cost while maintaining high accuracy. Compared with baselines, the FATD algorithm reduces MAE, RMSE, and the computational cost by approximately 35.04%, 26.86%, and 99.28% on average, respectively.

Numerical modelling of a hydro-pneumatic energy storage system for smoothing power fluctuations from offshore wind

Energy storage systems are imperative in tackling the regulation of electricity supply and demand mismatch to avoid curtailment of wind energy. Co-locating energy storage in the same operating region as the offshore wind farms allows for an unobtrusive environment while also steering away from rising land prices. The presented work involves an offshore Hydro-Pneumatic Energy Storage (HPES) system made up of a subsea accumulator pre-charged with compressed air. The Energy Conversion Unit (ECU) of the system consists of a megawatt-scale hydraulic pump and a turbine. This paper discusses a novel numerical model developed in Python™ for simulating the operation of the ECU pump and turbine of the offshore HPES system by using a simple moving average, based on a time window of wind data, whilst also introducing a one-step-ahead forecasting model. The results of the research are split into two parts; Firstly, the analysis and validation of the Seasonal Auto Regressive Integrated Moving Average (SARIMA) forecasting model is performed. Secondly, results on the pump and turbine performance based on the smoothened power are shown. The research found that despite the centrifugal pump limitations due to variable head operation, a smoothened power output is efficiently (> 90 %) attained in retaining the intermittent power generated.

Stochastic modelling and forecasting of relative humidity and wind speed for different zones of Kerala

The variations in climatic conditions depend on seasonal changes throughout the year. Modelling and prediction of climatic conditions can help to determine the impacts of seasonal changes in climate over a specific period of time. Climate change can directly and indirectly affect agricultural, industrial, geographical and technological sectors in our society. Agriculture and the allied sector are seriously affected by changes in climate since it leads to complete destruction of cultivated crops. In this study, in order to model and forecast relative humidity and wind speed for northern, central and southern zones of Kerala, stochastic approach using SARIMA (Seasonal Autoregressive Integrated Moving Average) model was employed. The monthly weather data for the northern zone and the central zone of Kerala was taken from the location of RARS Pilicode and RARS Pattambi for a period of 39 years (1982-2020) whereas for southern zone, data was collected from the location of RARS, Vellayani for a period of 36 years (1985-2020) with the help of data access viewer. The model validation was done using MSE (mean square error), RMSE (root mean square error), MAE (mean absolute error) and RMAPE (relative mean absolute percentage error). The RMAPE values of relative humidity and wind speed in different zones of Kerala was less than 10 per cent which indicated that fitted model is showing accurate performance. The best selected SARIMA model is used in attaining anticipated values of relative humidity and wind speed for the next 5 years.

Forecasting monthly soil moisture at broad spatial scales in sub-Saharan Africa using three time-series models: evidence from four decades of remotely sensed data

ABSTRACT Soil moisture is a critical environmental variable that determines primary productivity and contributes to climatic processes. It is, therefore, important to forecast soil moisture to inform expectations of derivative outputs reliably. While forecasting soil moisture continues to advance, there is a need to extend it to different geoclimatic regions, including in sub-Saharan Africa, where livelihoods predominantly rely on subsistence agriculture. We used remotely sensed soil moisture data produced by the European Space Agency – Climate Change Initiative (ESA CCI). The data, which covered the period 1978 to 2019, were used to forecast monthly soil moisture in different agroecological zones and land cover types. The Seasonal Random Walk, Exponential Smoothing and Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasting models were trained on 70% of the data (November 1978 – August 2007) and subsequently applied to a test dataset (September 2007 – December 2019). All models showed solid prediction accuracies for all agroecological zones (unbiased root mean square error, ubRMSE ≤ 0.05 m3 m−3) and land cover types (ubRMSE ≤ 0.04 m3 m−3). This was corroborated by similarities in season-adjusted anomalies between observed and forecasted soil moisture for nearly all agroecological zones and land cover types, with a correlation coefficient of r > 0.5 for most locations). The broad-scale interpretation of soil moisture forecasting can inform moisture availability and variability by regions; however, more research is encouraged to improve forecasting at spatially and temporally detailed levels to assist small-scale farming practices in the continent.

The Prediction of The Drought Index in The Indragiri Watershed Using SARIMA and SPI Methods

Farmers are concerned about the problem of drought in agriculture because it will disrupt the production system and cause losses. In 2015, the Indragiri Hulu area experienced a drought on 273 hectares of agricultural land. This area is part of the Indragiri River Watershed in Riau Province. To prevent the impact of future droughts, forecasting is required. The level of drought is influenced by rainfall, so the Seasonal Auto Regressive Integrated Moving Average (SARIMA) method is used to model rainfall data. In addition, the Standardized Precipitation Index (SPI) is also used to analyze the drought index. After the analysis, it was found that the best model in forecasting is SARIMA model (1,0,1)(2,1,1)12 with an average residual square of 18.0175, and the drought index in the Indragiri watershed is currently included in the "Normal" category. The results of this forecasting are expected to assist the Riau Province National Disaster Management Agency in anticipating and mitigating the impact of future drought disasters.