Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan

Multi-step ahead inflow forecasting has a critical role to play in reservoir operation and management in Taiwan during typhoons as statutory legislation requires a minimum of 3-hours warning to be issued before any reservoir releases are made. However, the complex spatial and temporal heterogeneity of typhoon rainfall, coupled with a remote and mountainous physiographic context makes the development of real-time rainfall-runoff models that can accurately predict reservoir inflow several hours ahead of time challenging. Consequently, there is an urgent, operational requirement for models that can enhance reservoir inflow prediction at forecast horizons of more than 3-hours. In this paper we develop a novel semi-distributed, data-driven, rainfall-runoff model for the Shihmen catchment, north Taiwan. A suite of Adaptive Network-based Fuzzy Inference System solutions is created using various combinations of auto-regressive, spatially-lumped radar and point-based rain gauge predictors. Different levels of spatially-aggregated radar-derived rainfall data are used to generate 4, 8 and 12 sub-catchment input drivers. In general, the semi-distributed radar rainfall models outperform their less complex counterparts in predictions of reservoir inflow at lead-times greater than 3-hours. Performance is found to be optimal when spatial aggregation is restricted to 4 sub-catchments, with up to 30% improvements in the performance over lumped and point-based models being evident at 5-hour lead times. The potential benefits of applying semi-distributed, data-driven models in reservoir inflow modelling specifically, and hydrological modelling more generally, is thus demonstrated

Forecasting Water Inflow System in Bang Lang Dam

วิทยาศาสตรมหาบัณฑิต (การจัดการเทคโนโลยีสารสนเทศ), 2566Forecasting water inflow into the Bang Lang Dam is important for the management of the Pattani River Basin, which serves as a multi-purpose irrigation source for electricity generation and agriculture in the area. Currently, the information system provides various reports but lacks predictive information regarding the amount of water flowing into the dam, which is crucial for effective water management. The prediction of dam inflow needs to be studied in order to understand the factors that affect the amount of water inflow, serving as a key element in accurate forecasting. Therefore, this research aims to study the factors that influence the water inflow to develop a dashboard model for forecasting water inflow in the Bang Lang Dam located in Bannang Sata District, Yala Province. The study utilized H2O's deep learning model, specifically feedforward neural networks, to create a predictive model for water inflows. Data were imported daily from January 1, 2012, to December 31, 2020. The most significant factor influencing the forecast of water flowing into the dam was the amount of water flowing into the Bang Lang Dam from the previous day, followed by daily rainfall, daily average temperature, daily average relative humidity, and average daily air pressure from STH031 station. The stations BTGH, BLD1, and VLGE35 followed with weight values of 0.136, 0.134, and 0.128, respectively. The model's accuracy was measured using MAE (Mean Absolute Error): 1.300, RMSE (Root Mean Square Error): 3.111, R2 (Coefficient of Determination): 0.767, and R (Correlation Coefficient): 0.876, which indicates good reliability. The model can be displayed as a dashboard tailored to the user's needs. The dashboard is divided into two parts. The first part shows the forecasting results of the amount of water flowing into the dam, presenting 22 variables and displaying the results in millions of cubic meters per day. The second part presents the necessary information to facilitate the operation of the staff involved in water management.การพยากรณ์น้ำไหลเข้าเขื่อนบางลางมีความสำคัญต่อการบริหารจัดการลุ่มแม่น้ำปัตตานี ซึ่งเป็นแหล่งชลประทานอเนกประสงค์เพื่อการผลิตไฟฟ้าและการเกษตรในพื้นที่ ปัจจุบันระบบสารสนเทศได้แสดงข้อมูลรายงานต่าง ๆ แต่ยังขาดข้อมูลการคาดการณ์เกี่ยวกับปริมาณน้ำไหลเข้าเขื่อนที่เป็นประโยชน์ต่อการบริหารจัดการน้ำที่มีประสิทธิภาพ การคาดการณ์น้ำไหลเข้าเขื่อนจำเป็นต้องทราบถึงปัจจัยที่ส่งผลต่อปริมาณน้ำไหลเข้าเป็นองค์ประกอบสำคัญในการสร้างแบบจำลองเพื่อการพยากรณ์ที่ถูกต้องและแม่นยำ ดังนั้น งานวิจัยนี้มีวัตถุประสงค์เพื่อศึกษาปัจจัยที่ส่งผลต่อปริมาณน้ำไหลเข้าของเขื่อนบางลางอำเภอบันนังสตา จังหวัดยะลา เพื่อนำไปพัฒนาแดชบอร์ดแบบจำลองการพยากรณ์การไหลเข้าของน้ำในเขื่อนบางลาง การศึกษานี้ใช้โมเดลการเรียนรู้เชิงลึกของ H2O ซึ่งเป็นโครงข่ายประสาทเทียมแบบฟีดฟอร์เวิร์ด (Feedforward Neural Networks) สร้างแบบจำลองสำหรับการพยากรณ์การไหลเข้าของน้ำ ข้อมูลที่นำเข้าเป็นรายวันตั้งแต่วันที่ 1 มกราคมพ.ศ.2555 ถึง 31 ธันวาคม 2563 การวิจัยพบว่า ปัจจัยที่มีผลต่อการพยากรณ์ปริมาณน้ำไหลเข้าเขื่อนมากที่สุด คือ ปริมาณน้ำไหลเข้าเขื่อนบางลางวันก่อนหน้า รองลงมาคือ ปริมาณน้ำฝนรายวัน อุณหภูมิเฉลี่ยรายวัน ความชื้นสัมพันธ์เฉลี่ยรายวัน และความกดอากาศเฉลี่ยรายวัน ด้วยค่าน้ำหนัก 0.665 0.247 0.169 0.151 และ 0.007 ตามลำดับ และสถานีตรวจวัดสภาพอากาศที่มีผลต่อการพยากรณ์ปริมาณน้ำไหลเข้าเขื่อนมากที่สุด คือ สถานี STH031 รองลงมาคือ สถานี BTGH สถานี BLD1 และสถานี VLGE35 ด้วยค่าน้ำหนัก 0.136 0.134 0.128 และ 0.064 ตามลำดับ แบบจำลองวัดค่าความแม่นยำด้วยค่า MAE:1.300, RMSE:3.111, R2: 0.767, R:0.876 ซึ่งให้ผลลัพธ์ความน่าเชื่อถือในระดับดี สามารถนำแบบจำลองแสดงผลเป็นแดชบอร์ดตามความต้องการของผู้ใช้งาน ออกเป็นสองส่วน คือ ส่วนแรกสำหรับการแสดงผลการพยากรณ์ปริมาณน้ำไหลเข้าเขื่อนรับข้อมูลตัวแปร 22 ค่า แสดงผลการพยากรณ์น้ำที่ไหลเข้าเขื่อนบางลางเป็นหน่วยล้านลูกบาศก์เมตรต่อวัน ส่วนที่สอง แสดงสารสนเทศข้อมูลที่จำเป็นเพื่ออำนวยความสะดวกต่อการปฏิบัติงานของเจ้าหน้าที

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

Optimal operation of dams/reservoirs emphasizing potential environmental and climate change impacts

Mahdi studied the potential ecological and climate change impacts on management of dams. He developed several new optimization frameworks in which benefits of dams are maximized, while above impacts are mitigated. Governments and consulting engineers can use the proposed frameworks for managing dams considering environmental challenges in river basins

Hypertuned temporal fusion transformer for multi-horizon time series forecasting of dam level in hydroelectric power plants

This paper addresses the challenge of predicting dam level rise in hydroelectric power plants during floods and proposes a solution using an automatic hyperparameters tuning temporal fusion transformer (AutoTFT) model. Hydroelectric power plants play a critical role in long-term energy planning, and accurate prediction of dam level rise is crucial for maintaining operational safety and optimizing energy generation. The AutoTFT model is applied to analyze time series data representing the water storage capacity of a hydroelectric power plant, providing valuable insights for decision-making in emergency situations. The results demonstrate that the AutoTFT model surpasses other deep learning approaches, achieving high accuracy in predicting dam level rise across different prediction horizons. Having a root mean square error (RMSE) of 2.78×10−3 for short-term forecasting and 1.72 considering median-term forecasting, the AutoTFT shows to be promising for time series prediction presented in this paper. The AutoTFT had lower RMSE than the adaptive neuro-fuzzy inference system, long short-term memory, bootstrap aggregation (bagged), sequential learning (boosted), and stacked generalization ensemble learning approaches. The findings underscore the potential of the AutoTFT model for improving operational efficiency, ensuring safety, and optimizing energy generation in hydroelectric power plants during flood events

Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management

The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management

A contemporary review on drought modeling using machine learning approaches

Drought is the least understood natural disaster due to the complex relationship of multiple contributory factors. Its beginning and end are hard to gauge, and they can last for months or even for years. India has faced many droughts in the last few decades. Predicting future droughts is vital for framing drought management plans to sustain natural resources. The data-driven modelling for forecasting the metrological time series prediction is becoming more powerful and flexible with computational intelligence techniques. Machine learning (ML) techniques have demonstrated success in the drought prediction process and are becoming popular to predict the weather, especially the minimum temperature using backpropagation algorithms. The favourite ML techniques for weather forecasting include singular vector machines (SVM), support vector regression, random forest, decision tree, logistic regression, Naive Bayes, linear regression, gradient boosting tree, k-nearest neighbours (KNN), the adaptive neuro-fuzzy inference system, the feed-forward neural networks, Markovian chain, Bayesian network, hidden Markov models, and autoregressive moving averages, evolutionary algorithms, deep learning and many more. This paper presents a recent review of the literature using ML in drought prediction, the drought indices, dataset, and performance metrics

Three layer wavelet based modeling for river flow

All existing methods regarding time series forecasting have always been challenged by the continuous climatic change taking place in the world. These climatic changes influence many unpredictable indefinite factors. This alarming situation requires a robust forecasting method that could efficiently work with incomplete and multivariate data. Most of the existing methods tend to trap into local minimum or encounter over fitting problems that mostly lead to an inappropriate outcome. The complexity of data regarding time series forecasting does not allow any one single method to yield results suitable in all situations as claimed by most researchers. To deal with the problem, a technique that uses hybrid models has also been devised and tested. The applied hybrid methods did bring some improvement compared to the individual model performance. However, most of these available hybrid models exploit univariate data that requires huge historical data to achieve precise forecasting results. Therefore, this study introduces a new hybrid model based on three layered architecture: Least Square Support Vector Machine (LSSVM), Discrete Wavelet Transform (DWT), correlation (R) and Kernel Principle Components Analyses (KPCA). The three-staged architecture of the proposed hybrid model includes Wavelet-LSSVM and Wavelet-KPCA-LSSVM enabling the model to present itself as a well-established alternative application to predict the future of river flow. The proposed model has been applied to four different data sets of time series, taking into account different time series behavior and data scale. The performance of the proposed model is compared against the existing individual models and then a comparison is also drawn with the existing hybrid models. The results of WKPLSSVM obtained from Coefficient of Efficiency (CE) performance measuring methods confirmed that proposed model has encouraging data of 0.98%, 0.99%, 0.94% and 0.99% for Jhelum River, Chenab River, Bernam River and Tualang River, respectively. It is more robust for all datasets regardless of the sample sizes and data behavior. These results are further verified using diverse data sets in order to check the stability and adaptability. The results have demonstrated that the proposed hybrid model is a better alternative tool for time series forecasting. The proposed hybrid model proves to be one of the best available solutions considering the time series forecasting issues

Wind turbine power output short-term forecast : a comparative study of data clustering techniques in a PSO-ANFIS model

Abstract:The emergence of new sites for wind energy exploration in South Africa requires an accurate prediction of the potential power output of a typical utility-scale wind turbine in such areas. However, careful selection of data clustering technique is very essential as it has a significant impact on the accuracy of the prediction. Adaptive neurofuzzy inference system (ANFIS), both in its standalone and hybrid form has been applied in offline and online forecast in wind energy studies, however, the effect of clustering techniques has not been reported despite its significance. Therefore, this study investigates the effect of the choice of clustering algorithm on the performance of a standalone ANFIS and ANFIS optimized with particle swarm optimization (PSO) technique using a synthetic wind turbine power output data of a potential site in the Eastern Cape, South Africa. In this study a wind resource map for the Eastern Cape province was developed. Also, autoregressive ANFIS models and their hybrids with PSO were developed. Each model was evaluated based on three clustering techniques (grid partitioning (GP), subtractive clustering (SC), and fuzzy-c-means (FCM)). The gross wind power of the model wind turbine was estimated from the wind speed data collected from the potential site at 10 min data resolution using Windographer software. The standalone and hybrid models were trained and tested with 70% and 30% of the dataset respectively. The performance of each clustering technique was compared for both standalone and PSO-ANFIS models using known statistical metrics. From our findings, ANFIS standalone model clustered with SC performed best among the standalone models with a root mean square error (RMSE) of 0.132, mean absolute percentage error (MAPE) of 30.94, a mean absolute deviation (MAD) of 0.077, relative mean bias error (rMBE) of 0.190 and variance accounted for (VAF) of 94.307. Also, PSO-ANFIS model clustered with SC technique performed the best among the three hybrid models with RMSE of 0.127, MAPE of 28.11, MAD of 0.078, rMBE of 0.190 and VAF of 94.311. The ANFIS-SC model recorded the lowest computational time of 30.23secs among the standalone models. However, the PSO-ANFIS-SC model recorded a computational time of 47.21secs. Based on our findings, a hybrid ANFIS model gives better forecast accuracy compared to the standalone model, though with a trade-off in the computational time. Since, the choice of clustering technique was observed to play a vital role in the forecast accuracy of standalone and hybrid models, this study recommends SC technique for ANFIS modeling at both standalone and hybrid models