scholarly journals Application of SARIMA model to forecasting monthly flows in Waterval River, South Africa

2017 ◽  
Vol 35 (1) ◽  
pp. 229-236 ◽  
Author(s):  
Kassahun Birhanu Tadesse ◽  
Megersa Olumana Dinka
Keyword(s):  
River Flow ◽  
River System ◽  
Forecast Performance ◽  
Sarima Model ◽  
River Flow Forecasting ◽  
Flow Information ◽  
Computational Intelligent ◽  
Development And Management ◽  
Sarima Models ◽  
Modelling And Forecasting

AbstractKnowledge of future river flow information is fundamental for development and management of a river system. In this study, Waterval River flow was forecasted by SARIMA model using GRETL statistical software. Mean monthly flows from 1960 to 2016 were used for modelling and forecasting. Different unit root and Mann–Kendall trend analysis proved the stationarity of the observed flow time series. Based on seasonally differenced correlogram characteristics, different SARIMA models were evaluated; their parameters were optimized, and diagnostic check up of forecasts was made using white noise and heteroscedasticity tests. Finally, based on minimum Akaike Information (AI) and Hannan–Quinn (HQ) criteria, SARIMA (3, 0, 2) x (3, 1, 3)12 model was selected for Waterval River flow forecasting. Comparison of forecast performance of SARIMA models with that of computational intelligent forecasting techniques was recommended for future study.

scholarly journals Application of Sarima Models in Modelling and Forecasting Monthly Rainfall in Nigeria

2021 ◽  
pp. 30-43
Author(s):  
Aliyu Sani Aliyu ◽  
Abubakar Muhammad Auwal ◽  
M. O. Adenomon
Keyword(s):  
World Bank ◽  
Rainfall Amount ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
P Value ◽  
Sarima Model ◽  
Nigerian Government ◽  
Unpredictable Variation ◽  
Sarima Models ◽  
Modelling And Forecasting

Application of SARIMA model in modelling and forecasting monthly rainfall in Nigeria was considered in this study. The study utilizes the Nigerian monthly rainfall data between 1980-2015 obtained from World Bank Climate Portal. The Box-Jenkin’s methodology was adopted.  SARIMA (2,0,1) (2,1,1) [12] was the best model among others that fit the Nigerian rainfall data (1980-2015) with maximum p-value from Box-Pierce Residuals Test. The study forecasts Nigeria’s monthly rainfall from 2018 through 2042. It was discovered that the month of April is the period of onset of rainfall in Nigeria and November is the period of retreat. Based on the findings, Nigeria will experience approximately equal amount of rainfall between 2018 to 2021 and will experience a slight increase in rainfall amount in 2022 to about 1137.078 (mm). There will be a decline of rainfall at 2023 to about 1061 (mm). Rainfall values will raise again to about 1142.756 (mm) in 2024 and continue to fluctuate with decrease in variation between 2024 to 2042, then remain steady to 2046 at approximately 1110.0 (mm). Nigerian Government should provide a more mechanized and drier season farming methods to ease the outage of rainfall in future that may be caused due to natural (or unpredictable) variation.

Morphotectonic Analysis of Tanjung Bungo Area Based on Geological Structure Control, Central Sumatera Basin

2020 ◽  
Author(s):  
D., A., L., A. Putri
Keyword(s):  
Natural Resources ◽  
River Flow ◽  
Measurement Data ◽  
River System ◽  
Flow Patterns ◽  
Source Rocks ◽  
Aerial Photographs ◽  
Morphological Data ◽  
Tectonic Activity ◽  
Geological Structures

Tectonic activity in an area could result in various impacts such as changes in elevation, level of slope percentages, river flow patterns and systems, and the formation of geological structures both locally and regionally, which will form a new landscape. The tectonic activity also affects the stratigraphic sequences of the area. Therefore, it is necessary to study morphotectonic or landscape forms that are influenced by active tectonic activities, both those occur recently and in the past. These geological results help provide information of the potential of natural resources in and around Tanjung Bungo area. Morphological data are based on three main aspects including morphogenesis, morphometry, and morphography. The data are collected in two ways, the first is field survey by directly observing and taking field data such as measuring geological structures, rock positions, and outcrop profiles. The second way is to interpret them through Digital Elevation Model (DEM) and aerial photographs by analyzing river flow patterns and lineament analysis. The field measurement data are processed using WinTensor, Dips, and SedLog Software. The supporting data such as Topographic Maps, Morphological Elevation Maps, Slope Maps, Flow Pattern Maps, and Lineament Maps are based on DEM data and are processed using ArcGis Software 10.6.1 and PCI Geomatica. Morphotectonically, the Tanjung Bungo area is at a moderate to high-class level of tectonic activity taken place actively resulted in several joints, faults, and folds. The formation of geological structures has affected the morphological conditions of the area as seen from the development of steep slopes, structural flow patterns such as radial, rectangular, and dendritic, as well as illustrated by rough surface relief in Tanjung Bungo area. This area has the potential for oil and gas resources as indicated by the Telisa Formation, consisting of calcareous silts rich in planktonic and benthonic fossils, which may be source rocks and its contact with the Menggala Formation which is braided river system deposits that could be good reservoirs. Further research needs to be done since current research is only an interpretation of surface data. Current natural resources being exploited in Tanjung Bungo region are coals. The coals have thicknesses of 5-7 cm and are classified as bituminous coals.

A comparison between neural-network forecasting techniques-case study: river flow forecasting

1999 ◽  
Vol 10 (2) ◽  
pp. 402-409 ◽  
Author(s):  
A.F. Atiya ◽  
S.M. El-Shoura ◽  
S.I. Shaheen ◽  
M.S. El-Sherif
Keyword(s):  
Neural Network ◽  
River Flow ◽  
River Flow Forecasting ◽  
Forecasting Techniques

scholarly journals Artificial Intelligence Techniques for river flow forecasting in the Seyhan River Catchment, Turkey

2007 ◽  
Vol 4 (3) ◽  
pp. 1369-1406 ◽  
Author(s):  
M. Firat
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
River Flow ◽  
Fuzzy Inference ◽  
Flow Data ◽  
Data Set ◽  
Inference System ◽  
Forecasting Models ◽  
River Flow Forecasting ◽  
Best Fit

Abstract. The use of Artificial Intelligence methods is becoming increasingly common in the modeling and forecasting of hydrological and water resource processes. In this study, applicability of Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) methods, Generalized Regression Neural Networks (GRNN) and Feed Forward Neural Networks (FFNN), for forecasting of daily river flow is investigated and the Seyhan catchment, located in the south of Turkey, is chosen as a case study. Totally, 5114 daily river flow data are obtained from river flow gauges station of Üçtepe (1818) on Seyhan River between the years 1986 and 2000. The data set are divided into three subgroups, training, testing and verification. The training and testing data set include totally 5114 daily river flow data and the number of verification data points is 731. The river flow forecasting models having various input structures are trained and tested to investigate the applicability of ANFIS and ANN methods. The results of ANFIS, GRNN and FFNN models for both training and testing are evaluated and the best fit forecasting model structure and method is determined according to criteria of performance evaluation. The best fit model is also trained and tested by traditional statistical methods and the performances of all models are compared in order to get more effective evaluation. Moreover ANFIS, GRNN and FFNN models are also verified by verification data set including 731 daily river flow data at the time period 1998–2000 and the results of models are compared. The results demonstrate that ANFIS model is superior to the GRNN and FFNN forecasting models, and ANFIS can be successfully applied and provide high accuracy and reliability for daily River flow forecasting.

scholarly journals The occurrence and distribution of antibiotics in the Karst river system in Kaiyang, Southwest China

2018 ◽  
Vol 18 (6) ◽  
pp. 2044-2052 ◽  
Author(s):  
Shengzhang Zou ◽  
Fuyang Huang ◽  
Liang Chen ◽  
Fei Liu
Keyword(s):  
Southwest China ◽  
River Flow ◽  
Wastewater Treatment Plants ◽  
River System ◽  
Contamination Source ◽  
Antibiotic Concentration ◽  
Detection Frequency ◽  
Karst River ◽  
Flow Quantity ◽  
Total Concentrations

Abstract To our knowledge, this was the first study to investigate the occurrence and distribution of antibiotics in the Karst river system in Kaiyang, Southwest China. Ten water samples were collected from the Karst river in Kaiyang, Southwest China. Thirty-five antibiotics, including nine sulfonamides, four tetracyclines, five macrolides, sixteen quinolones and chloramphenicol, were analyzed. The results suggest that antibiotics are widely prevalent in the Karst river, with macrolides and quinolones being the most dominant and occupying 47% and 43% of total antibiotic concentration, respectively. The maximum total concentrations of sulfonamides, tetracyclines, macrolides, and quinolones were 30.4, 421, 884, and 1,807 ng/L, respectively. Lincomycin, roxithromycin, nalidixic acid, ofloxacin, and norfloxacin were detected in all samples with a detection frequency of 100%. The main sources of antibiotics were wastewater treatment plants (WWTPs) and rural dumps that did not contain sanitary treatment, which accounted for 33% and 40% of the total antibiotics present in the Karst river. Due to an increase in river flow quantity, the presence of WWTPs and rural dumps did not affect the concentration and distribution of antibiotics in the Karst river; however, the mass flux of antibiotics were significantly affected by the contamination source and the poor natural attenuation.

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