Surface water quality classification using data mining approaches: Irrigation along the Aladag River*

Abstract

Knowledge of water quality is an important requirement in planning, developing, and protecting water resources. Therefore, it is essential to determine the quality of water for various uses, including irrigation in different areas. The aim of this study is to evaluate the performance of different data mining methods using water quality parameters measured in Aladag River. At the first stage, the water quality in the Aladag River in Turkey was classified by United States Salinity Laboratory (USSL) diagrams and the Schofield 1933, Schofield 1935, and Wilcox-Magistad methods. Support vector classifier (four different kernels), k-nearest neighbour, and decision tree methods-including Hoeffding tree, random forest, random tree, and REP tree methods were used to classify the water quality. Hydro-chemical and hydrological parameters were used in the analysis, based on alternative scenarios. The methods were compared for accuracy based on kappa statistics and error rates. Evaluation of the data mining methods reflected the high accuracy and performance of these methods in classifying the water quality. In general, from the kernels used for the support vector classifier method, the Pearson universal kernel and from the decision trees, the REP tree provided the best results for Aladag River water quality classification. For the k-nearest neighbour method, the best results were obtained using the four nearest neighbours and Euclidean distances.classifier, decision tree, kernel functions, k-nearest neighbour, water quality classification RésuméLa connaissance de la qualité de l'eau est une exigence importante dans la planification, le développement et la protection des ressources en eau. Par conséquent, il est essentiel de déterminer la qualité de l'eau pour divers usages, y compris l'irrigation dans différentes zones. Le but de cette étude est d'évaluer la performance de différentes méthodes d'extraction de données en utilisant des paramètres de qualité de l'eau mesurés dans la rivière Aladag. Dans un premier temps, la qualité de l'eau de la rivière Aladag en Turquie a été classée * Classification de la qualité des eaux de surface à l'aide d'approches d'exploration de données: Irrigation le long de la rivière Aladag

Crop water use estimation of drip irrigated walnut using ANNs and ANFIS models

“…Many studies explain that artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and other machine learning methods can outperform traditional computational methods to predict evaporation and evapotranspiration to explain the effect on agricultural water use (Adeloye et al, 2012;Kisi et al, 2015;Abrishami et al, 2018;Sanikhani et al, 2019;Yamac and Todorovic, 2020;Elbeltagi et al, 2020;Petković et al, 2020;Yamaç, 2021;Sattari et al, 2021). AlMukhtar (2021) and Goyal et al (2014) did this for this purpose and predicted E with high accuracy with these models and performance.…”

“…It is seen that the most effective parameter in the probabilistic scenarios created for the model is the average temperature. Through scientific studies, it has been demonstrated that the maximum, minimum and average temperatures are more successful than other climate parameters in estimating evapotranspiration using artificial intelligence models (Abyaneh et al, 2011;Tabari et al, 2013;Aghajanloo et al, 2013;Yamaç and Todorovic, 2020;Sattari et al, 2021).…”

“…The ReliefF feature selection algorithm, which is a filter feature selection method modified by Kira and Rendell (1992), was used to filter the data and sort them according to their weight values between -1 (worst) and +1 (best). The processing procedure is repeated for each attribute, and in the end, the weight value of each attribute is calculated (Köksal, 2020;Sattari et al, 2021).…”

Groundwater Quality Evaluation of Fractured Aquifers Using Machine Learning Models and Hydrogeochemical Approaches to Sustainable Water-Irrigation Security in Arid Climate (Central Tunisia)

“…However, this method falls short of providing decision-makers with instant insights and a comprehensive comprehension of groundwater quality, especially when multiple factors contributing to the deterioration of water quality are identified. To address this limitation, various computer-assisted plotting techniques like Piper, Durov, Schofield, Wilcox, and United States Salinity Laboratory (USSL) approaches have been involved in assessing the appropriateness of groundwater for agricultural watering [10][11][12][13][14][15][16][17]. Furthermore, Integrated Irrigation Groundwater Quality Indices (IIGWQIs) are derived from water's chemical composition and offer an effective means of evaluating water suitability.…”

“…Furthermore, Integrated Irrigation Groundwater Quality Indices (IIGWQIs) are derived from water's chemical composition and offer an effective means of evaluating water suitability. By amalgamating several pivotal water quality indicators into a single value, these indices are designed to facilitate water quality management decisions for stakeholders [10,11,15,[18][19][20][21]. In the context of agriculture, these IIGWQIs are typically evaluated using a range of indices and variables [15,18,19].…”