Deep learning-based total suspended solids concentration classification of stream water surface images captured by mobile phone

Hacıefendioğlu K., Baki O. T., Başağa H. B., Mete B.

ENVIRONMENTAL MONITORING AND ASSESSMENT, cilt.195, sa.12, 2023 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 195 Sayı: 12
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1007/s10661-023-12110-y
  • Dergi Adı: ENVIRONMENTAL MONITORING AND ASSESSMENT
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Agricultural & Environmental Science Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Compendex, EMBASE, Environment Index, Food Science & Technology Abstracts, Geobase, Greenfile, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, Civil Engineering Abstracts
  • Anahtar Kelimeler: Convolutional neural networks, Custom CNN, Deep learning, Total suspended solid, Trabzon
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

The continuous monitoring of total suspended solids (TSS) in streams plays an important role in the management of hydrological processes, and TSS is also a decisive parameter in the control of pollution in streams. Determination of TSS involves both a costly and time-consuming process. For this reason, there is a need to determine TSS more quickly, easily reliably, and economically. In this study, it is aimed to classify TSS concentration by using stream water surface images recorded with a mobile phone. For this purpose, the sampling studies carried out in the Lower Degirmendere Stream Watershed (Trabzon, northeast of Turkey) between June 2020 and May 2021, and the images of the stream water surface were taken and recorded with a mobile phone simultaneously with the sampling studies. The stream water surface images and TSS concentration were divided into four classification group, namely clear, turbid, very turbid, and extremely turbid and processed with deep learning method. More than 5500 stream water surface images and TSS concentration data were divided into groups as 70% training and the rest of test. This study uses eight convolutional neural network (CNN) models, including a custom CNN model designed for image classification, along with pre-existing architectures such as VGG-16, VGG-19, Inception-V3, Xception, DenseNet-121, MobileNet, and NASNet-mobile. The results revealed that the most effective ensemble model formation strategy was stacking with an accuracy rate of 87%, followed by weighted averaging, averaging, and majority voting. As a result, with the image of the stream water surface via the mobile phone camera, it can be easily determined which class the current TSS concentration belongs to instantly.