Deep Learning in Water Resources Management: Τhe Case Study of Kastoria Lake in Greece

Lina Karamoutsou,Aris Psilovikos

doi:10.3390/w13233364

Abstract

The effects of climate change on water resources management have drawn worldwide attention. Water quality predictions that are both reliable and precise are critical for an effective water resources management. Although nonlinear biological and chemical processes occurring in a lake make prediction complex, advanced techniques are needed to develop reliable models and effective management systems. Artificial intelligence (AI) is one of the most recent methods for modeling complex structures. The applications of machine learning (ML), as a part of AI, in hydrology and water resources management have been increasing in recent years. In this paper, the ability of deep neural networks (DNNs) to predict the quality parameter of dissolved oxygen (DO), in Lake Kastoria, Greece, is tested. The available dataset from 11 November 2015, to 15 March 2018, on an hourly basis, from four telemetric stations located in the study area consists of (1) Chl-a (μg/L), (2) pH, (3) temperature—Tw (°C), (4) conductivity (μS/cm), (5) turbidity (NTU), (6) ammonia (NH4, mg/L), (7) nitrate nitrogen (N–NO3, mg/L), and (8) dissolved oxygen (DO) (mg/L). Feed-forward deep neural networks (FF-DNNs) of DO, with different structures, are tested for all stations. All the well-trained DNNs give satisfactory results. The optimal selected FF-DNNs of DO for each station with a high efficiency (NSE > 0.89 for optimal selected structures/station) constitute a good choice for modeling dissolved oxygen. Moreover, they provide information in real time and comprise a powerful decision support system (DSS) for preventing accidental and emergency conditions that may arise from both natural and anthropogenic hazards.

Highlights

The physical, chemical, and biological responses of lakes to the climate give a variety of priceless information [1]
When high concentrations of dissolved oxygen (DO) are observed, they mainly occur: (a) at shallow eutrophic lake systems; (b) at late spring–early summer; (c) in the morning and at noon, when high concentrations of DO are observed due to the photosynthetic productivity of algae and/or cyanobacteria, which are associated with correspondingly high concentrations of Chl-a; and (d) when they are associated with low values of water temperature, which favors high values of DO of saturation, except in cases that the lake has an ice cap, which favors DO consumption and the inability to replenish
On the investigated structures, the results demonstrate that the proposed deep neural networks (DNNs) models

Summary

Introduction

The physical, chemical, and biological responses of lakes to the climate give a variety of priceless information [1]. Lakes are affected directly by changes in climate: (a) due to changes in mixing regime, including lake stratification, oxygen saturation by increase in temperature, and the frequency of extreme wind events; (b) by changes in trophic structure determined by temperature; and (c) by complex interactions between temperature, nutrients, and physical forces [2]. Due to the intense activity of the photosynthesis from phytoplankton, from all primary producers of the system, high DO values are observed, which are associated with correspondingly high concentrations of Chl-a. This phenomenon is reverses in the autumn, when respiration favors photosynthesis and DO depletion occurs, causing stress to the biota [4]

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